sensors

Article Estimating Clothing Thermal Insulation Using an Infrared Camera

Jeong-Hoon Lee 1, Young-Keun Kim 2, Kyung-Soo Kim 1,* and Soohyun Kim 1

1 Department of Mechanical Engineering, KAIST, Daejeon 305-701, Korea; [email protected] (J.-H.L.); [email protected] (S.K.) 2 Department of Mechanical and Control Engineering, Handong Global University, Pohang 791-708, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-42-350-3047

Academic Editor: Fabrizio Lamberti Received: 4 November 2015; Accepted: 2 March 2016; Published: 9 March 2016

Abstract: In this paper, a novel algorithm for estimating clothing insulation is proposed to assess thermal comfort, based on the non-contact and real-time measurements of the face and clothing temperatures by an infrared camera. The proposed method can accurately measure the clothing insulation of various garments under different clothing fit and sitting postures. The proposed estimation method is investigated to be effective to measure its clothing insulation significantly in different seasonal clothing conditions using a paired t-test in 99% confidence interval. Temperatures simulated with the proposed estimated insulation value show closer to the values of actual temperature than those with individual clothing insulation values. Upper clothing’s temperature is more accurate within 3% error and lower clothing’s temperature is more accurate by 3.7%~6.2% error in indoor working scenarios. The proposed algorithm can reflect the effect of air layer which makes insulation different in the calculation to estimate clothing insulation using the temperature of the face and clothing. In future, the proposed method is expected to be applied to evaluate the customized passenger comfort effectively.

Keywords: clothing insulation; thermal comfort; PMV; infrared camera

1. Introduction Demands for smart and energy-efficient thermal systems that can control the air conditions according to the thermal comfort state of their users are increasing for home applications and vehicle Heating Ventilation and Air Conditioning (HVAC) systems. In electric vehicles, electric power to heat and blow warm air into the cabin in winter reduces mileage by nearly half. Therefore, it is important to satisfy thermal comfort as marginally as possible and control thermal comfort independently to save power according to the passenger number and position. In the same environment, different users (passengers) may experience different thermal comfort depending on their body temperature, clothing insulation, relative humidity, and other factors. Thus, a smart thermal comfort system is required to control climatic parameters operations such as air temperature, air flow direction, and air speed by sensing the complex thermal state of each passenger and the environment. The standard measure of thermal comfort is the predicted mean vote (PMV) that is defined in ISO 7730 [1]. The PMV is calculated from a complex relationship of six input variables: air temperature, mean radiant temperature, air velocity, air humidity, clothing insulation, and metabolic rate of the passenger. If all six variables of the PMV could be measured in real time, then control of PMV for individual passengers could be realized. However, the variables of metabolic rate and clothing insulation cannot be detected by simple sensors: they are too complex and related to physical processes.

Sensors 2016, 16, 341; doi:10.3390/s16030341 www.mdpi.com/journal/sensors Sensors 2016, 16, 341 2 of 16

Also, the compact space in a vehicle cabin and cost-effectiveness are major obstacles to realizing such a smart vehicular thermal system. Some researchers have proposed various numerical models of clothing thermal insulation (hereafter referred to as clothing insulation). The commonly applied clothing thermal models are the simplified one-parameter model, two-parameter models, and three-parameter models [2]. The one-parameter model simply estimates the thermal behavior of clothing based on its dry thermal insulation [2]. Two-parameter models consider the additional factor of vapor permeability, and three-parameter model includes the air layer between the skin and clothing [2]. Other researchers have analyzed human temperature distribution, thermal sensation, comfort, and clothing insulation using more complex numerical methods than the above-mentioned simple models, that solve thermal interaction between the environment and a virtual thermal mannequin based on a human thermal physiological model [3]. However, although these methods offer accuracy, they require high computational power in a costly and time-consuming process. The standard methods to measure clothing insulation use thermal mannequins, but a study by Konarsk et al. [4] reported that analyzing clothing insulation measured on human subjects yielded different results. Their paper analyzed the results of comparative clothing thermal insulation measurements determined in tests on volunteers and on a thermal mannequin under specified conditions, i.e., a standing man and a standing mannequin in a climate chamber under wind-free conditions with three ensembles of disposable medical clothing. The clothing insulation on human subjects was found to be up to 13% higher than that measured on the thermal mannequin. Also, the measurement error on the mannequin was about 2%, whereas the measurement error on human subjects was as large as 18%. It can be inferred that characterizing actual clothing insulation on humans requires a more complex model, in which additional factors such as clothing fit and posture should be considered. The effects of clothing fit and posture on human thermal condition were studied by Kakitsuba [5]. The study showed that the clothing fit (tight or loose fitting) and the posture of the human subject (standing, sitting on a floor, or sitting on a chair) are significant factors in the clothing area factor, that is the ratio of the clothing surface area to the body surface area. The clothing fit and the posture change the volume of the clothing micro-environment, which affects the clothing insulation. Because the conventional measurement method with human subjects or with a thermal mannequin requires the same laboratory conditions, including various types of apparatus and specific facilities, which are cost-intensive, other researchers proposed simpler estimating clothing insulation based on a regression analysis that relates some variables to the clothing insulation obtained from experimental data. A study by McCullough et al. proposed a regression analysis to predict the clothing insulation in terms of the body surface area covered by a single clothing layer, the body surface area not covered by clothing and the total clothing weight [6]. A study by Kwon et al. proposed that the clothing insulation can be predicted based on a multivariate linear regression model that incorporates the factors of clothing microclimate temperature, total weight of clothing, and upper clothing layers from a test [7]. However, clothing fit and posture effects were not considered and some parameters, that is, the weight of clothing and the number of clothing layers were determined manually prior to conducting the experiments. Despite the proposed methods of predicting clothing insulation, some parameters of them are still based on variables that are not directly measurable by a sensor in a real-time application and as long as models are applied to predict clothing insulation (including physical and virtual models), there won’t be a perfect prediction of the individual clothing insulation. On the other hand, infrared thermography has shown great applications in clothing comfort research due to its direct and non-invasive measurement advantages [8]. Gasi and Bittencourt used infrared thermography to evaluate textile materials and it was found that if the temperature variation from thermal image is less, it gives higher thermal efficiency [9]. Matusiak mentioned in his article that irrespective of the thermal resistance of clothing material, a key role in heat exchange is played Sensors 2016, 16, 341 3 of 16 by the size of air layers closed between the human body and clothing surface, as well as between the particular layers of clothing, which shows a thermogram from an infrared camera [10]. Thus, this paper proposes a novel algorithm for estimating clothing insulation with parameters that can be directly and in real time measured by an IR camera. First, this paper derives the correlation formula between the clothing insulation and the skin and clothing temperatures. It also investigates the effects of variables such as clothing type and fit, that is, the air layer volume underneath the clothing. Then, the proposed algorithm is validated by conducting experiments on human subjects.

2. Algorithm of Estimating Clothing Insulation The our idea is to automatically estimate the manually defined parameter up to now, clothing insulation, using sensor technology (infrared camera) to assess thermal comfort. In this section, a novel algorithm for estimating clothing insulation will be based on the non-contact and real-time measurements of the face and clothing temperatures by an infrared camera to measure accurately the clothing insulation of various garments under different clothing fit and sitting postures. The reason that we can do that is that the face and clothing temperatures will be a powerful footprint which is thermally interactive between environment and human body like as window, its interactive level means clothing insulation. We will derive from static definition to dynamic field with correction factor reflecting air velocity effects.

2.1. Under Static Thermal State It is assumed that the clothing on the human body is at a steady thermal state and dry heat transfer condition. Total static insulation is the thermal insulation from the body surface to the environment including all clothing, enclosed air layers, and the boundary air layer. Static clothing insulation is the thermal insulation from the skin surface to the outer clothing surface including enclosed air layers under static conditions, that can be expressed as Equation (1) [11]:

Ia_st Itot_st “ Icl_st ` (1) fcl where, Icl_st is the static clothing insulation, Ia_st is the static air thermal resistance and fcl is the clothing area factor, that is the ratio of the clothed and naked-skin surface areas. It can be calculated in terms of the static clothing insulation (Icl_st)[11], as:

fcl “ 1 ` 1.97Icl_st (2)

Unit of thermal insulation value of clothing leads to Equation (3):

2 Icl_strm K{Ws “ 0.155 ¨ Iclrclos (3)

Then, the total static insulation of Equation (1) can be re-expressed in terms of Icl from Equation (3):

Ia_st Itot_st “ 0.155Icl ` (4) 1 ` 0.305Icl

2.2. Under Dynamic Thermal State In actual applications, such as inside a vehicle cabin with air ventilation, the thermal state is dynamic. When there is movement of the air and the human subject, the total thermal insulation may be decreased. Thus, in the dynamic thermal state, the dynamic air insulation (Ia_dyn) and the dynamic Sensors 2016, 16, 341 4 of 16

total clothing insulation (Itot_dyn) must be calculated, that can be obtained by applying correction factors of Corr,ia and Corr,tot, respectively, to the static conditions [11,12], as expressed by:

I “ C I a_dyn orr,ia a_st (5) Itot_dyn “ Corr,tot Itot_st

Corr,cl for Icl ě 0.6 Corr,tot “ p0.6 ´ I qC ` I C (6) $ cl orr,ia cl orr,cl for I ă 0.6 & 0.6 cl 2 2 t´0.559pvar´0.15q`0.057pvar´0.15q `0.271vw´0.027vw u Corr,ia “ e % ď 1.0 2 2 (7) t´0.263pvar´0.15q`0..0272pvar´0.15q ´0.193vw`0.101vw u Corr,cl “ e ď 1.0 The dynamic clothing insulation are considered as Equation (6) according to clothing insulation range using the definition in [11,12] and recent empirical correction factors from 486 thermal mannequin tests in totally nine different testing conditions regarding air velocity (var) and human walking velocity (vw) as Equation (7) [13]:

Ia_dyn tsk ´ tcl Icl_dyn “ Itot_dyn ´ “ (8) fcl C ` R

The convection (C) and radiation (R) heat exchange between the skin and the clothing for a seated subject are calculated from:

´8 4 4 C ` R “ fcl hcptcl ´ taq ` 3.85 ˆ 10 tcl ´ tr (9) ! ´ ¯) where the 3.85 value comes from the assumption that the emissivity value of a seated passenger is 0.97 and the fraction of skin surface involved in heat exchange by radiation is equal to 0.70 for a seated subject. Combining Equations (8) and (9), the dynamic clothing insulation (Icl_dyn) can be expressed as Equation (10) using α, that is defined as the ratio of the temperature difference between the skin and the clothing to convection (C) and radiation (R) heat exchange between the skin and the environment:

t ´ t 1 t ´ t α I “ sk cl “ sk cl “ cl_dyn 4 C ` R fcl $ h pt ´ t q ` 3.85 ˆ 10´8 t 4 ´ t , fcl & c cl a cl r . (10) t ´ t where, α “ sk cl ´ ¯ % ´8 4 4 - hcptcl ´ taq ` 3.85 ˆ 10 tcl ´ tr ´ ¯ 2.3. Generalized Clothing Insulation Formula under Static And Dynamic States

This paper proposes a method to derive Icl to enter into the thermal comfort index (PMV) formula using measurable or calculated parameters at dynamic conditions. Equation (1) can be rearranged in terms of Icl_st by substituting Equation (5):

Ia_st Itot_dyn Ia_st Icl_st “ Itot_st ´ “ ´ (11) fcl Corr,cl fcl

Also, Equation (7) can be rearranged in terms of the total dynamic heat resistance (Itot_dyn):

tsk ´ tcl Ia_dyn Itot_dyn “ ` (12) C ` R fcl Sensors 2016, 16, 341 5 of 16

Substituting Equation (12) into Equation (11), Icl_st becomes:

1 t ´ t Ia_dyn I I “ sk cl ` ´ a_st (13) cl_st C C ` R f f orr,tot ˆ cl ˙ cl

Using the variable α from Equation (10), Icl_st can be expressed as:

1 α Ia_dyn Corr,tot Ia_st 1 Icl_st “ ` ´ “ α ` Ia_dyn ´ Corr,tot Ia_st (14) Corr,tot fcl fcl fcl Corr,tot fcl ˆ ˙ ´ ¯ Next, the dynamic air insulation is substituted by the simpler static air insulation i.e., Ia_dyn “ Corr,ia Ia_ st to express Icl_st as:

1 Icl_st “ α ` Ia_st Corr,ia ´ Corr,tot (15) Corr,tot fcl ` ˘( The final step is expressing Equation (15) in terms of the clothing insulation by using Equations (2) and (3) to obtain:

1 Iclp1 ` 0.305Iclq “ α ` Ia_st Corr,ia ´ Corr,tot (16) 0.155Corr,tot ` ˘( Therefore, the newly proposed algorithm can estimate clothing insulation using a non-linear function of the exposed skin temperature (tsk), the clothing temperature (tcl) and the surrounding thermal condition, air temperature (ta), mean radiant temperature (tr), relative air velocity (var) with the form of Iclptsk, tcl, ta, tr, varq “ 0 expressed as

2 1 0.305Icl ` Icl ´ α ` Ia_st Corr,la ´ Corr,tot “ 0 0.155Corr,tot t ´ t where, α “ sk cl ` ˘( ´8 4 4 hcptcl ´ taq ` 3.85 ˆ 10 tcl ´ tr 0.25 0.25 ? 2.38 | tcl ´ ta| f or 2.38 | tcl ´´ ta| ě 12.1¯ var hc “ ? 0.25 ? # 12.1 var f or 2.38 | tcl ´ ta| ă 12.1 var (17) Corr,cl for Icl ě 0.6 Corr,tot “ I pC ´ C q $ cl orr,cl orr,ia ` C for I ă 0.6 & 0.6 orr,ia cl 2 2 t´0.559pvar´0.15q`0.057pvar´0.15q `0.271vw´0.027vw u Corr,ia “ e ď 1.0 % 2 2 t´0.263pvar´0.15q`0..0272pvar´0.15q ´0.193vw`0.101vw u Corr,cl “ e ď 1.0 where Corr,ia and Corr,cl are the correction factors for air velocity (var) and human walking velocity (vw) and α is the temperature gradient between the skin and the clothing surface divided by the dry heat loss (convection and radiation) per unit of nude body surface area. Figure1 shows a novel input/output block diagram for estimating the clothing insulation with parameters that can be directly measured by an infrared (IR) camera. However, this proposed calculation equations have to be applied carefully considering some limitation of steady-state condition, not transient or dynamic changing condition because this proposed algorithm are derived from steady-state form of the energy conservation equation. If IR camera doesn’t have the function to assign each emissivity to the face and clothing region to get accurate temperature, the emissivity of the clothing has to be almost similar to that of face skin. Sensors 2016, 16, 341 6 of 16 input/output block diagram for estimating the clothing insulation with parameters that can be directly measured by an infrared (IR) camera. However, this proposed calculation equations have to be applied carefully considering some limitation of steady‐state condition, not transient or dynamic changing condition because this proposed algorithm are derived from steady‐state form of the energy conservation equation. If IR camera doesn’t have the function to assign each emissivity to the face and clothing region to get accurate temperature, the emissivity of the clothing has to be almost similar to that of faceSensors skin.2016 , 16, 341 6 of 16

FigureFigure 1. Proposed 1. Proposed clothing clothing insulation insulation calculation calculation block block diagram. diagram.

3. Experiments, Simulation and Analysis 3. Experiments, Simulation and Analysis 3.1. Experiments: Comparison of the Clothing Insulation between Estimated on the Proposed Algorithm and 3.1. Experiments:Calculated Comparison According to ISOof the 9920 Clothing and Its Measurement Insulation between Estimated on the Proposed Algorithm and Calculated AccordingThree clothing to ISO ensembles 9920 and were Its Measurement selected in order to verify the proposed clothing insulation algorithm. They represent typical seasonal working clothing at office, that is, summer, spring/fall and Threewinter clothing as follows ensembles and whose were insulations selected are listed in onorder Table to1. verify the proposed clothing insulation algorithm. TheyTable represent 1. Tested garmentstypical andseasonal their thermal working insulation clothing from ISO at 9920office, and that measurement is, summer, (* value spring/fall and winter as followsmeasured and bywhose methods insulations ASTM 1518 or are equivalent). listed on Table 1.

Thermal Insulation Table 1. Tested garments andEnsembles their thermal insulation from ISO 9920 and measurement (* value clo m2¨ KW´1 measured by methods ASTM 1518 or equivalent). Underwear pants briefs 0.04 0.006 Trousers straight fitted 0.22Thermal Insulation 0.034 SocksEnsembles 0.02 0.003 2 −1 Shoes 0.03clo m ∙ 0.003KW UnderwearUnderwear shirts sleeveless pants briefs 0.060.04 0.006 0.009 Shirts long sleeves shirt collar 0.31 0.048 Spring/fallTrousers working straight jacket fitted 0.29 *0.22 0.034 0.045 * Winter workingSocks jacket 0.47 *0.02 0.003 0.073 * Shoes 0.03 0.003 In particular experimentsUnderwear were conductedshirts sleeveless with working 0.06 jacket uniforms0.009 which had same materials and design to improveShirts accuracy long as sleeves shown shirt in Figure collar2: 0.31 0.048 Spring/fall working jacket 0.29 * 0.045 * ‚ Winter clothing ensembles: underwear pants briefs, trousers straight fitted, socks, shoes, underwear shirts sleeveless, shirtsWinter long sleeves working shirt jacket collar, winter working0.47 * jacket0.073 (company’s * uniform). ‚ Spring/fall clothing ensembles: underwear pants briefs, trousers straight fitted, socks, shoes, underwear shirts sleeveless, shirts long sleeves shirt collar, spring/fall working jacket (company’s uniform). ‚ Summer clothing ensembles: underwear pants briefs, trousers straight fitted, socks, shoes, underwear shirts sleeveless, shirts long sleeves shirt collar

Figure 2. Spring/fall and winter working jacket uniform. Sensors 2016, 16, 341 6 of 16 input/output block diagram for estimating the clothing insulation with parameters that can be directly measured by an infrared (IR) camera. However, this proposed calculation equations have to be applied carefully considering some limitation of steady‐state condition, not transient or dynamic changing condition because this proposed algorithm are derived from steady‐state form of the energy conservation equation. If IR camera doesn’t have the function to assign each emissivity to the face and clothing region to get accurate temperature, the emissivity of the clothing has to be almost similar to that of face skin.

Figure 1. Proposed clothing insulation calculation block diagram.

3. Experiments, Simulation and Analysis

3.1. Experiments: Comparison of the Clothing Insulation between Estimated on the Proposed Algorithm and Calculated According to ISO 9920 and Its Measurement Three clothing ensembles were selected in order to verify the proposed clothing insulation algorithm. They represent typical seasonal working clothing at office, that is, summer, spring/fall and winter as follows and whose insulations are listed on Table 1.

Table 1. Tested garments and their thermal insulation from ISO 9920 and measurement (* value measured by methods ASTM 1518 or equivalent).

Thermal Insulation Ensembles clo m2∙KW−1 Underwear pants briefs 0.04 0.006 Trousers straight fitted 0.22 0.034 Socks 0.02 0.003 Shoes 0.03 0.003 Underwear shirts sleeveless 0.06 0.009 Shirts long sleeves shirt collar 0.31 0.048 Spring/fall working jacket 0.29 * 0.045 * Sensors 2016, 16, 341 7 of 16 Winter working jacket 0.47 * 0.073 *

Figure 2. Spring/fall and winter working jacket uniform. Figure 2. Spring/fall and winter working jacket uniform.

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OR, USA) with an infrared shirtsshirtsshirts sleeveless, sleeveless,sleeveless, shirts shirtsshirts longlong longlong sleeves sleevessleeves shirt shirtshirt collar, collar,collar, winter winter working working jacketjacket jacketjacket (company’s (company’s(company’s uniform). uniform). ˝ resolution•• Spring/fallSpring/fall of 320 clothing ˆclothing240 ensembles: pixels,ensembles: spatial underwear underwear resolution pants pants briefs, briefs, 1.3 trousers trouserstrousers mrad, straight straight thermal fitted, fitted,fitted, socks, socks, sensitivity shoes, shoes, below 0.05 C, and •• Spring/fallSpring/fall clothingclothingclothing ensembles:ensembles: underwearunderwear pantspants briefs,briefs, trouserstrouserstrouserstrousers straightstraightstraight fitted,fitted,fitted, socks,socks,socks, shoes,shoes,shoes, underwearunderwear˝ shirtsshirts sleeveless,sleeveless, shirtsshirts longlonglong sleevessleeves shirtshirt collar,collar, spring/fallspring/fall workingworking jacketjacketjacket accuracyunderwear ofunderwear˘2 C. shirtsshirtsshirts sleeveless,sleeveless,sleeveless, shirtsshirtsshirts longlonglonglong sleevessleevessleeves shirtshirtshirt collar,collar,collar, spring/fallspring/fallspring/fall workingworking jacketjacketjacketjacket (company’s(company’s(company’s uniform). uniform). (company’s(company’s(company’s uniform). uniform). •• SummerSummer clothingclothing ensembles:ensembles: underwearunderwear pantspants briefs,briefs, trouserstrouserstrousers straightstraight fitted,fitted,fitted, socks,socks, shoes,shoes, •• SummerSummer clothingclothingclothing ensembles:ensembles: underwearunderwear pantspants briefs,briefs, trouserstrouserstrouserstrousers straightstraightstraight fitted,fitted,fitted, socks,socks,socks, shoes,shoes,shoes, Tableunderwearunderwear 2. Mean shirts shirts values sleeveless, sleeveless, and shirts shirts standard long longlong sleeves sleeves deviations shirt shirt collar collar (SD) of physical characteristics of subjects and underwearunderwear shirts shirtsshirts sleeveless, sleeveless,sleeveless, shirts shirtsshirts longlong longlong sleeves sleevessleeves shirt shirtshirt collar collarcollar environmentalExperimentsExperiments were conditions were carried carried out concerningout on on sitting sitting posture posture clothing at at rest rest ensembles in in the the climatic climatic tested room room on during during sitting each each at 30 30 rest. min min ExperimentsExperimentsExperiments were werewere carried carriedcarried out outout on onon sitting sittingsitting posture postureposture at atat rest restrest in inin the thethe climatic climaticclimatic room roomroom during duringduring each eacheach 30 3030 min minmin exposureexposureExperimentsExperiments under under physical physical were were carried characteristicscarried characteristics out out on on sittingof sittingof subjects subjects posture posture and and atenvironmental atenvironmental rest rest in in the the climatic climatic conditions conditions room room measured measured during during each byeach by SWEMA, SWEMA,30 30 min min exposureexposureexposure under under under physical physical physical characteristics characteristics characteristics of of of subjects subjects subjects and and and environmental environmental environmental conditions conditions conditions measured measured measured by by by SWEMA, SWEMA, SWEMA, exposureISOexposureISO 7726, 7726, under 7730under 7730 measurement physical measurementphysical characteristics characteristics equipment equipment of of assubjects assubjects shown shown and andin in Tableenvironmental Tableenvironmental 2, 2, and and thermal thermal conditions conditions images images measured measured were were recorded recorded by by SWEMA, SWEMA, by by a a ISOISO 7726, 7726, 7730 7730 measurement measurement equipment equipment as as shown shown in in Table Table 2, 2, and and thermal thermaltr ˘ imagesSD images were wereta ˘recorded recordedSD by by a a ClothingISOISOISO 7726, 7726, 7730 7730 measurement measurementAge ˘ SDequipment equipmentHeight as as shown shown˘shownSD inin inin Table TableMass 2, ˘ 2, andSD and thermalthermal thermalthermal imagesimages imagesimages were were recorded recordedrecorded by byv ar a a ˘ SD (Air Humidity ˘ SD ThermaCAMThermaCAMSex S45 S45 thermographic thermographicthermographic camera camera (FLIR, (FLIR,(FLIR, Wilsonville, Wilsonville, OR, OR, USA) USA)(Radiant with with an an infrared infraredinfrared(Ambient resolution resolution of of EnsembleThermaCAMThermaCAM S45 S45 thermographicthermographic thermographicthermographic(year) camera cameracamera (cm)(FLIR, (FLIR,(FLIR, Wilsonville, Wilsonville,(kg) OR, OR, USA) USA) with with˝ an an infraredinfrared infraredinfrared resolution resolutionresolution˝ Velocity) of of (m/s) (%RH) 320320 × ×240 240 pixels, pixels, spatial spatial resolution resolution 1.3 1.3 mrad, mrad, thermal thermalthermal sensitivity sensitivity below belowtemp) 0.05 0.05 ° C, (°C,C) and and accuracy accuracytemp) ( C)of of ±2 ±2 ° C.°C. 320320 × × 240 240 pixels, pixels, spatial spatialspatial resolution resolutionresolution 1.3 1.3 mrad, mrad, thermalthermal thermalthermal sensitivity sensitivitysensitivity below below 0.05 0.05 ° °C,°C, and and accuracy accuracy of of ±2 ±2 ° °C.°C. Winter male (n = 7) 34.3 ˘ 1.4 173.9 ˘ 6.1 73.1 ˘ 8.5 22.12 ˘ 0.28 21.76 ˘ 0.60 0.06 ˘ 0.06 20.04 ˘ 0.45 Spring/FallTableTablemale 2. (n2. Mean =Mean 7) values values 34.3 ˘and and1.4 standard standard 173.9 deviations deviations˘ 6.1 (SD) (SD) 73.1 of of ˘physical physical8.5 characteristics 22.85characteristics˘ 0.19 of 22.41of subjects subjects˘ 0.42 and and 0.06 ˘ 0.06 19.74 ˘ 0.38 TableTableTable 2. 2.2. Mean MeanMean values valuesvalues and andand standard standardstandard deviations deviationsdeviations (SD) (SD)(SD) of ofof physical physicalphysical characteristics characteristicscharacteristics of ofof subjects subjectssubjects and andand TableTable 2. 2. Mean Mean values values ˘and and standard standard deviations deviations˘ (SD) (SD) of of ˘physical physical characteristics characteristics˘ of of subjects subjects˘ and and ˘ ˘ Summerenvironmentalenvironmental male (n = 6) conditions conditions 34.7 1.0concerning concerning 173.7 clothing clothing6.7 ensembles ensembles 71.5 tested testedtested8.0 on on sitting sittingsitting 23.40 at at rest. 0.15rest.rest. 22.79 0.50 0.07 0.05 18.94 0.42 environmentalenvironmentalenvironmental conditions conditionsconditions concerning concerningconcerning clothing clothingclothing ensembles ensemblesensembles testedtested testedtested on on sitting sittingsitting at atat rest. rest.rest. AgeAge ± ± trt±r ±SD SD tat±a ±SD SD varv ar± ±SD SD (Air (Air AgeAge ± ± tr t±r SD± SD tat±a SD± SD varv ar± SD± SD (Air (Air ClothingClothing AgeAge ± ± HeightHeight ± ± MassMass ± ±SD SD ttr r±± SD SD ttaa±± SD SD vvarar ± ± SD SD (Air (Air HumidityHumidity ± ± ClothingClothing SexSex AgeAgeSDSD ± ± HeightHeight ± ± MassMass ± SD± SD (Radiantt(Radiantrt±r ±SD SD (Ambientt(Ambientata± ±SD SD varvVelocity) ar±Velocity) ±SD SD (Air (Air HumidityHumidity ± ± EnsembleClothingClothingEnsemble SexSex SDSD HeightSDHeightSD (cm) (cm) ± ± ± MassMass(kg)(kg) ± ± ±SD SD (Radiant(Radiant (Ambient(Ambient Velocity)Velocity) HumiditySDHumiditySD (%RH) (%RH) ± ± ± EnsembleEnsemble SexSex (year)SD(year)SD SDSD (cm) (cm) (kg)(kg) temp)(Radiant(Radianttemp)(Radiant(Radiant (°C) (°C) temp)(Ambient(Ambient(Ambienttemp)(Ambient (°C) (°C) Velocity)Velocity)(m/s)(m/s) SDSD (%RH) (%RH) EnsembleEnsembleTables 3–5 show the(year)(year) results SDSD (cm)(cm) (cm) of(cm) thermal (kg)(kg)(kg)(kg) images,temp)temp) (°C) (°C) the temp) measuredtemp) (°C) (°C) temperature,(m/s)(m/s) SDSD (%RH)(%RH) (%RH)(%RH) and estimated upper, (year)(year)(year)(year) temp)temp)temp)temp) (°C)(°C) (°C)(°C) temp)temp)temp)temp) (°C)(°C) (°C)(°C) (m/s)(m/s)(m/s)(m/s) WinterWinter malemale (n (n(n = =7) 7) 34.334.3 ± ±1.4 1.4 173.9173.9 ± ±6.1 6.1 73.173.1 ± ±8.5 8.5 22.1222.12 ± ±0.28 0.28 21.7621.76 ± ±0.60 0.60 0.060.06 ± ±0.06 0.06 20.0420.04 ± ±0.45 0.45 lowerWinterWinter and ensemblemalemale (n(n (n(n = = =7) 7)7) clothing34.334.334.3 ± ± ±1.4 1.41.4 173.9173.9173.9 insulation ± ± ±6.1 6.16.1 73.173.173.1 ± in ± ±8.5 8.58.5 each 22.1222.1222.12 ensemble. ± ± ±0.28 0.280.28 21.7621.7621.76 ± ± ±0.60 0.600.60 0.060.060.06 ± ± ±0.06 0.060.06 20.0420.0420.04 ± ± ±0.45 0.450.45 Spring/FallSpring/Fall malemale (n (n(n = =7) 7) 34.334.3 ± ±1.4 1.4 173.9173.9 ± ±6.1 6.1 73.173.1 ± ±8.5 8.5 22.8522.85 ± ±0.19 0.19 22.4122.41 ± ±0.42 0.42 0.060.06 ± ±0.06 0.06 19.7419.74 ± ±0.38 0.38 Spring/FallSpring/FallSpring/Fall malemale (n(n (n(n = = =7) 7)7) 34.334.334.3 ± ± ±1.4 1.41.4 173.9173.9173.9 ± ± ±6.1 6.16.1 73.173.173.1 ± ± ±8.5 8.58.5 22.8522.8522.85 ± ± ±0.19 0.190.19 22.4122.4122.41 ± ± ±0.42 0.420.42 0.060.060.06 ± ± ±0.06 0.060.06 19.7419.7419.74 ± ± ±0.38 0.380.38 SummerSummer malemale (n (n(n = =6) 6) 34.734.7 ± ±1.0 1.0 173.7173.7 ± ±6.7 6.7 71.571.5 ± ±8.0 8.0 23.4023.40 ± ±0.15 0.15 22.7922.79 ± ±0.50 0.50 0.070.07 ± ±0.05 0.05 18.9418.94 ± ±0.42 0.42 SummerSummerSummer malemale (n(n (n(n = = =6) 6)6) 34.734.734.7 ± ± ±1.0 1.01.0 173.7173.7173.7 ± ± ±6.7 6.76.7 71.571.571.5 ± ± ±8.0 8.08.0 23.4023.4023.40 ± ± ±0.15 0.150.15 22.7922.7922.79 ± ± ±0.50 0.500.50 0.070.070.07 ± ± ±0.05 0.050.05 18.9418.9418.94 ± ± ±0.42 0.420.42 Table 3. Results of measured mean temperature and estimated clothing insulation concerning winter TableTable 3. 3. Results Results of of measured measured mean mean temperature temperaturetemperature and and estimated estimated clothing clothing insulation insulationinsulation concerning concerning TableTable 3. 3.3. Results Results ofof measuredmeasured meanmean temperaturetemperaturetemperaturetemperature andandand estimatedestimatedestimated clothingclothingclothing insulationinsulationinsulationinsulation concerningconcerningconcerning clothingwinterwinter ensembles clothing clothing ensembles ensembles after after 30after minutes30 30 minutes minutes of of of exposure. exposure. winterwinter clothing clothingclothing ensembles ensemblesensembles after afterafter 30 3030 minutes minutes of of exposure. exposure.exposure. ThermalThermal Images Images MeasuredMeasured Mean Mean Temperature Temperature EstimatedEstimated Clothing Clothing Insulation Insulation ThermalThermalThermal Images Images Images MeasuredMeasuredMeasured Mean Mean Mean Temperature Temperature Temperature EstimatedEstimatedEstimated Clothing Clothing Clothing Insulation Insulation Insulation ThermalThermal Images Images MeasuredMeasured Measured Mean Mean Mean Temperature Temperature TemperatureIcl_up_estiIcl_up_estiEstimatedEstimated Icl_lo_estiClothing Icl_lo_estiClothing Estimated Insulation InsulationIcl_estiIcl_esti Clothing Insulation tcl_uptcl_up ± ± SD SD tcl_lotcl_lo ± ± SD SD Icl_up_estiIcl_up_esti Icl_lo_estiIcl_lo_esti Icl_estiIcl_esti tcl_uptcl_up ± ± SD SD tcl_lotcl_lo ± ± SD SD Icl_up_estiIcl_up_estiIcl_up_esti Icl_lo_estiIcl_lo_estiIcl_lo_esti Icl_estiIcl_estiIcl_esti UpperUpper LowerLower cl_upcl_up ± cl_locl_lo ± tskt sk± ± SD SD Icl_up_esti(upper(upper Icl_lo_esti(lower(lower (Ensemble(EnsembleIcl_esti Icl_esti UpperUpper LowerLower tcl_upttcl_up ± SDSDSD tcl_lottcl_lo ± SDSDSD tskt sk± ± SD SD (upper(upper (lower(lower (Ensemble(Ensemble UpperUpper LowerLowerLower t t ˘ SD± SD (upper t t ± ˘SDSD (lowersksk ±± t ˘ SD (face)(upper(upper Icl_up_esti(lower(lower(upper (Ensemble(EnsembleIcl_lo_esti (lower UpperUpper LowerLower cl_up(upper(upper cloth) cloth) (lower(lowercl_lo cloth) cloth) tskttsk ± ± SDSDskSD (upper(upper (lower(lower (Ensemble(Ensemble clothingclothing clothingclothing (upper(upper cloth) ˝cloth) (lower(lower cloth) cloth)˝ (face)t(face) (°C)SD (°C) ˝ clothing)clothing) clothing)clothing) clothing)clothing) (Ensemble clothingclothingclothingclothing clothingclothingclothingclothing (upper(upper(uppercloth) cloth) ( cloth)cloth)C) (lower(lower(lowercloth) cloth) cloth)cloth) ( C) (face)(face) (°C) (°C) ( C)clothing)clothing)clothing) clothing)clothing)clothing)clothing) (clo) clothing)clothing)clothing) (clo) clothingclothing clothingclothing (°C)(°C)(°C) (°C)(°C)(°C) (face)(face)(face)(face) (°C)(°C) (°C)(°C) clothing)clothing)(clo)(clo) clothing)clothing)(clo)(clo) clothing)clothing)(clo)(clo) clothing) (clo) (°C)(°C)(°C) (°C)(°C)(°C) (clo)(clo) (clo)(clo) (clo)(clo) (°C) (°C) (clo)(clo)(clo)(clo) (clo)(clo)(clo)(clo) (clo)(clo)(clo)(clo)

25.0325.0325.03 ˘± ±0.810.81 0.81 27.8327.83 27.83 ± ±0.06 0.06˘ 0.06 32.7732.77 ± ±0.4 32.770.4 ˘ 0.41.361.36 1.360.550.55 1.761.76 0.55 1.76 25.0325.0325.03 ± ± ± 0.81 0.810.81 27.8327.8327.83 ± ± ± 0.06 0.060.06 32.7732.7732.77 ± ± ± 0.4 0.40.4 1.361.361.36 0.550.550.55 1.761.761.76

24.6524.6524.65 ˘± ±0.080.08 0.08 26.9126.91 26.91 ± ±0.08 0.08˘ 0.08 32.5032.50 ± ±0.08 32.500.08 ˘ 0.081.531.53 1.530.710.71 2.032.03 0.71 2.03 24.6524.6524.65 ± ± ± 0.08 0.080.08 26.9126.9126.91 ± ± ± 0.08 0.080.08 32.5032.5032.50 ± ± ± 0.08 0.080.08 1.531.531.53 0.710.710.71 2.032.032.03

24.9324.93 ± ±0.05 0.05 26.9626.96 ± ±0.06 0.06 33.1833.18 ± ±0.05 0.05 1.461.46 0.770.77 2.022.02 24.9324.9324.9324.93 ˘± ± ± 0.05 0.05 0.050.05 26.9626.9626.96 26.96 ± ± ± 0.06 0.060.06˘ 0.06 33.1833.1833.18 ± ± ± 0.05 0.05 33.180.05 ˘ 0.051.461.461.46 1.460.770.770.77 2.022.022.02 0.77 2.02

24.4724.47 ± ±0.05 0.05 26.0926.09 ± ±0.06 0.06 33.1033.10 ± ±0.05 0.05 1.721.72 0.990.99 2.432.43 24.4724.4724.4724.47 ˘± ± ± 0.05 0.05 0.050.05 26.0926.0926.09 26.09 ± ± ± 0.06 0.060.06˘ 0.06 33.1033.1033.10 ± ± ± 0.05 0.05 33.100.05 ˘ 0.051.721.721.72 1.720.990.990.99 2.432.432.43 0.99 2.43

Sensors 2016, 16, 341 8 of 16

Table 3. Cont.

Thermal Images Measured Mean Temperature Estimated Clothing Insulation SensorsSensors 2016 2016, 16, 16, 341, 341 8 8of of 16 16 SensorsSensors 2016 2016, 16, 16, 341, 341 8 8of of 16 16 Icl_esti Upper Lower tcl_up ˘ SD (upper tcl_lo ˘ SD (lower tsk ˘ SD (face) Icl_up_esti (upper Icl_lo_esti (lower ˝ ˝ ˝ (Ensemble clothing clothing cloth) ( C) TableTablecloth) 3. 3. (Cont ContC) . . ( C) clothing) (clo) clothing) (clo) SensorsSensorsSensors 2016 20162016, 16,, 1616, 341,, 341341 TableTable 3. 3. Cont Cont. . 8 8of8 ofof 16 1616 clothing) (clo) SensorsSensors 2016 2016,, 16, 16,, 341, 341 8 8of of 16 16 SensorsSensors 2016 2016, 16,, 16, 341,, 341 8 8of of 16 16 TableTable 3. 3. Cont Cont. . TableTable 3. 3. Cont Cont. . 25.3325.3325.33 ± ˘ ±0.06 0.060.06 26.3326.33 26.33± ±0.07Table 0.07Table˘ 0.073. 32.573. Cont32.57 Cont ..± ..± 0.06 0.06 32.57 ˘ 0.061.201.20 0.87 1.200.87 1.891.89 0.87 1.89 25.3325.33 ± ±0.06 0.06 26.3326.33 ± ±0.07 0.07 32.5732.57 ± ±0.06 0.06 1.201.20 0.870.87 1.891.89

25.3325.33 ± ±0.06 0.06 26.3326.33 ± ±0.07 0.07 32.5732.57 ± ±0.06 0.06 1.201.20 0.870.87 1.891.89 25.3325.33 ± ±0.06 0.06 26.3326.33 ± ±0.07 0.07 32.5732.57 ± ±0.06 0.06 1.201.20 0.870.87 1.891.89 25.3325.33 ± ±0.06 0.06 26.3326.33 ± ±0.07 0.07 32.5732.57 ± ±0.06 0.06 1.201.20 0.870.87 1.891.89

25.8325.83 ± ±0.06 0.06 27.0327.03 ± ±0.10 0.10 33.5333.53 ± ±0.12 0.12 1.121.12 0.780.78 1.751.75 25.8325.83 ± ˘±0.06 0.06 27.0327.03 ± ±0.10 0.10˘ 33.5333.53 ± ±0.12 0.12 ˘ 1.121.12 0.780.78 1.751.75 25.8325.8325.83 ± ±0.06 0.060.06 27.0327.03 27.03± ±0.10 0.10 0.1033.5333.53 ± ±0.12 0.12 33.53 0.121.121.12 0.78 1.120.78 1.751.75 0.78 1.75

25.8325.83 ± ±0.06 0.06 27.0327.03 ± ±0.10 0.10 33.5333.53 ± ±0.12 0.12 1.121.12 0.780.78 1.751.75 25.8325.83 ± ±0.06 0.06 27.0327.03 ± ±0.10 0.10 33.5333.53 ± ±0.12 0.12 1.121.12 0.780.78 1.751.75 25.8325.83 ± ±0.06 0.06 27.0327.03 ± ±0.10 0.10 33.5333.53 ± ±0.12 0.12 1.121.12 0.780.78 1.751.75

25.68 25.68 ± ±0.10 0.10 27.1027.10 ± ±0.09 0.09 33.4233.42 ± ±0.04 0.04 1.171.17 0.760.76 1.771.77 25.68 25.6825.68 ± ˘ ±0.10 0.100.10 27.1027.10 27.10± ±0.09 0.09˘ 0.0933.4233.42 ± ±0.04 0.04 33.42 ˘ 0.041.171.17 0.76 1.170.76 1.771.77 0.76 1.77

25.6825.68 ± ±0.10 0.10 27.1027.10 ± ±0.09 0.09 33.4233.42 ± ±0.04 0.04 1.171.17 0.760.76 1.771.77 25.6825.68 ± ±0.10 0.10 27.1027.10 ± ±0.09 0.09 33.4233.42 ± ±0.04 0.04 1.171.17 0.760.76 1.771.77 25.6825.68 ± ±0.10 0.10 27.1027.10 ± ±0.09 0.09 33.4233.42 ± ±0.04 0.04 1.171.17 0.760.76 1.771.77 TotalTotal (Mean (Mean ± ±SD) SD) 25.1325.1325.68 25.68± ±0.51 0.51 ± ±0.10 0.10 26.8926.8927.1027.10 ± ±0.56 0.56± ±0.09 0.09 33.01 33.0133.4233.42 ± ±0.41 ±0.41 ±0.04 0.04 1.37 1.37 ± 1.17±0.22 1.170.22 0.780.78 ±0.76 ±0.140.76 0.14 1.951.95 ±1.77 ±0.241.77 0.24 TotalTotalTotal (Mean (Mean (Mean˘ ± SD) ±SD) SD) 25.13 25.1325.13 ± ˘ ±0.51 0.510.51 26.8926.89 26.89± ±0.56 0.56˘ 0.5633.0133.01 ± ±0.41 0.41 33.01 1.37˘1.370.41 ± ±0.22 0.22 0.78 1.370.78 ±˘ ±0.14 0.220.14 1.951.95 ± 0.78 ±0.24 0.24˘ 0.14 1.95 ˘ 0.24

TotalTableTotalTable (Mean (Mean 4. 4. Results Results ± ±SD) SD) of of measured measured 25.1325.13 ±mean ±0.51mean 0.51 temperature temperature26.8926.89 ± ±0.56 0.56 and and 33.01estimated 33.01estimated ± ±0.41 0.41 clothing clothing1.371.37 ± ±0.22insulation 0.22insulation 0.780.78 concerning ±concerning ±0.14 0.14 1.95 1.95 ± ±0.24 0.24 TotalTableTotalTable (Mean (Mean 4. 4. Results Results ± ±SD) SD) of of measured measured 25.1325.13 ±mean ±0.51mean 0.51 temperature temperature26.8926.89 ± ±0.56 0.56 and and 33.01estimated 33.01estimated ± ±0.41 0.41 clothing clothing1.371.37 ± ±0.22insulation 0.22insulation 0.780.78 concerning ±concerning ±0.14 0.14 1.95 1.95 ± ±0.24 0.24 Totalspring/fallTotalspring/fall (Mean(Mean (Mean clothing ±clothing ±SD) SD) ensembles ensembles25.1325.13 after ±after ±0.51 0.51 30 30 min min26.89 of 26.89of exposure. exposure. ± ±0.56 0.56 33.0133.01 ± ±0.41 0.41 1.371.37 ± ±0.22 0.22 0.780.78 ± ±0.14 0.14 1.951.95 ± ±0.24 0.24 TableTotalspring/fallTotalspring/fall 4. (Mean (MeanResults clothing ±clothing ±SD) SD) ofensembles ensembles measured25.1325.13 after ±after ±0.51 0.51 30 30 min mean min26.89 of26.89 of exposure. exposure. temperature± ±0.56 0.56 33.0133.01 ± ±0.41 0.41 and 1.371.37 estimated ± ±0.22 0.22 0.780.78 clothing ± ±0.14 0.14 1.95 insulation1.95 ± ±0.24 0.24 concerning TableTable 4. 4.4. Results Results of of measured measured mean mean temperature temperaturetemperature and andand estimated estimatedestimated clothing clothingclothing insulation insulationinsulation concerning concerningconcerning ThermalThermal TableImages TableImages 4. 4. Results Results of of measured MeasuredmeasuredMeasured mean Meanmean Mean temperature Temperaturetemperature Temperature and and estimated estimated clothing EstimatedclothingEstimated insulation insulation Clothing Clothing concerning concerningInsulation Insulation spring/fallThermalThermal Tablespring/fallImages spring/fallTableImages clothing 4. 4. Results clothingResults clothing ensemblesof ensemblesof ensemblesmeasured MeasuredmeasuredMeasured after aftermean after Meanmean Mean 30 30 mintemperature temperature min 30Temperaturetemperature Temperature of minof exposure. exposure. of and and exposure. estimated estimated clothingclothing EstimatedclothingEstimated insulationinsulation insulation Clothing Clothing concerningconcerning concerningInsulation Insulation spring/fallspring/fall clothing clothing ensembles ensembles after after 30 30 min min of of exposure. exposure. Icl_up_estiIcl_up_esti Icl_lo_estiIcl_lo_esti Icl_estiIcl_esti spring/fallspring/fall clothing clothingt cl_upensemblest cl_upensembles ± ±SD SD after aftertcl_lot cl_lo30 30 ± min ± minSD SD of of exposure. exposure. Icl_up_estiIcl_up_esti Icl_lo_estiIcl_lo_esti Icl_estiIcl_esti spring/fallspring/fall clothing clothingt cl_upensemblest cl_upensembles ± ±SD SD after aftertcl_lo t30cl_lo 30 ±min ± minSD SDof of exposure. exposure. Icl_up_estiIcl_up_esti Icl_lo_estiIcl_lo_esti Icl_estiIcl_esti UpperUpper Thermal ThermalLower ImagesLower Images tcl_uptcl_up ± ±SD SDMeasured Measured tcl_lotcl_lo Mean ± Mean± SD SD Temperature Temperature tskt sk± ± SD SD (upper(upperEstimatedEstimated (lowerClothing (lowerClothing Insulation Insulation(Ensemble(Ensemble UpperUpper Thermal ThermalLower ImagesLower Images (upper(upper cloth) cloth)MeasuredMeasured Mean Mean Temperature Temperaturetskt sk± ± SD SD (upper(upperEstimatedEstimated (lowerClothing (lowerClothing Insulation Insulation(Ensemble(Ensemble UpperclothingUpper ThermalThermal ThermalLower clothing ImagesLower Images (upper(upper cloth) cloth)MeasuredMeasured Measured(lower (lower Mean Mean Mean Temperature Temperature Temperaturetskt sk± ± SD SD (upperclothing)(upperEstimatedEstimated (lowerclothing)Clothing (lowerClothing Estimated Insulation Insulation(Ensemble(Ensembleclothing) Clothing Insulation clothingclothingThermal Thermal clothing clothingImages Images (upper(upper cloth) cloth)MeasuredMeasured (lower (lowerMean Mean Temperature Temperature(face)(face) (°C) (°C) clothing)clothing)Icl_up_estiIcl_up_estiEstimatedEstimated clothing) Clothingclothing)Icl_lo_esti ClothingIcl_lo_esti Insulation Insulation clothing)clothing)Icl_estiIcl_esti clothingclothing clothingclothing (°C)(°C)tcl_uptcl_up ± ±SD SD cloth)cloth)tcl_lotcl_lo (°C) (°C)± ± SD SD (face)(face) (°C) (°C) clothing)clothing)Icl_up_esti clothing)clothing)Icl_lo_esti clothing)clothing)Icl_esti (°C)(°C)tcl_uptcl_up ± ±SD SD tcl_lotcl_lo ± ± SD SD (face)(face) (°C) (°C) Icl_up_estiIcl_up_esti Icl_lo_estiIcl_lo_esti Icl_estiIcl_esti UpperUpper LowerLower (°C)(°C)tcl_uptcl_up ± ±SD SD cloth)cloth)tcl_lot cl_lo(°C) (°C)± ± SD SD tskt sk± ± SD SD (clo)Icl_up_esti(clo)Icl_up_esti(upper(upper Icl_up_estiIcl_lo_esti(clo)(lowerIcl_lo_esti(clo)(lower (EnsembleIcl_lo_esti(clo)(EnsembleIcl_esti(clo)Icl_esti Icl_esti UpperUpper LowerLower tcl_uptcl_up ± ±SD SD cloth)cloth)tcl_lo cl_lo(°C) (°C)±± SD tskt sk± ± SD SD Icl_up_esti(clo)Icl_up_esti(clo)(upper(upper Icl_lo_esti(clo)(lowerIcl_lo_esti(clo)(lower (Ensemble(clo)(EnsembleIcl_esti(clo)Icl_esti UpperUpperUpper LowerLowerLower tcl_up(upper(uppercl_up˘cl_upSD cloth) cloth) (upper t(lower(lowertcl_lo ˘SD SD (lowertskt sk± ± SD SDt sk ˘ SD(clo)(clo)(upper (face)(upper (clo)(lower(clo)(lower (Ensemble(clo)(Ensemble(clo) clothingUpperclothingUpper clothingLowerclothingLower (upper(uppert t ± cloth)±SD cloth)SD tcl_lot(lowercl_lo (lower± ± SD SD tskt sk± ± SD SD clothing)(upperclothing)(upper clothing)clothing)(lower(lower (Ensembleclothing)(Ensembleclothing) clothingUpperUpper clothingLowerLower (upper(upper cloth) cloth)˝ (lower(lower ˝ (face)(face)sk sk±± (°C) (°C) ˝ clothing)(upper(upper (upperclothing)(lower(lower (Ensembleclothing)(Ensemble(lower (Ensemble clothingclothingclothing clothingclothingclothing (upper(uppercloth)(°C)(°C) cloth) (cloth) C) cloth)cloth)(lower(lower (°C)cloth) (°C) ( C)(face)t(face)t (°C)SD (°C)SD ( C)clothing)clothing) clothing)clothing) clothing)clothing) clothingclothing clothingclothing (upper(upper(°C)(°C) cloth) cloth) cloth)cloth)(lower(lower (°C) (°C) (face)(face)(face) (°C)(°C) (°C) clothing)clothing)(clo)(clo) clothing)clothing)clothing)(clo)(clo) (clo) clothing)clothing)clothing)(clo)(clo) (clo) clothing) (clo) clothingclothing clothingclothing (°C)(°C)(°C) cloth)cloth) (°C) (°C) (face)(face) (°C) (°C) clothing)clothing)(clo)(clo) clothing)clothing)(clo)(clo) clothing)clothing)(clo)(clo) (°C)(°C) cloth)cloth)cloth) (°C)(°C) (°C) (clo)(clo) (clo)(clo) (clo)(clo) 25.7725.77 ± ±0.35 0.35 27.8327.83 ± ±0.67 0.67 33.1033.10 ± ±0.26 0.26 1.151.15(clo)(clo)(clo) 0.650.65(clo)(clo)(clo) 1.661.66(clo)(clo)(clo) 25.7725.77 ± ±0.35 0.35 27.8327.83 ± ±0.67 0.67 33.1033.10 ± ±0.26 0.26 1.151.15 0.650.65 1.661.66

25.7725.7725.7725.77 ± ˘ ±±0.35 0.350.35 27.8327.8327.83 ± 27.83 ±±0.67 0.670.67 ˘ 0.6733.1033.1033.10 ± ±±0.26 0.260.26 33.10 ˘ 0.261.151.151.15 1.150.650.650.65 0.651.661.661.66 1.66 25.7725.77 ± ±0.35 0.35 27.8327.83 ± ±0.67 0.67 33.1033.10 ± ±0.26 0.26 1.151.15 0.650.65 1.661.66 25.7725.77 ± ±0.35 0.35 27.8327.83 ± ±0.67 0.67 33.1033.10 ± ±0.26 0.26 1.151.15 0.650.65 1.661.66

27.55 27.55 ± ±0.07 0.07 27.5927.59 ± ±0.14 0.14 33.3533.35 ± ±0.07 0.07 0.740.74 0.730.73 1.381.38 27.5527.55 ± ±0.07 0.07 27.5927.59 ± ±0.14 0.14 33.3533.35 ± ±0.07 0.07 0.740.74 0.730.73 1.381.38 27.5527.55 ± ±0.07 0.07 27.5927.59 ± ±0.14 0.14 33.3533.35 ± ±0.07 0.07 0.740.74 0.730.73 1.381.38 27.5527.5527.55 ±˘ ±0.07 0.07 27.5927.59 ± 27.59 ±0.14 0.14 ˘ 0.1433.3533.35 ± ±0.07 0.07 33.35 ˘ 0.070.740.74 0.740.730.73 0.731.381.38 1.38 27.5527.55 ± ±0.07 0.07 27.5927.59 ± ±0.14 0.14 33.3533.35 ± ±0.07 0.07 0.740.74 0.730.73 1.381.38 27.5527.55 ± ±0.07 0.07 27.5927.59 ± ±0.14 0.14 33.3533.35 ± ±0.07 0.07 0.740.74 0.730.73 1.381.38

27.25 27.25 ± ±0.07 0.07 27.8827.88 ± ±0.06 0.06 34.5034.50 ± ±0.00 0.00 0.930.93 0.770.77 1.631.63 27.2527.25 ± ±0.07 0.07 27.8827.88 ± ±0.06 0.06 34.5034.50 ± ±0.00 0.00 0.930.93 0.770.77 1.631.63 27.2527.2527.25 ± ±±0.07 0.070.07 27.8827.8827.88 ± ±±0.06 0.060.06 34.5034.5034.50 ± ±±0.00 0.000.00 0.930.930.93 0.770.770.77 1.631.631.63 27.2527.2527.25 ±˘ ±0.07 0.07 27.8827.88 ± 27.88 ±0.06 0.06 ˘ 0.0634.5034.50 ± ±0.00 0.00 34.50 ˘ 0.000.930.93 0.930.770.77 0.771.631.63 1.63 27.2527.25 ± ±0.07 0.07 27.8827.88 ± ±0.06 0.06 34.5034.50 ± ±0.00 0.00 0.930.93 0.770.77 1.631.63

26.55 26.55 ± ±0.13 0.13 26.8026.80 ± ±0.11 0.11 34.0334.03 ± ±0.06 0.06 1.091.09 1.011.01 1.681.68 26.5526.55 ± ±0.13 0.13 26.8026.80 ± ±0.11 0.11 34.0334.03 ± ±0.06 0.06 1.091.09 1.011.01 1.681.68 26.5526.55 ± ±0.13 0.13 26.8026.80 ± ±0.11 0.11 34.0334.03 ± ±0.06 0.06 1.091.09 1.011.01 1.681.68 26.5526.55 ± ±0.13 0.13 26.8026.80 ± ±0.11 0.11 34.0334.03 ± ±0.06 0.06 1.091.09 1.011.01 1.681.68 26.5526.5526.55 ±˘ ±0.13 0.13 26.8026.80 ± 26.80 ±0.11 0.11 ˘ 0.1134.0334.03 ± ±0.06 0.06 34.03 ˘ 0.061.091.09 1.091.011.01 1.011.681.68 1.68

27.3727.37 ± ±0.06 0.06 26.2326.23 ± ±0.07 0.07 33.5733.57 ± ±0.15 0.15 0.800.80 1.151.15 1.811.81 27.3727.37 ± ±0.06 0.06 26.2326.23 ± ±0.07 0.07 33.5733.57 ± ±0.15 0.15 0.800.80 1.151.15 1.811.81 27.3727.3727.37 ± ±±0.06 0.060.06 26.2326.2326.23 ± ±±0.07 0.070.07 33.5733.5733.57 ± ±±0.15 0.150.15 0.800.800.80 1.151.151.15 1.811.811.81 SensorsSensors 2016 2016, 16, 16, 341, 341 27.3727.37 ± ±0.06 0.06 26.2326.23 ± ±0.07 0.07 33.5733.57 ± ±0.15 0.15 0.800.80 1.151.15 9 of91.81 of 1.8116 16 SensorsSensors 2016 2016, 16, 16, 341, 341 27.3727.3727.37 ±˘ ±0.06 0.06 26.2326.23 ± 26.23 ±0.07 0.07 ˘ 0.0733.5733.57 ± ±0.15 0.15 33.57 ˘ 0.150.800.80 0.801.151.15 9 9of 1.151.81 of 1.8116 16 1.81

TableTable 4. 4. Cont Cont. . TableTable 4. 4. Cont Cont. .

26.4326.4326.43 ±˘ ±0.25 0.25 26.7126.71 ± 26.71 ±0.13 0.13 ˘ 0.1333.2333.23 ± ±0.12 0.12 33.23 ˘ 0.120.960.96 0.960.950.95 0.951.761.76 1.76

27.0027.00 ± ±0.14 0.14 27.5827.58 ± ±0.09 0.09 34.0334.03 ± ±0.06 0.06 0.950.95 0.800.80 1.611.61 27.0027.0027.00 ±˘ ±0.14 0.14 27.5827.58 ± 27.58 ±0.09 0.09 ˘ 0.0934.0334.03 ± ±0.06 0.06 34.03 ˘ 0.060.950.95 0.950.800.80 0.801.611.61 1.61

TotalTotal (Mean (Mean ± ±SD) SD) 26.9326.93 ± ±0.51 0.51 27.2327.23 ± ±0.64 0.64 33.6933.69 ± ±0.51 0.51 0.930.93 ± ±0.15 0.15 0.870.87 ± ±0.18 0.18 1.651.65 ± ±0.14 0.14 TotalTotalTotal (Mean (Mean (Mean˘ ± ±SD) SD) 26.93 26.9326.93 ±˘ ±0.51 0.51 27.2327.23 ± 27.23 ±0.64 0.64 ˘ 0.6433.6933.69 ± ±0.51 0.51 33.69 0.93˘0.930.51 ± ±0.15 0.15 0.930.870.87˘ 0.15± ±0.18 0.18 0.871.651.65˘ ± ±0.180.14 0.14 1.65 ˘ 0.14

TableTable 5. 5. Results Results of of measured measured mean mean temperature temperature and and estimated estimated clothing clothing insulation insulation concerning concerning summersummer clothing clothing ensembles ensembles after after 30 30 min min of of exposure. exposure.

ThermalThermal Images Images MeasuredMeasured Mean Mean Temperature Temperature EstimatedEstimated Clothing Clothing Insulation Insulation Icl_up_estiIcl_up_esti Icl_lo_estiIcl_lo_esti Icl_estiIcl_esti tcl_upcl_up ±± SD tcl_locl_lo ±± SD Icl_up_estiIcl_up_esti Icl_lo_estiIcl_lo_esti Icl_estiIcl_esti cl_uptcl_up± SD cl_lotcl_lo± SD UpperUpper LowerLower t t ± SD SD t t ± SD SD tskt sk± ± SD SD (upper(upper (lower(lower (Ensemble(Ensemble UpperUpper LowerLower (upper cloth) (lower tskt sk± ± SD SD (upper(upper (lower(lower (Ensemble(Ensemble clothingclothing clothingclothing (upper(upper cloth) cloth) (lower(lower clothing)clothing) clothing)clothing) clothing)clothing) clothingclothing clothingclothing (face)(face) (°C) (°C) clothing)clothing) clothing)clothing) clothing)clothing) (°C)(°C) cloth)cloth) (°C) (°C) (°C) cloth) (°C) (clo)(clo) (clo)(clo) (clo)(clo)

29.1529.15 ± ±0.13 0.13 27.9127.91 ± ±0.06 0.06 33.6033.60 ± ±0.27 0.27 0.490.49 0.740.74 1.181.18

29.5829.58 ± ±0.22 0.22 27.8027.80 ± ±0.32 0.32 34.4834.48 ± ±0.21 0.21 0.500.50 0.850.85 1.291.29

30.0530.05 ± ±0.07 0.07 27.0227.02 ± ±0.07 0.07 33.6333.63 ± ±0.06 0.06 0.350.35 0.990.99 1.281.28

29.9029.90 ± ±0.10 0.10 26.5926.59 ± ±0.06 0.06 33.5333.53 ± ±0.21 0.21 0.370.37 1.131.13 1.411.41

29.1029.10 ± ±0.22 0.22 27.0027.00 ± ±0.15 0.15 33.8533.85 ± ±0.24 0.24 0.520.52 1.021.02 1.441.44

28.9328.93 ± ±0.12 0.12 27.3827.38 ± ±0.13 0.13 34.0334.03 ± ±0.21 0.21 0.570.57 0.920.92 1.401.40

TotalTotal (Mean (Mean ± ±SD) SD) 29.4529.45 ± ±0.46 0.46 27.2827.28 ± ±0.51 0.51 33.8533.85 ± ±0.36 0.36 0.460.46 ± ±0.09 0.09 0.940.94 ± ±0.14 0.14 1.341.34 ± ±0.10 0.10 SensorsSensors 2016 2016, 16, 16, 341, 341 9 of9 of 16 16 SensorsSensors 2016 2016, 16, 16, 341, 341 9 9of of 16 16 SensorsSensors 2016 2016, 16, 16, 341, 341 9 9of of 16 16 SensorsSensors 2016 2016, 16,, 16, 341,, 341 9 9of of 16 16 TableTable 4. 4. Cont Cont.. . TableTable 4. 4. Cont Cont. . TableTable 4. 4. Cont Cont. ..

26.4326.43 ± ±0.25 0.25 26.7126.71 ± ±0.13 0.13 33.2333.23 ± ±0.12 0.12 0.960.96 0.950.95 1.761.76 26.4326.43 ± ±0.25 0.25 26.7126.71 ± ±0.13 0.13 33.2333.23 ± ±0.12 0.12 0.960.96 0.950.95 1.761.76 26.4326.43 ± ±0.25 0.25 26.7126.71 ± ±0.13 0.13 33.2333.23 ± ±0.12 0.12 0.960.96 0.950.95 1.761.76

27.0027.00 ± ±0.14 0.14 27.5827.58 ± ±0.09 0.09 34.0334.03 ± ±0.06 0.06 0.950.95 0.800.80 1.611.61 Sensors 16 27.0027.00 ± ±0.14 0.14 27.5827.58 ± ±0.09 0.09 34.0334.03 ± ±0.06 0.06 0.950.95 0.800.80 1.611.61 2016, , 341 27.0027.00 ± ±0.14 0.14 27.5827.58 ± ±0.09 0.09 34.0334.03 ± ±0.06 0.06 0.950.95 0.800.80 1.611.61 9 of 16

Total (Mean ± SD) 26.93 ± 0.51 27.23 ± 0.64 33.69 ± 0.51 0.93 ± 0.15 0.87 ± 0.18 1.65 ± 0.14 TotalTotal (Mean (Mean ± ±SD) SD) 26.9326.93 ± ±0.51 0.51 27.2327.23 ± ±0.64 0.64 33.6933.69 ± ±0.51 0.51 0.930.93 ± ±0.15 0.15 0.870.87 ± ±0.18 0.18 1.651.65 ± ±0.14 0.14 TotalTotal (Mean (Mean ± ±SD) SD) 26.9326.93 ± ±0.51 0.51 27.2327.23 ± ±0.64 0.64 33.6933.69 ± ±0.51 0.51 0.930.93 ± ±0.15 0.15 0.870.87 ± ±0.18 0.18 1.651.65 ± ±0.14 0.14 TableTotalTotal 5. (Mean Results(Mean ± ±SD) SD) of measured26.9326.93 ± ±0.51 0.51 mean 27.2327.23 temperature ± ±0.64 0.64 33.6933.69 and ± ±0.51 0.51 estimated 0.930.93 ± ±0.15 0.15 clothing 0.870.87 ± insulation ±0.18 0.18 1.651.65 concerning ± ±0.14 0.14 summer TableTable 5. 5. Results Results of of measured measured mean mean temperature temperature and and estimated estimated clothing clothing insulation insulation concerning concerning TableTable 5. 5. Results Results of of measured measured mean mean temperature temperature and and estimated estimated clothing clothing insulation insulation concerning concerning clothingsummerTableTablesummer ensembles 5. 5. clothing Results clothingResults afterensemblesof ensemblesof measured measured 30 minafter after mean 30mean of30 min min exposure.temperature temperaturetemperatureof of exposure. exposure. and and estimated estimated clothing clothing insulation insulationinsulation concerning concerning TablesummersummerTable 5. 5. clothing Results clothingResults ensemblesof ensemblesof measured measured after after mean 30mean 30 min min temperature temperatureof of exposure. exposure. and and estimated estimated clothing clothing insulation insulation concerning concerning summersummer clothing clothing ensembles ensembles after after 30 30 min min of of exposure. exposure. ThermalThermalsummersummer Images Images clothing clothing ensembles ensemblesMeasured Measuredafter after 30 30 min Mean min Mean of of exposure.Temperature exposure.Temperature EstimatedEstimated Clothing Clothing Insulation Insulation ThermalThermal Images Images MeasuredMeasured Mean Mean Temperature Temperature EstimatedEstimated Clothing Clothing Insulation Insulation ThermalThermal Images Images MeasuredMeasured Measured Mean Mean Mean Temperature Temperature TemperatureIcl_up_estiIcl_up_estiEstimatedEstimated ClothingIcl_lo_esti ClothingIcl_lo_esti Estimated Insulation Insulation ClothingIcl_estiIcl_esti Insulation ThermalThermal Images Images tcl_uptcl_up Measured± Measured± SD SD tMeancl_lo tMeancl_lo ± ±TemperatureSD TemperatureSD Icl_up_estiIcl_up_estiEstimatedEstimated ClothingIcl_lo_esti ClothingIcl_lo_esti Insulation Insulation Icl_estiIcl_esti cl_upcl_up±± cl_locl_lo±± Icl_up_estiIcl_up_esti Icl_lo_estiIcl_lo_esti Icl_estiIcl_esti UpperUpper LowerLower tcl_uptcl_up ± SD SD tcl_lotcl_lo ± SD SD tskt sk± ± SD SD (upper(upper Icl_up_esti(lower(lower (EnsembleIcl_lo_esti(Ensemble Icl_esti UpperUpper LowerLower tcl_uptcl_up ± ± SD SD tcl_lotcl_lo ± ± SD SD tskt sk± ± SD SD Icl_up_esti(upperIcl_up_esti(upper Icl_lo_esti(lowerIcl_lo_esti(lower (Ensemble(EnsembleIcl_estiIcl_esti UpperUpperUpper LowerLowerLower tcl_up(uppert(uppercl_uptcl_up˘ ±SD cloth) ± cloth)SD (upperSD tcl_lot(lowercl_lot(lower cl_lo± ±SD ˘SD SD (lowertskt sk± ± SD SDt sk ˘ SDIcl_up_estiIcl_up_esti(upper (face)(upper Icl_lo_esti(lowerIcl_lo_esti(lower (Ensemble(EnsembleIcl_estiIcl_esti UpperclothingUpper LowerclothingLower (uppert(uppercl_upcl_up ± cloth)± cloth)SD tcl_lo(lowercl_lo(lower ±± SD tskt sk± ± SD SD (upperclothing)(upper (lowerclothing)(lower (Ensemble(Ensembleclothing) clothingUpperclothingUpper clothingLowerclothingLower (upper(uppert cloth) cloth) ˝SD t(lower(lower SD ˝ (face)tsk(face)t sk± ± (°C)SD (°C)SD ˝ clothing)(upperclothing)(upper (upperclothing)(lowerclothing)(lower (Ensembleclothing)(lower(Ensembleclothing) (Ensemble clothingclothingUpperclothingUpper clothingclothingLowerclothingLower (upper(upper(uppercloth)(°C)(°C) cloth) (cloth) C) cloth)cloth)(lower(lower(lower (°C)cloth) (°C) ( C)(face)t(face)skt sk± ± (°C)SD (°C)SD ( C)clothing)(upperclothing)(upper clothing)(lowerclothing)(lower (Ensembleclothing)(Ensembleclothing) clothingclothing clothingclothing (upper(upper(°C)(°C) cloth) cloth) cloth)cloth)(lower(lower (°C) (°C) (face)(face) (°C) (°C) clothing)clothing)(clo)(clo) clothing)clothing)clothing)(clo)(clo) (clo) clothing)clothing)clothing)(clo)(clo) (clo) clothing) (clo) clothingclothing clothingclothing (°C)(°C) cloth)cloth) (°C) (°C) (face)(face) (°C) (°C) clothing)clothing)(clo)(clo) clothing)clothing)(clo)(clo) clothing)clothing)(clo)(clo) (°C)(°C) cloth)cloth) (°C) (°C) (clo)(clo) (clo)(clo) (clo)(clo) (°C) cloth) (°C) (clo)(clo) (clo)(clo) (clo)(clo)

29.1529.1529.15 ±˘ ±0.13 0.13 27.9127.91 ± 27.91 ±0.06 0.06 ˘ 0.0633.6033.60 ± ±0.27 0.27 33.60 ˘ 0.270.490.49 0.490.740.74 0.741.181.18 1.18 29.1529.15 ± ±0.13 0.13 27.9127.91 ± ±0.06 0.06 33.6033.60 ± ±0.27 0.27 0.490.49 0.740.74 1.181.18 29.1529.15 ± ±0.13 0.13 27.9127.91 ± ±0.06 0.06 33.6033.60 ± ±0.27 0.27 0.490.49 0.740.74 1.181.18

29.5829.58 ± ±0.22 0.22 27.8027.80 ± ±0.32 0.32 34.4834.48 ± ±0.21 0.21 0.500.50 0.850.85 1.291.29 29.5829.5829.58 ±˘ ±0.22 0.22 27.8027.80 ± 27.80 ±0.32 0.32 ˘ 0.3234.4834.48 ± ±0.21 0.21 34.48 ˘ 0.210.500.50 0.500.850.85 0.851.291.29 1.29 29.5829.58 ± ±0.22 0.22 27.8027.80 ± ±0.32 0.32 34.4834.48 ± ±0.21 0.21 0.500.50 0.850.85 1.291.29 29.5829.58 ± ±0.22 0.22 27.8027.80 ± ±0.32 0.32 34.4834.48 ± ±0.21 0.21 0.500.50 0.850.85 1.291.29

30.0530.05 ± ±0.07 0.07 27.0227.02 ± ±0.07 0.07 33.6333.63 ± ±0.06 0.06 0.350.35 0.990.99 1.281.28 30.0530.0530.05 ±˘ ±0.07 0.07 27.0227.02 ± 27.02 ±0.07 0.07 ˘ 0.0733.6333.63 ± ±0.06 0.06 33.63 ˘ 0.060.350.35 0.350.990.99 0.991.281.28 1.28 30.0530.05 ± ±0.07 0.07 27.0227.02 ± ±0.07 0.07 33.6333.63 ± ±0.06 0.06 0.350.35 0.990.99 1.281.28

29.9029.90 ± ±0.10 0.10 26.5926.59 ± ±0.06 0.06 33.5333.53 ± ±0.21 0.21 0.370.37 1.131.13 1.411.41 29.9029.90 ± ±0.10 0.10 26.5926.59 ± ±0.06 0.06 33.5333.53 ± ±0.21 0.21 0.370.37 1.131.13 1.411.41 29.9029.9029.90 ±˘ ±0.10 0.10 26.5926.59 ± 26.59 ±0.06 0.06 ˘ 0.0633.5333.53 ± ±0.21 0.21 33.53 ˘ 0.210.370.37 0.371.131.13 1.131.411.41 1.41 29.9029.90 ± ±0.10 0.10 26.5926.59 ± ±0.06 0.06 33.5333.53 ± ±0.21 0.21 0.370.37 1.131.13 1.411.41

29.1029.10 ± ±0.22 0.22 27.0027.00 ± ±0.15 0.15 33.8533.85 ± ±0.24 0.24 0.520.52 1.021.02 1.441.44 29.1029.10 ± ±0.22 0.22 27.0027.00 ± ±0.15 0.15 33.8533.85 ± ±0.24 0.24 0.520.52 1.021.02 1.441.44 29.1029.1029.10 ±˘ ±0.22 0.22 27.0027.00 ± 27.00 ±0.15 0.15 ˘ 0.1533.8533.85 ± ±0.24 0.24 33.85 ˘ 0.240.520.52 0.521.021.02 1.021.441.44 1.44

28.9328.93 ± ±0.12 0.12 27.3827.38 ± ±0.13 0.13 34.0334.03 ± ±0.21 0.21 0.570.57 0.920.92 1.401.40 28.9328.93 ± ±0.12 0.12 27.3827.38 ± ±0.13 0.13 34.0334.03 ± ±0.21 0.21 0.570.57 0.920.92 1.401.40 28.9328.93 ± ±0.12 0.12 27.3827.38 ± ±0.13 0.13 34.0334.03 ± ±0.21 0.21 0.570.57 0.920.92 1.401.40 28.9328.9328.93 ±˘ ±0.12 0.12 27.3827.38 ± 27.38 ±0.13 0.13 ˘ 0.1334.0334.03 ± ±0.21 0.21 34.03 ˘ 0.210.570.57 0.570.920.92 0.921.401.40 1.40

TotalTotal (Mean (Mean ± ±SD) SD) 29.4529.45 ± ±0.46 0.46 27.2827.28 ± ±0.51 0.51 33.8533.85 ± ±0.36 0.36 0.460.46 ± ±0.09 0.09 0.940.94 ± ±0.14 0.14 1.341.34 ± ±0.10 0.10 TotalTotal (Mean (Mean ± ±SD) SD) 29.4529.45 ± ±0.46 0.46 27.2827.28 ± ±0.51 0.51 33.8533.85 ± ±0.36 0.36 0.460.46 ± ±0.09 0.09 0.940.94 ± ±0.14 0.14 1.341.34 ± ±0.10 0.10 TotalTotal (Mean (Mean(Mean ± ±SD) SD) 29.4529.45 ± ±0.46 0.46 27.2827.28 ± ±0.51 0.51 33.8533.85 ± ±0.36 0.36 0.460.46 ± ±0.09 0.09 0.940.94 ± ±0.14 0.14 1.341.34 ± ±0.10 0.10 TotalTotalTotal (Mean (Mean (Mean˘ ± ±SD) SD) 29.45 29.4529.45 ±˘ ±0.46 0.46 27.2827.28 ± 27.28 ±0.51 0.51 ˘ 0.5133.8533.85 ± ±0.36 0.36 33.85 0.46˘0.460.36 ± ±0.09 0.09 0.460.940.94˘ 0.09± ±0.14 0.14 0.941.341.34˘ ± ±0.140.10 0.10 1.34 ˘ 0.10

Sensors 2016, 16, 341 10 of 16 But for the same lower clothing ensembles, winter lower clothing insulation measured equally to Tables 3–5 show the results of thermal images, the measured temperature, and estimated spring/fall lowerupper, clothing lower and insulation ensemble clothing as t-value insulation = 2.32, in eachp-value ensemble. = 0.059, as well as, spring/fall lower clothing insulation is measuredBut for equally the same to lower summer clothing lowerensembles, clothing winter lower insulation clothing insulation as t-value measured = 1.68, equallyp-value = 0.154. to spring/fall lower clothing insulation as t‐value = 2.32, p‐value = 0.059, as well as, spring/fall lower In conclusion,clothing the insulation proposed is measured estimation equally to method summer lower is effective clothing insulation to measure as t‐value its = 1.68, clothing insulation significantly inp‐ thesevalue = 0.154. different clothing ensembles as shown in Figure3. Next investigation on In conclusion, the proposed estimation method is effective to measure its clothing insulation accuracies of thesignificantly proposed in these method different will clothing be analyzed ensembles as using shown simulationin Figure 3. Next stated investigation in following on paragraph with the comparisonaccuracies between of the proposed resultant method temperatures will be analyzed using applied simulation estimated stated in following insulation paragraph values and those of with the comparison between resultant temperatures applied estimated insulation values and those reference insulationof reference values. insulation values.

Boxplot of upper clothing in summer, spring/fall, and winter condition Boxplot of lower clothing in summer, spring/fall, and winter condition 1.2 1.75

1.1 1.50

1.0 1.25 0.9

1.00 0.8

0.75 0.7 Clothing Insulation (Icl) Insulation Clothing Clothing Insulation (Icl) Insulation Clothing

0.50 0.6

0.5 summer upper clothing spring/fall upper clothing winter upper clothing summer lower clothing spring/fall lower clothing winter lower clothing

(a) (b)

Figure 3. Boxplot for the estimated clothing insulation in the case of summer, spring/fall, and winter Figure 3. Boxplotclothing for theensembles estimated using the clothing proposed insulationestimation method. in the (a) caseBoxplot of for summer, the estimated spring/fall, upper and winter clothing ensemblesclothing using insulation. the proposed (b) Boxplot for estimation the estimated lower method. clothing (a insulation.) Boxplot for the estimated upper clothing insulation; (b) BoxplotThe purpose for of the these estimated experiments lower was to clothing investigate insulation. whether the proposed estimation method is effective for measuring clothing insulation under different clothing conditions and to show how accurate the proposed method is compared with previous reference values. Therefore reference values are necessary and the reference insulation for an ensemble was obtained that based on a summation of the insulation of individual garments using the empirical equation, Equation (18) [14]

IIcl0.161 0.835 clu (18)

Table 6 shows the summarized results of the estimated clothing insulation, reference clothing insulation, and errors for ensembles and subjects. In order to analyze whether the proposed estimation method can measure different clothing insulation on the same subject or not, experiments were carried out between paired subjects, that is, three clothing ensembles tested on one subject independently. Therefore, paired t‐test was applied in statistics using Minitab Release 14. After checking the normality of all samples (upper and lower clothing insulation in three ensembles, six samples in total) with an Anderson‐Darling test (p > 0.05), a paired t‐test was applied to analyze the measurability of different insulation despite their variances. A coefficient of significance α = 0.01 (99% confidence interval) was adopted. Paired t for summer upper clothing—spring/fall upper clothing with null hypothesis t‐test of mean difference = 0 (vs. < 0) shows t‐value = −6.77, p‐value = 0.001. This analysis reveals that it is not plausible that the mean difference = 0, and so we can conclude that spring/fall upper clothing insulation can be measured better than summer upper clothing insulation using the proposed estimation method. Also, we can conclude that winter upper clothing insulation can be measured better than spring/fall upper clothing insulation using the proposed estimation method as t‐value = −4.63, p‐value = 0.002.

Sensors 2016, 16, 341 10 of 16

The purpose of these experiments was to investigate whether the proposed estimation method is effective for measuring clothing insulation under different clothing conditions and to show how accurate the proposed method is compared with previous reference values. Therefore reference values are necessary and the reference insulation for an ensemble was obtained that based on a summation of the insulation of individual garments using the empirical equation, Equation (18) [14]

Icl “ 0.161 ` 0.835 Iclu (18) ÿ Table6 shows the summarized results of the estimated clothing insulation, reference clothing insulation, and errors for ensembles and subjects. In order to analyze whether the proposed estimation method can measure different clothing insulation on the same subject or not, experiments were carried out between paired subjects, that is, three clothing ensembles tested on one subject independently. Therefore, paired t-test was applied in statistics using Minitab Release 14. After checking the normality of all samples (upper and lower clothing insulation in three ensembles, six samples in total) with an Anderson-Darling test (p > 0.05), a paired t-test was applied to analyze the measurability of different insulation despite their variances. A coefficient of significance α = 0.01 (99% confidence interval) was adopted. Paired t for summer upper clothing—spring/fall upper clothing with null hypothesis t-test of mean difference = 0 (vs. < 0) shows t-value = ´6.77, p-value = 0.001. This analysis reveals that it is not plausible that the mean difference = 0, and so we can conclude that spring/fall upper clothing insulation can be measured better than summer upper clothing insulation using the proposed estimation method. Also, we can conclude that winter upper clothing insulation can be measured better than spring/fall upper clothing insulation using the proposed estimation method as t-value = ´4.63, p-value = 0.002.

Table 6. Comparison of clothing insulations between estimated values and reference values.

Estimated Clothing Insulation Icl_ref Error Clothing Icl_up_esti ˘ SD Icl_lo_esti ˘ SD Icl_esti ˘ SD (Reference Icl) (clo) (Icl_esti-Icl_ref) (clo) Ensemble (Upper Clothing) (clo) (Lower Clothing) (clo) (Ensemble Clothing) (clo) Winter 1.37 ˘ 0.22 0.78 ˘ 0.14 1.95 ˘ 0.24 1.12 0.83 Spring/fall 0.93 ˘ 0.15 0.87 ˘ 0.18 1.65 ˘ 0.14 0.97 0.68 Summer 0.46 ˘ 0.09 0.94 ˘ 0.14 1.34 ˘ 0.10 0.73 0.61

3.2. Simulation: Comparison of Regional Temperature between Actual and Simulated with the Estimated Clothing Insulation Value and the Reference Clothing Insulation Value Although standard values of clothing insulation are described in ISO 9920 [15], they may be not a suitable reference for our experiments with sitting human subjects. The clothing insulation values in ISO 9920 are only for the standing posture and did not considered the fit of the clothing, especially as it pertains to upper clothing and the air layer between clothing that are crucial parameters for clothing insulation [8]. In addition, although the insulation of an ensemble calculated from the individual garment (Equation (18)) gives acceptable accuracy for typical indoor clothing, overall accuracies are on the order of ˘ 25% [16]. Thus, a simulation was conducted to analyze the accuracy of the proposed algorithm for estimating clothing insulation applied to a sitting person by comparing simulated temperature distribution with actual one. The thermal analysis software is PowerTherm that is based on the UC Berkeley Thermo-physiological Comfort Model (BTCM) which developed by Huizenga et al. and has been validated against empirical physiological responses in transient, non-uniform thermal environments [3,17,18]. The human model for the simulation was created by applying a standard height (172 cm) and weight (72.4 kg), male gender, body fat of 14.95 kg, skin emissivity of 0.95, absorptivity of 0.72 (brown skin), and a basal metabolic rate of 0.8 met, to be closely related to the test human subject as shown in Table2. The winter ensemble simulation model is designed to be wearing three layers of clothing with a winter working jacket as the outermost layer, similar to the test subject’s clothing as shown in Figure4. Sensors 2016, 16, 341 11 of 16

Table 6. Comparison of clothing insulations between estimated values and reference values.

Estimated Clothing Insulation Icl_ref Error Clothing Icl_up_esti ± SD Icl_lo_esti ± SD Icl_esti ± SD (Reference Icl) (Icl_esti-Icl_ref) Ensemble (Upper Clothing) (Lower Clothing) (Ensemble (clo) (clo) (clo) (clo) Clothing) (clo) Winter 1.37 ± 0.22 0.78 ± 0.14 1.95 ± 0.24 1.12 0.83 Spring/fall 0.93 ± 0.15 0.87 ± 0.18 1.65 ± 0.14 0.97 0.68 Summer 0.46 ± 0.09 0.94 ± 0.14 1.34 ± 0.10 0.73 0.61

3.2. Simulation: Comparison of Regional Temperature between Actual and Simulated with the Estimated Clothing Insulation Value and the Reference Clothing Insulation Value Although standard values of clothing insulation are described in ISO 9920 [15], they may be not a suitable reference for our experiments with sitting human subjects. The clothing insulation values in ISO 9920 are only for the standing posture and did not considered the fit of the clothing, especially as it pertains to upper clothing and the air layer between clothing that are crucial parameters for clothing insulation [8]. In addition, although the insulation of an ensemble calculated from the individual garment (Equation (18)) gives acceptable accuracy for typical indoor clothing, overall accuracies are on the order of ± 25% [16]. Thus, a simulation was conducted to analyze the accuracy of the proposed algorithm for estimating clothing insulation applied to a sitting person by comparing simulated temperature distribution with actual one. The thermal analysis software is PowerTherm that is based on the UC Berkeley Thermo-physiological Comfort Model (BTCM) which developed by Huizenga et al. and has been validated against empirical physiological responses in transient, non-uniform thermal environments [3,17,18]. The human model for the simulation was created by applying a standard height (172 cm) and weight (72.4 kg), male gender, body fat of 14.95 kg, skin emissivity of 0.95, absorptivity of 0.72 (brown skin), and a basal metabolic rate of 0.8 met, to be closely related to the test human subject as shown in Table 2. The winter ensemble simulation model is designed to be wearing three layers of clothing with a winter working jacket as the outermost layer, similar to the test subjectSensors’s 2016clothing, 16, 341 as shown in Figure 4. 11 of 16

Figure 4. A simulation model with various layers of clothing and body segments in the case of winter Figure 4. A simulation model with various layers of clothing and body segments in the case of ensemble. Tested insulation values are obtained from ISO 9920 and measurements. winter ensemble. Tested insulation values are obtained from ISO 9920 and measurements.

The simulation program can estimate the expected temperature of the skin and clothing of the Themodel simulation when the program test conditions can estimate and clothing the insulation expected values temperature are used as of input the parameters.skin and clothing Thus, of the model whenthe simulation the test conditions model can be and evaluated clothing what insulation insulation values value isare more used accurate as input by comparingparameters. the Thus, the simulatioestimatedn model temperature can be evaluated of the skinand what clothing insulation with temperature value is measurementsmore accurate by an by IR camera.comparing the estimatedFor temperature the purpose, a testof the setup skin was and designed clothing with thewith parameter temperature values shownmeasurements in Tables7– 9by on an a test IR camera. subject. The temperatures of the face and clothing were measured using an IR camera. The clothing For the purpose, a test setup was designed with the parameter values shown in Table 7–9 on a test insulation Icl is calculated using the proposed algorithm. subject. The temperatures of the face and clothing were measured using an IR camera. The clothing insulation IclTable is calculated 7. Simulation using input parameters the proposed concerning algorithm. each clothing ensembles (ensemble clothing insulation values estimated from mean values of environmental conditions).

t t t Humidity Icl_up_esti Icl_lo_esti Icl_esti Ensemble t (˝C) t (˝C) cl_up cl_lo sk v (m/s) r a (˝C) (˝C) (˝C) ar (%RH) (clo) (clo) (clo) winter 22.12 21.76 25.13 26.89 33.01 0.06 20.04 1.34 0.77 1.92 spring/fall 22.85 22.41 26.93 27.23 33.69 0.06 19.74 0.93 0.85 1.65 summer 23.40 22.79 29.45 27.28 33.85 0.07 18.94 0.46 0.93 1.32

Table 8. Layer properties table of upper body in simulation.

Thermal Resistance Layer No. (Chest) Layer No. (Arm) Material Thickness (mm) (m2¨ K/W) Winter working jacket 19 18 10 0.085 * (company’s uniform) 18 17 shirts long sleeves shirt collar 1 0.048 17 Underwear shirts sleeveless 1 0.009 Body segments (skin, muscle, 16~1 16~1 -- fat, bone etc.)

Table 9. Layer properties table of lower body in simulation.

Thickness Thermal Resistance Layer No. (Hip) Layer No. (Leg) Layer No. (Foot) Material (mm) (m2¨ K/W) 18 Shoes 1.5 0.003 17 Socks 1 0.003 18 17 Trousers straight fitted 1 0.034 17 Underwear pants briefs 1 0.006 Body segments (skin, 16~1 16~1 16~1 -- muscle, fat, bone etc.) Sensors 2016, 16, 341 12 of 16

The same test conditions were used as the input parameters in the simulation program to estimate the resulting clothing temperature (chest, legs) and face temperature. We analyzed two simulation models with different clothing insulation conditions: Case 1: Individual clothing insulation values without air layers between clothing. Thermal resistance values for each clothing layer are from ISO 9920 and measured data. The air insulation layer between clothing is not considered. Case 2: Ensemble thermal insulation values from the proposed clothing insulation Equation. The estimated clothing insulation is used as the ensemble thermal insulation values based on the proposed clothing insulation estimation. As shown in Tables 10–12 when the clothing insulation calculated by the proposed algorithm was used in the simulation mode, as in Case 2, temperatures simulated with the estimated insulation value were closer to the values of actual temperature than individual clothing insulation values without air layers between clothing, as in Case1. Compared with previous reference insulation values, the % face temperature error was almost same within 1% between cases, but the chest clothing temperature applying the proposed insulation value was more accurate with an 3% error reduction shown as ∆% error in Tables 10–12 and legs’ clothing temperature applying the proposed insulation value was more accurate with a 3.7%~6.2% error reduction. Therefore, it can be concluded that the proposed algorithm is more accurate than one using the previous measured individual garment insulation database for these clothing ensemble sets.

SensorsSensors 2016 ,201 16,6 341, 16 , 341 13 of 1316 of 16 Table 10. Comparison of experiment and simulation according to clothing insulation values inputs concerningTableTable winter 10. Comparison 10. clothingComparison of ensembles,experiment of experiment and (∆ %error)andsimulation simulation = according %error according ofto clothing Case to clothing 1 ´insulation%error insulation values of values Case inputs 2.inputs concerningconcerning winter winter clothing clothing ensembles ensembles, (Δ%error, (Δ%error) = %error) = %error of Case of Case 1 − %error 1 − %error of C aseof C 2ase. 2.

ClothingClothingClothing T Tempemp Temp SkinSkin TempSkin Temp (Face) Temp (Face) (Face) Simulation Model ChestChest LegsLegs SimulationSimulation Model Model Chest Legs %error%error %error%error %error%error (°C) (%error°C) (°C) (°C%error) (°C) (°C) %error (˝C) (Δ%error)(˝C) (Δ%error)(˝ C) (Δ%error) (∆%error)(Δ%error) (∆%error)(Δ%error) (Δ%error)(∆%error) IR cameraIR camera measurement measurement (Actual) (Actual) 33.0133.01 - - 25.1325.13 26.8926.89 - - IR camera measurement (Actual) 33.01 - 25.13 26.89 - CaseCase 1: Individual 1: Individual clothing clothing insulation insulation values values Case 1: Individual clothing insulation 34.16 3.48 27.53 9.55 28.63 6.47 34.1634.16 3.483.48 27.5327.53 9.559.55 28.63 6.47 6.47 withoutvalueswithout air without layer air layers between airs layers between clothing between clothing clothing CaseCaseCase 2: Ensemble 2: 2: Ensemble Ensemble thermal thermal thermal insulation insulation insulation values values values of the of the 4.12 4.12 4.12 6.576.57 6.57 2.752.75 2.75 estimatedofestimated the estimated clothing clothing insulation clothing insulation insulation (Icl_up_esti (Icl_up_esti = 1.34, = 1.34, 34.37 34.3734.37 26.7826.7826.78 27.6327.63 (´0.64)(−0.64)(−0.64) (2.98)(2.98)(2.98) (3.72)(3.72)(3.72) Icl_lo_esti(Icl_up_estiIcl_lo_esti = 0.77) = 0.77) 1.34, Icl_lo_esti = 0.77)

Case1C:ase1 : Case2Case2: :

TableTable 11. Comparison 11. Comparison of experiment of experiment and andsimulation simulation according according to clothing to clothing insulation insulation values values inputs inputs concerningconcerning spring/fall spring/fall clothing clothing ensembles, ensembles, (Δ%error (Δ%error) = %error) = %error of Case of Case 1 − %error 1 − %error of Case of Case 2. 2.

ClothingClothing Temp. Temp. SkinSkin Temp Temp (Face) (Face) ChestChest LegsLegs SimulationSimulation Model Model %error%er ror %error%error %error%error (°C) (°C) (°C) (°C) (°C) (°C) (Δ%error)(Δ%error) (Δ%error)(Δ%error) (Δ%error)(Δ%error) IR cameraIR camera measurement measurement (Actual) (Actual) 33.6933.69 - - 26.9326.93 27.2327.23 - - CaseCase 1: Individual 1: Individual clothing clothing insulation insulation values values 34.4334.43 2.20 2.20 28.4228.42 5.53 5.53 29.0429.04 6.65 6.65 withoutwithout air layers air layers between between clothing clothing CaseCase 2: Ensemble 2: Ensemble thermal thermal insulation insulation values values of the of the 2.70 2.70 4.34 4.34 2.09 2.09 estimatedestimated clothing clothing insulation insulation (Icl_up_esti (Icl_up_esti = 0.93, = 0.93, 34.6034.6 0 28.1028.1 0 27.8027.8 0 (−0.5)(− 0.5) (1.19)(1.19) (4.56)(4.56) Icl_lo_estiIcl_lo_esti = 0.85) = 0.85)

Case1C:ase1 : Case2Case2: :

TableTable 12. Comparison 12. Comparison of experiment of experiment and andsimulation simulation according according to clothing to clothing insulation insulation values va luesinputs inputs concerningconcerning summer summer clothing clothing ensembles ensembles, (Δ%error, (Δ%error) = %error) = %error of Case of Case 1 − %error 1 − %error of Case of Case 2. 2.

ClothingClothing Temp. Temp. SkinSkin Temp Temp (Face) (Face) ChestChest LegsLegs SimulationSimulation Model Model %error%error %error%error %error%error (°C) (°C) (°C) (°C) (°C) (°C) (Δ%error)(Δ%error) (Δ%error)(Δ%error) (Δ%error)(Δ%error) IR cameraIR camera measurement measurement (Actual) (Actual) 33.8533.85 - - 29.4529.45 27.2827.28 - - CaseCase 1: Individual 1: Individual clothing clothing insulation insulation values values without without 33.9633.96 0.32 0.32 29.7329.73 0.95 0.9529.24 29.24 7.18 7.18 air layersair layers between between clothing clothing SensorsSensors 201 6201, 166,, 34116, 341 13 of13 1 6of 16

TableTable 10. 10Comparison. Comparison of experimentof experiment and and simulation simulation according according to clothingto clothing insulation insulation values values inputs inputs concerningconcerning winter winter clothing clothing ensembles ensembles, (Δ,%error (Δ%error) = %error) = %error of Case of Case 1 − 1%error − %error of Case of Case 2. 2.

ClothingClothing Temp Temp SkinSkin Temp Temp (Face) (Face) ChestChest LegsLegs SimulationSimulation Model Model %error%error %error%error %error%error (°C)( °C) (°C)( °C) (°C)( °C) (Δ%error)(Δ%error) (Δ%error)(Δ%error) (Δ%error)(Δ%error) IR cameraIR camera measurement measurement (Actual) (Actual) 33.0133.01 - - 25.1325.13 26.8926.89 - - CaseCase 1: Individual 1: Individual clothing clothing insulation insulation values values 34.1634.16 3.483.48 27.5327.53 9.559.55 28.6328.63 6.476.47 withoutwithout air layersair layers between between clothing clothing CaseCase 2: Ensemble 2: Ensemble thermal thermal insulation insulation values values of the of the 4.124.12 6.576.57 2.752.75 estimatedestimated clothing clothing insulation insulation (Icl_up_esti (Icl_up_esti = 1.34, = 1.34, 34.3734.37 26.7826.78 27.6327.63 (−0.64)(−0.64) (2.98)(2.98) (3.72)(3.72) Icl_lo_estiIcl_lo_esti = 0.77) = 0.77)

Sensors 2016, 16, 341 13 of 16 Case1Case1: : Case2Case2: :

Table 11. Comparison of experiment and simulation according to clothing insulation values inputs concerning spring/fall clothing ensembles, (∆%error) = %error of Case 1 ´ %error of Case 2.

Sensors 2016, 16, 341 Sensors 2016, 16, 341Clothing Temp.13 of 16 13 of 16 TableTable 11. 11Comparison. ComparisonSkin of Temp experimentof experiment (Face) and and simulation simulation according according to clothingto clothing insulation insulation values values inputs inputs Chest Legs TableSimulation 10. Comparison Modelconcerningconcerning of experiment spring/fall spring/fall and clothing simulation clothing ensembles, ensembles, according (Δ%error to(Δ %errorclothing)Table = %error) = insulation %error 10. ofComparison Case of valuesCase 1 − 1%error −inputsof %error experiment of Case of Case 2 .and 2. simulation according to clothing insulation values inputs %error %error concerning winter clothing ensembles(˝C), (Δ%error %error) = %error (∆%error) of Case 1 − %errorconcerning (˝C) of Case winter 2. clothing ensembles(˝C) , (Δ%error) = %error of Case 1 − %error of Case 2. (∆%error) ClothingClothing Temp.( ∆T%error)emp. SkinSkin Temp Temp (Face) (Face) IR camera measurement Clothing TempChestChest LegsLegs Clothing Temp Simulation33.69Simulation Model ModelSkin Temp - (Face) 26.93 27.23 - Skin Temp (Face) (Actual) Chest%error%error %errorLegs%error %error%error Chest Legs Simulation Model (°C)( °C) Simulation(°C)( °C Model) (°C)( °C) Case 1: Individual clothing %error (Δ%error)(Δ%error%error) (Δ%error)(Δ%error)%error (Δ%error)(Δ%error) %error %error %error (°C) (°C) (°C) (°C) (°C) (°C) insulation valuesIR without cameraIR camera air measurement measurement34.43 (Actual) (Actual) 2.20(Δ%error) 33.6933.69 28.42 (Δ- %error)- 26.93 5.5326.93 (Δ 29.04%error) 27.23 27.236.65 - - (Δ%error) (Δ%error) (Δ%error) layers between clothing IR camera measurementCaseCase 1(Actual): Individual 1: Individual clothing clothing insulation insulation33.01 values values - IR camera25.13 measurement (Actual)26.89 - 33.01 - 25.13 26.89 - Case 2: Ensemble thermal 34.4334.43 2.202.20 28.4228.42 5.535.53 29.0429.04 6.656.65 withoutwithout air layersair layers between between clothing clothing Caseinsulation 1: Individual values clothing of the insulation values Case 1: Individual clothing insulation values 34.16 2.70 3.48 27.53 9.55 4.3428.63 6.47 2.09 34.16 3.48 27.53 9.55 28.63 6.47 withoutestimated air layer clothings betweenCaseCase 2: Ensembleclothing 2: Ensemble 34.60 thermal thermal insulation insulation values values of the of thewithout air28.10 layers between clothing 27.80 (´0.5) 2.702.70 (1.19) 4.344.34 (4.56) 2.092.09 Caseinsulation 2: Ensemble (Icl_up_esti thermalestimatedestimated= insulation 0.93, clothing clothing values insulation insulation of the ( Icl_up_esti (Icl_up_esti = 0.93, = 0.93, Case34.6 34.62:0 Ensemble0 thermal28.128.10 insulation 0 values27.8 of27.80 the0 4.12 (−0.5)(−6.570.5) (1.19)(1.19)2.75 (4.56)(4.56) 4.12 6.57 2.75 estimatedIcl_lo_esti clothing= 0.85) insulationIcl_lo_estiIcl_lo_esti =( Icl_up_esti0.85) = 0.85) = 1.34, 34.37 estimated26.78 clothing insulation27.63 (Icl_up_esti = 1.34, 34.37 26.78 27.63 (−0.64) (2.98) (3.72) (−0.64) (2.98) (3.72) Icl_lo_esti = 0.77) Icl_lo_esti = 0.77)

Case1Case1: : Case2Case2: : Case1: Case2: Case1: Case2:

Table 12. ComparisonTableTable 1 of2. experiment1Comparison2. Comparison of and experimentof experiment simulation and and simulation accordingsimulation according according to clothing to clothingto clothing insulation insulation insulation valuesvalues values inputs inputs inputs Table 11. Comparisonconcerningconcerning of experiment summer summer and clothing clothingsimulation ensembles ensembles according, (Δ,%error (Δ to%error clothing) = Table%error) = %error insulation 11 of. ComparisonCase of Case 1values − 1%error − %errorinputsof experiment of Case of Case 2. 2and. simulation according to clothing insulation values inputs ´ concerningconcerning summer spring/fallclothing clothing ensembles, ensembles, (Δ%error (∆%error)) = %error = of %error Case 1concerning of− %error Case of 1 spring/fall Case%error 2. clothing of Case ensembles, 2. (Δ%error) = %error of Case 1 − %error of Case 2. ClothingClothing Temp. Temp. SkinSkin Temp Temp (Face) (Face) ClothingClothing Temp.Chest Temp.Chest LegsLegs Clothing Temp. SimulationSimulation Model ModelSkin Temp (Face) Skin Temp (Face) Chest%error %error Legs%error %error %error%error Chest Legs SimulationSimulation Model Model (°C)( °C) ChestSimulation(°C )(Model °C) Legs(°C)( °C) %error %error(Δ%error)(Δ%error) (Δ%error%error)(Δ%error) (Δ%error)(Δ%error) %er ror %error %error (°C) %error (°C) %error(°C) %error (°C) (°C) (°C) IR cameraIR camera measurement measurement (Actual) (Actual) ( ˝C) (Δ%error) 33.85(33.85˝ C)(Δ%error) - - 29.4529.45 (Δ( %error)˝C) 27.2827.28 - - (Δ%error) (Δ%error) (Δ%error) (∆%error) (∆%error) (∆%error) IR camera measurementCaseCase 1 :(Actual) Individual 1: Individual clothing clothing insulation insulation33.69 values values without- withoutIR 26.93 camera measurement 27.23 (Actual) - 33.69 - 26.93 27.23 - 33.9633.96 0.320.32 29.7329.73 0.950.95 29.2429.24 7.187.18 Case IR1: Individual camera measurement airclothing layersair layers insulation between (Actual) between clothing values clothing 33.85 -Case 1: 29.45Individual clothing insulation 27.28 values - Case 1: Individual clothing insulation values 34.43 2.20 28.42 5.53 29.04 6.65 34.43 2.20 28.42 5.53 29.04 6.65 without air layers between clothing 33.96 0.32without 29.73 air layers between 0.95 clothing 29.24 7.18 without air layers between clothing Case 2: Ensemble thermal insulation values of the Case 2: Ensemble thermal insulation values of the Case 2: Ensemble thermal insulation values 2.700.71 4.34 1.02 2.09 0.99 2.70 4.34 2.09 estimatedof the clothing estimated insulation clothing ( insulationIcl_up_esti = 0.93, 34.0934.60 estimated28.1029.75 clothing insulation27.80 (Icl_up_esti27.55 = 0.93, 34.60 28.10 27.80 (−(0.5)´0.39) (1.19) (´0.07) (4.56) (6.19) (−0.5) (1.19) (4.56) Icl_lo_esti(Icl_up_esti = 0.85)= 0.46, Icl_lo_esti = 0.93) Icl_lo_esti = 0.85)

Case1: Case2: Case1: Case2:

3.3. ExperimentTable 12. Comparison with the Effect of experiment Of Air and Layer simulation Thickness according to clothingTable insulation 12. Comparison values inputsof experiment and simulation according to clothing insulation values inputs concerning summer clothing ensembles, (Δ%error) = %error of Case 1 −concerning %error of Case summer 2. clothing ensembles, (Δ%error) = %error of Case 1 − %error of Case 2.

We supposed that the clothing insulation differences betweenClothing the estimated Temp. value and reference Clothing Temp. Skin Temp (Face) Skin Temp (Face) value are caused by the effect of air layer between clothing becauseChest the clothingLegs fit (tight or loose Chest Legs Simulation Model Simulation Model %error %error %error %error %error %error fitting) and the posture of the human subject(°C (standing) or(°C sitting) on a(°C chair)) are such a significant(°C) (°C) (°C) (Δ%error) (Δ%error) (Δ%error) (Δ%error) (Δ%error) (Δ%error) factorsIR camera which measurement can change (Actual) the volume of the clothing33.85 micro-environment- IR camera29.45 measurement 27.28 (Ac (airtual) insulation). - 33.85 - 29.45 27.28 - CaseTo 1: verifyIndividual whether clothing insulation the proposed values without algorithm can reflectCase 1 the: Individual air layers clothing effect insulation or not,values an without experiment 33.96 0.32 29.73 0.95 29.24 7.18 33.96 0.32 29.73 0.95 29.24 7.18 wasair conducted layers betweenwith clothing several different thicknesses of airair layerslayers between on the clothing test subject. Depending on the fit of the clothing, different thicknesses of air layers can affect the clothing insulation. To simulate the thickness of air layers, air cap films were wrapped around the upper body as shown in Table 13. For each test, the seated human subject wore the same pants but with different air layer thickness under a tight-fitting long-sleeved shirt. Sensors 2016, 16Sensors, 341 2016, 16, 341 14 of 16 14 of 16 Sensors 2016, 16SensorsSensors, 341 20162016,, 1616,, 341341 14 of 16 1414 ofof 1616

Table 12. ConTablet. 12. Cont. Table 12. ConTableTablet. 1212.. ConCont.t. Case 2: EnsembleCase thermal2: Ensemble insulation thermal values insulation of the values of the Case 2: EnsembleCaseCase 2:2:thermal EnsembleEnsemble insulation thermalthermal values insulationinsulation of the valuesvalues ofof thethe 0.71 0.71 1.02 1.02 0.99 0.99 estimated clothingestimated insulation clothing (Icl_up_esti insulation = (0.46,Icl_up_esti = 0.46,34.09 0.7134.09 29.750.710.71 1.0229.75 27.551.021.02 0.9927.55 0.990.99 estimated clothingestimatedestimated insulation clothingclothing (Icl_up_esti insulationinsulation = (( Icl_up_esti0.46,Icl_up_esti == 0.46,0.46,34.09 (−0.39)34.0934.09 29.75(−0.39) ( −0.07)29.7529.75 27.55(−0.07) (6.19)27.5527.55 (6.19) Icl_lo_esti = 0.93)Icl_lo_esti = 0.93) (−0.39) ((−−0.39)0.39) ( −0.07) ((−−0.07)0.07) (6.19) (6.19)(6.19) Icl_lo_esti = 0.93)Icl_lo_estiIcl_lo_esti == 0.93)0.93)

Case1: Case1: Case2: Case2: Case1: Case1:Case1: Case2: Case2:Case2:

3.3. Experiment3.3. withExperiment the Effect with Of the Air Effect Layer Of Thickness Air Layer Thickness 3.3. Experiment3.3.3.3. ExperimentwithExperiment the Effect withwith Of thethe Air EffectEffect Layer OfOf Thickness AirAir LayerLayer ThicknessThickness We supposedWe thatsupposed the clothing that the insulation clothing differencesinsulation differencesbetween the between estimated the valueestimated and value and We supposedWeWe supposedthatsupposed the clothing thatthat thethe insulation clothingclothing insulationinsulationdifferences differences differencesbetween the betweenbetween estimated thethe estimatedvalueestimated and valuevalue andand reference valuereference are caused value areby thecaused effect by of the air effect layer ofbetween air layer clothing between because clothing the because clothing the fit clothing(tight fit (tight reference valuereferencereference are caused valuevalue areareby thecausedcaused effect byby of thethe air effecteffect layer of ofbetween airair layerlayer clothing betweenbetween because clothingclothing the becausebecause clothing thethe fit clothing clothing(tight fitfit (tight(tight or loose fitting)or loose and fitting) the posture and the of theposture human of thesubject human (standing subject or (standing sitting on or asitting chair) onare a suchchair) a are such a or loose fitting)oror looseloose and fitting)fitting) the posture andand thethe of postureposturethe human ofof the thesubject humanhuman (standing subjectsubject or(standing(standing sitting on oror asittingsitting chair) on onare aa suchchair)chair) a areare suchsuch aa significant factorssignificant which factors can change which canthe volumechange theof the volume clothing of the micro clothing‐environment micro‐environment (air insulation). (air insulation). significant significantfactorssignificant which factorsfactors can changewhichwhich can canthe changechangevolume thethe of thevolumevolume clothing ofof thethe micro clothingclothing‐environment micromicro‐‐environmentenvironment (air insulation). (air(air insulation).insulation). To verify whetherTo verify the whether proposed the algorithm proposed can algorithm reflect thecan air reflect layers the effect air layers or not, effect an experiment or not, an experiment To verify whetherToTo verifyverify the whetherwhether proposed thethe algorithm proposedproposed can algorithmalgorithm reflect canthecan air reflectreflect layers thethe effect airair layerslayers or not, effecteffect an experiment oror not,not, anan experimentexperiment was conductedwas withconducted several with different several thicknesses different thicknessesof air layers of on air the layers test subject.on the test Depending subject. 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AirAir Layer Layer AirConditions LayerConditions Conditions Visible Thermal Skin Temp,Cloth Temp,Cloth Cloth Temp, Temp, tsk ´ tcl Estimated Air Layer AirConditions Layer Conditions Thermal SkinThermal Temp, SkinCloth Temp, Temp, tClothsk − tcl Temp, Estimated tsk − tcl Estimated (ThicknessAir Layer AirConditions Layer Conditions Thermal SkinThermal Temp, SkinCloth˝ Temp, Temp,Abdomen Clothtsk − t clTemp, (t Estimated) tsk − tcl˝ Estimated (Thickness(Thickness (Thickness˝ ˝ Visible ImageImageImageVisible ImageThermalImage SkinThermalFace Temp, ( tsk )(SkinAbdomenC) Temp, tskAbdomen − tcl clEstimated tsk − t(cl C)EstimatedUpper Icl (clo) (Thickness (Thickness(Thicknesstrt (°C)r ( C) t a (°C)tr (°C)ta ( C)tVisiblea (°C) ImageVisibleVisible ImageImageImage FaceImage (tsk) (°C) FaceAbdomen (tsk) (°C) Abdomen(°C)Abdomen˝ Upper Icl(°C) (clo) Upper Icl (clo) (mm))(mm)) tr (°C)(mm)) ta (°C)tr (°C) ta (°C) Image FaceImage (tsk) (°C) Face(t (clt)sk (°C)) (°C) (°C)((tC)cl) (°C)Upper Icl(°C) (clo) Upper Icl (clo) (mm)) tr (°C)(mm)) ta (°C)tr (°C) ta (°C) Image FaceImage (tsk) (°C) Face( t(cltsk) )(°C) (°C) (°C)(tcl) (°C)Upper (°C)Icl (clo) Upper Icl (clo) (mm)) (mm)) (tcl) (°C) (tcl) (°C)

TestTest 1: 1: 00 layer Test26.6 1: 0 layer26.7 26.6 26.7 34.4 34.430.6 30.6 Test 1: 0 layer TestTest26.626.6 1:1: ˘0 0 layer layer0.1 26.7 26.6 26.6 26.7 ˘ 0.126.726.7 34.434.4 ˘ 0.134.434.430.6 30.63.8˘30.630.6 0.1 0.603.8 3.80.60 0.60 layer (0(0 mm)mm) ±(0 0.1 mm) ± 0.1 ± 0.1 ± 0.1 ± 0.1 ±± 0.1 0.1 3.8± 0.1 0.603.83.8 0.600.60 (0 mm) (0±(0 0.1 mm)mm) ± 0.1 ± ± 0.10.1 ±± 0.10.1 ± 0.1 ±± ± 0.10.1 0.1 ±± 0.10.1

Test 2: 1 layer Test26.3 2: 1 layer26.5 26.3 26.5 35.0 35.030.2 30.2 TestTest 2: 2: 11 layer TestTest26.3 2:2: 1 1 layer layer 26.5 26.3 26.3 26.526.5 35.0 35.035.030.2 4.830.230.2 0.754.8 0.75 (3 mm) 26.3 ±(3 0.1 mm)˘ 0.1 ± 0.1 ± 26.5 0.1 ˘ 0.1± 0.1 ± 0.135.0 ˘ 0.1±± 0.1 0.1 30.24.8˘± 0.1 0.754.84.8 4.80.750.75 0.75 layer (3(3 mm) mm) (3±(3 0.1 mm)mm) ± 0.1 ± ± 0.10.1 ±± 0.10.1 ± 0.1 ±± ± 0.10.1 0.1 ±± 0.10.1

Test 3: 2 layer Test26.2 3: 2 layer26.3 26.2 26.3 34.6 34.629.5 29.5 TestTest 3: 3: 2 2 layer TestTest26.2 3:3: 2 2 layer layer 26.3 26.2 26.2 26.326.3 34.6 34.634.629.5 5.129.529.5 0.925.1 0.92 (6 mm) 26.2±(6 0.1 mm)˘ 0.1 ± 0.1± 0.1 26.3 ˘ 0.1± 0.1 ± 0.134.6 ˘ 0.1± ±0.1 0.1 29.55.1˘ ± 0.10.1 0.925.15.1 5.10.920.92 0.92 layer (6(6 mm) mm) ±(6(6 0.1 mm)mm) ± 0.1±± 0.10.1 ±± 0.10.1 ± 0.1 ±± 0.1±0.1 0.1 ±± 0.10.1

The experimental results show that the difference of face and clothing temperature (tsk − tcl) sk cl The experimentalThe experimental results show results that theshow difference that the ofdifference face and of clothing face and temperature clothing temperature (tsk − tcl) (tsk − tcl) The experimentalTheThe experimentalexperimental results show resultsresults that showtheshow difference thatthat thethe differenceofdifference face and ofof clothing faceface andand temperature clothingclothing temperaturetemperature (tsk − tcl) ((ttsk − − ttcl)) increases asincreases the thickness as the of thickness air layer ofincreases. air layer This increases. means This that meansthe air that layer the thickness air layer is thickness closely is closely increases asincreasesincreases the thickness asas thethe of thicknessthickness air layer ofof increases. airair layerlayer Thisincreases.increases. means ThisThis that means meansthe air thatthat layer thethe thickness airair layerlayer is thicknessthickness closely isis closelyclosely Thecorrelated experimental withcorrelated the temperature with results the temperature difference show that betweendifference the differencethe between face and the clothing. of face face and Thus, and clothing. the clothing estimation Thus, the temperature ofestimation of (tsk ´ tcl) correlated correlatedwithcorrelated the temperature withwith thethe temperaturetemperature difference betweendifferencedifference the betweenbetween face and thethe clothing. faceface andand Thus, clothing.clothing. the estimation Thus,Thus, thethe estimationestimationof ofof increasesthe clothing as thethe insulation thicknessclothing that insulation includes of air that layerthe includes effect increases. of the air effectlayer ofcan This air be layer made means can using be that made the temperature the using air the layer temperature of the thickness of the is closely the clothingthethe insulation clothingclothing thatinsulationinsulation includes thatthat the includesincludes effect of thethe air effecteffect layer of ofcan airair be layerlayer made cancan using bebe mademade the temperature usingusing thethe temperaturetemperature of the ofof thethe face and clothingface and as clothingin the proposed as in the algorithm.proposed algorithm.To show whether To show the whether proposed the algorithmproposed canalgorithm can correlatedface and with clothingfaceface the andand as temperature clothingclothing in the proposedasas inin the differencethe algorithm.proposedproposed betweenalgorithm.algorithm.To show whether ToTo the showshow face the whetherwhether andproposed clothing. thethe algorithmproposedproposed Thus, algorithmcanalgorithm the estimation cancan of reflect the effect of different air layer thicknesses, the estimated Icl values werecl inserted into the reflect the effectreflect ofthe different effect of air different layer thicknesses, air layer thicknesses, the estimated the Iestimatedcl values were Icl values inserted were into inserted the into the reflect the reflecteffectreflect ofthethe different effecteffect ofof air differentdifferent layer thicknesses, airair layerlayer thicknesses,thicknesses, the estimated thethe Iestimatedestimatedcl values were IIcl valuesvalues inserted werewere into insertedinserted the intointo thethe the clothingsimulation insulation model.simulation The model.simulated that includes The temperature simulated the temperature effectof the skin of airandof the layerclothing skin and can due clothing be to the made estimated due to using the clothing estimated the temperature clothing of the simulation simulationsimulationmodel. The model. model.simulated TheThe temperature simulatedsimulated temperaturetemperature of the skin ofandof thethe clothing skinskin andand due clothingclothing to the estimated duedue toto thethe clothing estimatedestimated clothingclothing face andinsulation clothing wereinsulation ascompared in were the with compared proposed the actual with algorithm. temperature the actual temperature measured To show by measured whether the IR camera. by the the IR proposed camera. algorithm can reflect insulation wereinsulationinsulation compared werewere with comparedcompared the actual withwith temperature thethe actualactual temperaturetemperature measured by measuredmeasured the IR camera. byby thethe IRIR camera.camera. the effect of different air layer thicknesses, the estimated Icl values were inserted into the simulation model. The simulated temperature of the skin and clothing due to the estimated clothing insulation were compared with the actual temperature measured by the IR camera. As Table 14 shows, the simulation output has less than 1.9% error relative to the actual temperature. Thus, it can be concluded that the proposed algorithm can reflect the effect of different air layer thickness in the calculation to estimate clothing insulation. The relationship between difference of temperature (tsk ´ tcl) and clothing insulation according to air layer thickness is judged by the correlation factor R2,R2 is 0.89 in linear regression model. It means that the difference of temperature (tsk ´ tcl) is related with clothing insulation. In the average skin (face) temperature, it was of little importance whether you wear glasses or not if the region of interest may be assigned as face’s area below glasses as in Table 13.

Table 14. Comparison of experiment and simulation for different air layer thicknesses.

Skin (Face) Temp Clothing (Chest) Temp Air Layer (Thickness (mm)) Actual Simulation Error Actual Simulation Error (˝C) (˝C) (%error) (˝C) (˝C) (%error) Test 1: Shirts and 0 air layer with 33.1 33.3 0.4 29.8 29.9 0.2 estimated Icl = 0.6 Test 2: Shirts and 1 air layer with 33.6 33.3 ´0.9 29.3 29.5 0.7 estimated Icl = 0.75 Test 3: Shirts and 2 air layers with 33.5 33.2 ´0.8 28.4 28.9 1.9 estimated Icl = 0.92

4. Conclusions In this paper, a novel algorithm for estimating the clothing insulation using an IR camera is proposed. The proposed algorithm can estimate clothing insulation values in real time from a relationship of the exposed skin temperature, and the clothing temperature, that are all directly Sensors 2016, 16, 341 15 of 16 measurable by an IR camera. To investigate whether the proposed estimation method is effective to measure clothing insulation accurately for different clothing conditions, experiments using three clothing ensembles on one subject independently were performed. Paired t-test was applied with a coefficient of significance α = 0.01 (99% confidence interval). We can conclude that spring/fall upper clothing insulation can be measured greater than summer upper clothing insulation using the proposed estimation method. Also, we can conclude that winter upper clothing insulation can be measured greater than spring/fall upper clothing insulation using the proposed estimation method, but lower clothing insulation is measured equally for three different ensembles as same lower clothing ensembles. Temperatures simulated with the estimated insulation value were closer to the actual temperature values than temperatures simulated with individual clothing insulation values without air layers between clothing. The upper clothing’s surface temperature was more accurate with a 3% error reduction and the lower clothing’s surface temperature was more accurate with a 3.7% ~ 6.2% error reduction. Therefore, it can be concluded that the proposed algorithm is more accurate than those using the previous measured individual garment insulation database. The estimation of the clothing insulation that includes the effect of air layer can be made using the temperature of the face and clothing as in the proposed algorithm. The simulation output has less than 1.9% error relative to the actual temperature. Thus, it can be concluded that the proposed algorithm can reflect the effect of different air layer thickness in the calculation to estimate clothing insulation. Thus, the proposed algorithm accurately reflects the effects of the different clothing to estimate the clothing insulation by real-time and non-contact measurements of face and clothing temperatures. Future work should focus on developing a specialized formula that reflects cabin air conditioning characteristics through a variety of vehicle tests. This could eventually lead to methods for assessing thermal comfort in vehicle cabins and applications in customized passenger comfort control.

Acknowledgments: The authors thank Hanon Systems for the financial support and the use of its equipment and EXA Corp. for the technical support. Author Contributions: Jeong-Hoon Lee derived the formula and performed the experiments. Young-Keun Kim checked the formula and analyzed the data together. Kyung-Soo Kim and Soohyun Kim provided guidance during the whole research and reviewed the manuscript. Conflicts of Interest: The authors declare no conflict of interest.

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