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MONITORING OF HEART RATE FROM PHOTOPLETHYSMOGRAPHIC SIGNALS USING A SAMSUNG GALAXY NOTE8 IN UNDERWATER ENVIRONMENTS

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Citation Askarian, B., Jung, K., & Chong, J. (2019). Monitoring of heart rate from photoplethysmography signals using a Samsung Galaxy Note8 in underwater environments, Sensors, 19, 2846. https://doi.org/10.3390/s19132846 Citable Link https://hdl.handle.net/2346/86925 Terms of Use CC-BY 4.0

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sensors

Article Monitoring of Heart Rate from Photoplethysmographic Signals Using a Samsung Galaxy Note8 in Underwater Environments

Behnam Askarian 1, Kwanghee Jung 2 and Jo Woon Chong 1,* 1 Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA 2 Educational Psychology and Leadership, Texas Tech University, Lubbock, TX 79409, USA * Correspondence: [email protected]; Tel.: +1-806-834-8392

Received: 27 March 2019; Accepted: 4 June 2019; Published: 26 June 2019

Abstract: Photoplethysmography (PPG) is a commonly used in determining heart rate and oxygen saturation (SpO2). However, PPG measurements and its accuracy are heavily affected by the measurement procedure and environmental factors such as light, temperature, and medium. In this paper, we analyzed the effects of different mediums (water vs. air) and temperature on the PPG signal quality and heart rate estimation. To evaluate the accuracy, we compared our measurement output with a gold-standard PPG device (NeXus-10 MKII). The experimental results show that the average PPG signal amplitude values of the underwater environment decreased considerably (22% decrease) compared to PPG signals of dry environments, and the heart rate measurement deviated 7% (5 beats per minute on average. The experimental results also show that the signal to noise ratio (SNR) and signal amplitude decrease as temperature decreases. Paired t-test which compares amplitude and heart rate values between the underwater and dry environments was performed and the test results show statistically significant differences for both amplitude and heart rate values (p < 0.05). Moreover, experimental results indicate that decreasing the temperature from 45 ◦C to 5 ◦C or changing the medium from air to water decreases PPG signal quality, (e.g., PPG signal amplitude decreases from 0.560 to 0.112). The heart rate is estimated within 5.06 bpm deviation at 18 ◦C in underwater environment, while estimation accuracy decreases as temperature goes down.

Keywords: photoplethysmography; PPG; heart rate variability; underwater; temperature

1. Introduction Cardiovascular diseases (CVDs) are considered one of the main reasons of deaths causing more than 30% of deaths around the world [1]. It is reported that 2200 out of 801,000 deaths in the U.S are caused by CVDs. Almost 47% of sudden CVD deaths occur outside the hospital, which indicates that most individuals may not manage warning signs of heart disease [2,3]. Accordingly, continuous heart rate monitoring is highly demanded to prevent the complications of CVDs. Recently, smartphone applications for healthcare, e.g., mobile heart disease detection, heart rhythm analysis, remote home care monitoring, eye disease diagnosis, have become highlighted [4–10]. Moreover, smartwatches, wrist bands, and activity trackers based on electrocardiography (ECG) or photoplethysmography (PPG) are also widely used for continuous measurement of physiological signs such as heart rate and peripheral oxygen saturation (SpO2) in daily life [11]. Among these technologies, PPG using a smartphone camera or smartwatches is highlighted as continuous and comfortable method for heart rate measurement since it does not require additional electrodes or skin preparation. Moreover, PPG measurement using smart devices was shown to be eﬀective in detecting arrhythmia [12–14]. PPG signals can reﬂects changes in blood volume below underlying body tissues in a simple, noninvasive, and low-cost manner [15]. PPG sensor technology facilitates the development of several

Sensors 2019, 19, 2846; doi:10.3390/s19132846 www.mdpi.com/journal/sensors Sensors 2019, 19, 2846 2 of 16 wearable devices such as smartwatches, Fitbits, and activity trackers for easy monitoring of heart rate in rest state, during daily activities, or exercises [13]. Since PPG measurement is sensitive to any disturbance and is affected by changes in the reflected light intensity induced by the variations in the volume of blood [13], motion and noise artifacts (MNA) caused by any movement or physical activity, displacement of the finger, finger pressure, and temperature can cause disturbance and discrepancy in PPG measurements, which yields to inaccurate heart rate measurements [16]. Therefore, there have been studies improving PPG signals acquired in the presence of MNA [17,18]. The effects of skin and room temperature on PPG signals have been studied in [19], which shows that cold temperature significantly reduces the amplitude of PPG signals and the accuracy of the SpO2 measurement while those are improved in warm temperature. Representative examples of constant heart rate monitoring during underwater activities are (1) heart rate monitoring of divers [20], and (2) heart rate monitoring of subjects during water walking or jogging, which is suggested for rehabilitation and fitness enhancement. Underwater electrodes have been proposed using carbon black powder (CB) and polydimethylsiloxane (PDMS) for underwater ECG measurements [21]. Cold water is observed to influence both heart rate and blood pressure in [22]. Changes in blood pressure of trained divers during cold water immersion was analyzed by asking the divers to hold their breath during the cold water submersion in [20]. The diver’s vital signs were analyzed using an ECG unit and the study focused on the biological changes in the body that effects the changes in blood pressure. The influence of cold water immersion on blood flow has been investigated [22] while The effects of underwater activities (water immersion, submersion, and scuba diving) on heart rate variability has been studied [23]. Comparison of surface electromyography (sEMG) electrodes for measuring muscle movement in water and land has been studied [24] and Electromyography (EMG) sensors measuring muscle movement in rehabilitation treatment in exercise pools were studied in [25,26]. However, these studies do not use PPG but ECG which requires additional electrodes. In this paper, we evaluate the performance of PPG sensors embedded in the Samsung Galaxy Note8 in dry and underwater environments. As performance metrics, we consider the accuracy of the heart measurements, signal amplitude, and signal to noise ratio (SNR). Specifically, we compared the acquired information from PPG signals to a gold-standard reference which is obtained from NeXus-10 MKII (Mind Media, Herten, Germany) device. The NeXus-10 MKII has a Food and Drug Administration (FDA) approval. The effect of cold temperature on PPG signals was studied in the viewpoints of (1) physiology and (2) sensor hardware. In the viewpoint of physiology, the effect of cold temperature on PPG signals was analyzed in terms of blood vessels in human body [27–29]. In this study, a temperature drop from 20 ◦C to 3 ◦C was observed to increase heart rate from 82 to 98 beats per minute (bpm) [28,29], and increase oxygen saturation level from 97% to 99%. Moreover, the temperature drop caused blood viscosity to increase, which decreased the PPG signal amplitude. On the other hand, the effect of cold temperature on PPG signal amplitude was studied in the viewpoint of sensor hardware including its photodiodes [19]. For example, temperature change from 25 ◦C to 4 ◦C shifted the wavelength by 18 nm, decreased the voltage by 0.1 volts, and decreased the current by 0.05 amps. However, cold temperature was observed not to change the pattern (or shape) of PPG signals in both physiology and sensor hardware viewpoints [30]. The rest of this paper is organized as follows. Section2 describes data collection, measurement devices, and study protocol. In Section3, our proposed image processing method consisting of signal extraction, data processing, feature extraction, and heart rate measurement is explained. Experimental results are presented in Section4. Finally, Section5 concludes this paper. Sensors 2019, 19, 2846 3 of 16

2. Materials

2.1.Sensors Measurement 2019, 19, x FOR Devices PEER REVIEW 3 of 16

InIn thisthis paper,paper, wewe usedused FLUKEFLUKE SPOTSPOT LightLight FunctionalFunctional testertester whichwhich generatesgenerates artiﬁcialartificial PPGPPG signalssignals forfor calibrationcalibration purpose,purpose, SamsungSamsung GalaxyGalaxy Note8Note8 forfor datadata acquisitionacquisition andand Nexus-10Nexus-10 MKIIMKII forfor gold-standardgold-standard reference reference which which are are shown shown in Figurein Figures1a–c respectively.1a–c respectively. Samsung Samsung Galaxy Galaxy Note8 isNote8 a water- is a andwater- dust-resistant and dust-resistant smartphone smartphone which has which a PPG has sensor a PPG on sensor the rear on side the for rear the side purpose for the of purpose measuring of heartmeasuring rate and heart SpO rate2 [31 and]. The SpO NeXuS-102 [31]. The MKII NeXuS-10 is equipped MKII withis equipped sensors measuringwith sensors physiological measuring signalsphysiological including signals electroencephalogram including electroencephalogram (EEG), ECG, PPG, (EEG), SpO ECG,2, respiration PPG, SpO signals,2, respiration and EMG signals, [32]. Whenand EMG measuring [32]. When PPG measuring signals, Samsung PPG signals, Galaxy Samsun Note8g usesGalaxy a reﬂection Note8 uses mode a reflection while the mode NeXus-10 while MKIIthe NeXus-10 device uses MKII a transmission device uses mode.a transmission Both devices mode use. Both a 645 devices nm red use light a 645 emitting nm red diode light (LED) emitting and adiode 940 nm (LED) infra-red and a LED.940 nm infra-red LED.

FigureFigure 1.1. OurOur measurement measurement and and calibration calibration devices devices used used for datafor data acquisition: acquisition (a) FLUKE : (a) FLUKE device device which iswhich an emulation is an emulation device todevice calibrate to ca otherlibrate measurement other measurement devices, (bdevices,) NeXus-10 (b) NeXus-10 MKII is FDA MKII approved is FDA physiologicalapproved physiological signal measurement signal measurement device which device we use wh asich our we golduse as standard our gold device standard in this device paper, in andthis (paper,c) Samsung and (c Galaxy) Samsung Note8, Galaxy a water-resistant Note8, a water-resistant device which devi is capablece which of measuringis capable of PPG measuring in both dry PPG and in underwaterboth dry and environments. underwater environments. 2.2. Data Acquisition 2.2. Data Acquisition We recruited 20 healthy volunteers following the Texas Tech University (TTU) Institutional Review We recruited 20 healthy volunteers following the Texas Tech University (TTU) Institutional Board (IRB) (IRB#: IRB2018-688). The ages of volunteers were ranged from 18 to 80 years old. The Review Board (IRB) (IRB#: IRB2018-688). The ages of volunteers were ranged from 18 to 80 years old. volunteers were asked to contact the index finger of the left hand on the NeXus-10 MKII device and The volunteers were asked to contact the index finger of the left hand on the NeXus-10 MKII device the index finger of the right hand on the Samsung Galaxy Note8 smartphone. This hand selection was and the index finger of the right hand on the Samsung Galaxy Note8 smartphone. This hand selection not randomized but the same for all volunteers. The volunteers were asked to sit in a relaxed position, was not randomized but the same for all volunteers. The volunteers were asked to sit in a relaxed breathe normally, and avoid any movement for the duration of data acquisition. The position of the position, breathe normally, and avoid any movement for the duration of data acquisition. The volunteer’s hand was in a stable and relaxed position on a table at the height of their heart. In this position of the volunteer’s hand was in a stable and relaxed position on a table at the height of their paper, we considered (1) underwater and (2) dry environments. The underwater and dry experiments heart. In this paper, we considered (1) underwater and (2) dry environments. The underwater and were performed serially without any interruption since heart rate can change due to stress, activity, and dry experiments were performed serially without any interruption since heart rate can change due even due to changes in the volunteer’s position. The calibration process was done on each volunteer to stress, activity, and even due to changes in the volunteer’s position. The calibration process was before each data acquisition, and we used SPOT light pulse oximeter analyzer [33] shown in Figure1a done on each volunteer before each data acquisition, and we used SPOT light pulse oximeter analyzer to generate artificial PPG signals. The calibration was performed to validate that smartphones and [33] shown in Figure 1a to generate artificial PPG signals. The calibration was performed to validate NeXus devices are working correctly before the actual measurement. The length of each measurement that smartphones and NeXus devices are working correctly before the actual measurement. The varied due to the time it takes for the smartphone application to get cleaner PPG signals, which is length of each measurement varied due to the time it takes for the smartphone application to get required for the Samsung Galaxy Note8 app’s heart rate estimation. cleaner PPG signals, which is required for the Samsung Galaxy Note8 app’s heart rate estimation. a. Dry environment In the dry environment, the volunteers were instructed to hold the smartphone with covering the Samsung’s PPG sensor by the index fingertip of their right hand while the NeXus-10 MKII was attached to the index fingertip of their left hand. The beginning and end time of the measurement

Sensors 2019, 19, x FOR PEER REVIEW 4 of 16 and the calculated heart rate by the smartphone built-in application was recorded for our analysis. The data acquisition setup for dry environment is shown in Figure 2a. b. Underwater environment In this environment, we performed two different experiments: (1) Underwater Samsung smartphone vs dry NeXus device at 18 °C fixed temperature, and (2) Samsung smartphones in underwater various temperatures. In the first experiment, to eliminate temperature factor and focus only on the medium factor, we compare dry and underwater (Samsung) PPG signal at a fixed temperature as shown in Figure 2. For underwater experiment, volunteers were asked to hold the smartphone in an emulated underwater condition while the alternate index finger with the NeXus PPG sensor was out of the water for the baseline measurement as shown in Figure 2b. The underwater condition is emulated by a transparent bucket of water containing clear water with the temperature maintained at the room temperature (18 °C) as shown in Figure 2b. The temperature of the water was set as the room temperature (18 °C) to eliminate the effect of temperature variations on the PPG Sensors 2019 19 measurement., , 2846 4 of 16 In the second experiment, we varied the temperature in three steps: warm, moderate, and cold. Specifically,a. Dry environment we considered 45 °C, 18 °C and 5 °C for warm, moderate, and cold temperature, respectively. The water was first heated up to 45 °C and the volunteer was asked to immerse their fingerIn in the the dry warm environment, water. We add the volunteersice to cool the were wa instructedter down. It to took hold around the smartphone 20 s for the with temperature covering ofthe water Samsung’s goes down PPG sensorby 1 °C. by We the first index measured ﬁngertip da ofta theirat 45 right°C for hand the warm while temperature the NeXus-10 and MKII put was ice intoattached the water. to the indexAfter ﬁngertipwater temperature of their left reaches hand. The18 °C, beginning the data and at were end time measured of the measurementfor the moderate and temperature.the calculated After heart water rate by temperature the smartphone reaches built-in 5 °C, applicationthe data were was measured recorded forat cold our analysis.temperature. The Thesedata acquisition tests were setupperformed for dry in environmenta serial way to is shownminimize in Figurethe changes2a. of a volunteer’s heart rate.

(a) (b)

Figure 2. Data acquisition in dry and underwater environments. ( a) Dry environment: the Samsung Galaxy Note8 PPG sensor is covered with the index finger finger of the righ rightt hand while the PPG sensor of thethe NeXus-10 NeXus-10 MKII MKII device device encloses encloses the the index index finger finger of of the the left left hand; hand; and and ( (bb)) underwater underwater environment: environment: thethe Samsung Galaxy Galaxy Note8 Note8 PPG PPG sensor sensor is is covered covered with with the the index index finger finger of of the right hand while the hand is immersed in the transparent bucket filled filled with water. The PPG sensor of the NeXus-10 MKII device encloses to the inde indexx finger finger of the left hand. b. Underwater environment 3. Method In this environment, we performed two different experiments: (1) Underwater Samsung 3.1.smartphone Data Processing vs dry NeXus device at 18 ◦C fixed temperature, and (2) Samsung smartphones in underwaterWe extracted various raw temperatures. PPG signals from In the the first PPG experiment, signal images to eliminateon the smartphone temperature screen factor by andour proposedfocus only image on the and medium signal factor,processing we compare algorithms. dry Fi andgure underwater 3 shows the (Samsung) flowchart PPGof our signal proposed at a fixeddata processingtemperature procedure as shown which in Figure consists2. For of underwater acquiring experiment,screenshot during volunteers the measurement, were asked to cropping hold the acquiredsmartphone images, in an converting emulated underwaterRGB (Red, Green, condition Blue while) image the to alternate HSV (Hue, index Saturation, finger with Value) the NeXus color space,PPG sensor obtaining was outa binary of the image water forafter the denoising baseline measurementbackground from as shown HSV inimages, Figure and2b. Thescaling underwater a binary condition is emulated by a transparent bucket of water containing clear water with the temperature maintained at the room temperature (18 ◦C) as shown in Figure2b. The temperature of the water was set as the room temperature (18 ◦C) to eliminate the effect of temperature variations on the PPG measurement. In the second experiment, we varied the temperature in three steps: warm, moderate, and cold. Specifically, we considered 45 ◦C, 18 ◦C and 5 ◦C for warm, moderate, and cold temperature, respectively. The water was first heated up to 45 ◦C and the volunteer was asked to immerse their finger in the warm water. We add ice to cool the water down. It took around 20 s for the temperature of water goes down by 1 ◦C. We first measured data at 45 ◦C for the warm temperature and put ice into the water. After water temperature reaches 18 ◦C, the data at were measured for the moderate temperature. After water temperature reaches 5 ◦C, the data were measured at cold temperature. These tests were performed in a serial way to minimize the changes of a volunteer’s heart rate. Sensors 2019, 19, 2846 5 of 16

3. Method

3.1. Data Processing We extracted raw PPG signals from the PPG signal images on the smartphone screen by our proposed image and signal processing algorithms. Figure3 shows the ﬂowchart of our proposed data processing procedure which consists of acquiring screenshot during the measurement, cropping acquiredSensors 2019 images,, 19, x FOR converting PEER REVIEW RGB (Red, Green, Blue) image to HSV (Hue, Saturation, Value)5 colorof 16 space, obtaining a binary image after denoising background from HSV images, and scaling a binary image toto valuesvalues in in x-y x-y axis. axis. We We implement implement this this data data processing processing procedure procedure using using MATLAB MATLAB 2017b 2017b (from MathWorks)(from MathWorks) [34]. [34].

Figure 3.3. TheThe flowchart ﬂowchart of our proposed data processingprocessing procedure which consistsconsists of acquiringacquiring screenshot during the measurement, cropping acquired images, converting RGB image to HSV color space, obtaining a binary image after denoising background from HSV images,images, and scaling a binary image to valuesvalues inin x-yx-y axis.axis. The heart rate is extrac extractedted from peak-to-peak (or pulse) interval from the constructed PPGPPG signal (left). The acquired smartphone PPG signal is compared to gold-standard PPG signal acquired byby NeXus-10NeXus-10 MKII (right).(right).

First, the PPG signal images on the smartphone screen were acquired by the built-in application for thethe PPGPPG sensorsensor embeddedembedded inin GalaxyGalaxy Note8Note8 smartphone.smartphone. During the time of measurement,measurement, the PPG signal was shown on the screen of the smartphone and the ﬁnalfinal output of the application is estimated heart rate value. To evaluate the quality of the raw PPG signals obtained by Galaxy Note8, we needed to extract the raw PPG signal from thethe PPG signal images on the smartphone screen. We used the Mobizen screen recording application whichwhich provides full high-definition high-deﬁnition (HD) smartphone screen recording recording at at 60 60 frames frames per per second second (fps) (fps) [35] [.35 PPG]. PPGsignal signal extraction extraction from the from smartphone the smartphone started startedwith image with cropping image cropping from recorded from recorded videos videos of smartphone of smartphone screen screen by this by Mobizen this Mobizen application. application. We Wethen then applied applied thethe stitching stitching technique technique to toalign align co consecutivensecutive screenshots screenshots after after the the screenshots were acquired [[36,37].36,37]. After obtaining a stitched RGB PPG image, we convert this RBG image into HSV color space image. Then, Then, noise noise and and background background were were reduced reduced in in the the HSV HSV images images by by the the color color mask mask technique technique of ofour our proposed proposed method method [38]. [38 Finally,]. Finally, the thedenoised denoised image image was wasconverted converted to a binary to a binary image image which which itself itselfis the israw the PPG raw signal PPG signal that we that aimed we aimed to obtain. to obtain. For the For heart the rate heart calculation rate calculation from the from raw the PPG raw signal, PPG signal,we use wethe usemaximum the maximum amplitude amplitude value at value each pulse at each (or pulse heartbeat) (or heartbeat) of the raw of thePPG raw signal. PPG The signal. detailed The detailed procedure converting from RGB images to heart rate (RGB HSV color masking binary procedure converting from RGB images to heart rate (RGB  HSV→  color→ masking  binary→  scaling heartbeat calculation) is described in Section 3.2. →scaling  →heartbeat calculation) is described in Section 3.2.

3.2. PPG Signal Extraction from RGB Images Figure 4a shows an example of a smartphone screenshot whose size is 1080 × 2220 pixels. First, we cropped the image to extract only the region in which the signal exists (see Figure 4b). Next, the image of the signal in the RGB color space was converted to the HSV color space using the HSV color conversion method. The reason why we used the HSV color space was that HSV color space is shown to be effective color space in detecting signals from bright background [38]. By defining the thresholds for each of the channels of the image based on the histograms of the S and V components, the binary image of the signal was extracted from its background. The reason why we chose S and V component

Sensors 2019, 19, 2846 6 of 16

3.2. PPG Signal Extraction from RGB Images Figure4a shows an example of a smartphone screenshot whose size is 1080 2220 pixels. First, × we cropped the image to extract only the region in which the signal exists (see Figure4b). Next, the image of the signal in the RGB color space was converted to the HSV color space using the HSV color conversion method. The reason why we used the HSV color space was that HSV color space is shown to be effective color space in detecting signals from bright background [38]. By defining the thresholds for each of the channels of the image based on the histograms of the S and V components, the binary image of the signal was extracted from its background. The reason why we chose S and V component was that S and V components give larger difference values between signal and background compared to H component. For S and V components, we set the thresholds to be 0.6 and 0.7, respectively. If V values are lower than these thresholds, then it is mapped into black. Otherwise, it is mapped into white. The points in the binary image of the signal, which are shown in Figure4c, were then scaled to the x-y axis to construct PPG signals in time domain as shown in Figure4d. The constructed PPG signals from the screenshots were stitched together, and finally, the heart rate is extracted from the stitched PPG signal (see Section 3.3). Sensors 2019, 19, x FOR PEER REVIEW 7 of 16

Figure 4. Figure 4. An An exampleexample ofof output result at at each each step step of of our our proposed proposed data data processing processing procedure. procedure. (a) (a) Screenshot from the smartphone built-in application, (b) its cropped image, (c) the binary image Screenshot from the smartphone built-in application, (b) its cropped image, (c) the binary image derived by the RGB to HSV transformation and background removal, and (d) extracted signal by derived by the RGB to HSV transformation and background removal, and (d) extracted signal by scaling the binary image to the x-y axis. scaling the binary image to the x-y axis. Speciﬁcally, the HSV color conversion method is described in Equations (1) to (5). First, the RGB 3.3. Signal quality index (SQI) and heart rate calculation and from the PPG signal values are normalized using the following equation: To evaluate the effect of underwater conditions on heart rate measurement and compare the results with those from the dryR0 environment,= R/255, G0 we= Gderived/255, BSQI0 = asB/ well255. as heart rate from PPG signals. (1) th As SQI, we consider signal amplitude and SNR [39]. Signal amplitude 𝐴, at the n pulse is calculated by 𝐴, =𝑆, − 𝑆, where 𝑆, and 𝑆, denote sampled values of peak and trough at the nth pulse. SNR compares the signal level to its background noise and is calculated by:

SNR = 20log , (6) where 𝐴 is signal amplitude and 𝐴 is noise amplitude. In cold temperatures, peripheral blood flow reduces due to vasoconstriction of the blood vessels, which leads to a smaller PPG signal amplitude [28]. According to Snell’s Law, on the other hand, any medium change alters the light speed causing a deviation in the transmitted and received light beam [40]. Snell’s Law declares that the speed of the light traveling from one medium (air) to another medium (water) will change based on the following equation which results in changes in the light direction:

= = , (8) where 𝜃 is the angle of incidence in air environment, 𝜃 is the angle of refraction in the water environment, 𝜗 is the speed of light in dry environment, and 𝜗 is the speed of light in the underwater environment as shown in Figure 5. As a result, deviation in the light beam of the PPG

Sensors 2019, 19, 2846 7 of 16

where R0, G0, and B0 are the normalized values of the RGB color space and each of the values is in the range of 0 to 1. The maximum (Cmax) and minimum (Cmin) values of the normalized RGB channels (R0G0B0) and the diﬀerence ∆ between the maximum and the minimum values of the normalized RGB channels are calculated as follows:

Cmax = max(R0, G0, B0), C = min(R0, G0, B0), ∆ = Cmax C . (2) min − min The Hue (H) component of the HSV color space image is calculated from the following equation:   0 , if ∆ = 0  G B  60 ( 0− 0 ) , if Cmax = R , H =  ◦ × ∆ 0 (3)  ( B0 R0 + ) =  60◦ −∆ 2 , if Cmax G0,  × R G  60 ( 0− 0 + 4), if Cmax = B . ◦ × ∆ 0 The saturation (S) component of the HSV color space image is calculated by implementing Equation (4): ( 0 , Cmax = 0, S = (4) ∆ , C 0. Cmax max ,

Finally, the value (V) component of the HSV color space image is derived from the following equation:

V = Cmax. (5)

3.3. Signal Quality Index (SQI) and Heart Rate Calculation and from the PPG Signal To evaluate the eﬀect of underwater conditions on heart rate measurement and compare the results with those from the dry environment, we derived SQI as well as heart rate from PPG signals. th As SQI, we consider signal amplitude and SNR [39]. Signal amplitude Asignal, n at the n pulse is calculated by A = S S where S and S denote sampled values of peak signal,n peak,n − trough,n peak, n trough,n and trough at the nth pulse. SNR compares the signal level to its background noise and is calculated by: ! Asignal SNRdb = 20 log , (6) Anoise where Asignal is signal amplitude and Anoise is noise amplitude. In cold temperatures, peripheral blood ﬂow reduces due to vasoconstriction of the blood vessels, which leads to a smaller PPG signal amplitude [28]. According to Snell’s Law, on the other hand, any medium change alters the light speed causing a deviation in the transmitted and received light beam [40]. Snell’s Law declares that the speed of the light traveling from one medium (air) to another medium (water) will change based on the following equation which results in changes in the light direction: sin θ ϑ n 1 = 1 = 1 , (7) sin θ2 ϑ2 n2 where θ1 is the angle of incidence in air environment, θ2 is the angle of refraction in the water environment, ϑ1 is the speed of light in dry environment, and ϑ2 is the speed of light in the underwater environment as shown in Figure5. As a result, deviation in the light beam of the PPG sensor makes less light enter the PPG photodetector sensor, which results in lower signal amplitude. Moreover, the water absorbs some of the light, which may also decrease the amount of light entering the photodetector. Sensors 2019, 19, x FOR PEER REVIEW 8 of 16 Sensors 2019, 19, x FOR PEER REVIEW 8 of 16 sensor makes less light enter the PPG photodetector sensor, which results in lower signal amplitude. sensorMoreover, makes the less water light absorbs enter thesome PPG of thephotodetector light, whic hsensor, may also which decrease results the in amountlower signal of light amplitude. entering Moreover,the photodetector. the water absorbs some of the light, which may also decrease the amount of light entering the photodetector. Sensors 2019, 19, 2846 8 of 16

Figure 5. Refraction effect between water and air with the smartphone configuration with an LED Figure 5. Refraction eﬀect between water and air with the smartphone conﬁguration with an LED Figureemitter 5.and Refraction the photodiode effect between (PD) receiver water sensor. and air with the smartphone configuration with an LED emitteremitter and and the the photodiode photodiode (PD) receiver sensor. Heart rate is calculated by counting the number of systolic peaks per minute. Figure 6 shows the Heart rate is calculated by counting the number of systolic peaks per minute. Figure6 shows the systolicHeart peaks rate and is calculated peak-to-peak by counting interval the of anumber PPG signal of systolic measured peaks by per the minute. NeXus-10 Figure MKII 6 shows device the in systolic peaks and peak-to-peak interval of a PPG signal measured by the NeXus-10 MKII device in the systolicthe dry environment.peaks and peak-to-peak interval of a PPG signal measured by the NeXus-10 MKII device in thedry dry environment. environment. Amplitude (au) Amplitude (au)

Figure 6. SystolicSystolic peaks peaks and and peak-to-peak peak-to-peak intervals intervals of of a a PPG signal measured by the NeXus-10 MKII Figuredevice signal6. Systolic in the peaks dry environment. and peak-to-peak intervals of a PPG signal measured by the NeXus-10 MKII device signal in the dry environment. 4. Results Results 4. Results The PPG signalssignals extractedextracted from from the the smartphone smartphone and and the the NeXus NeXus device device for eachfor each environment environment were wereanalyzedThe analyzed PPG in terms signals in ofterms signal extracted of quality signal from (signalquality the amplitudesmartphone (signal amplitude and and SNR). the and ToNeXus eliminate SNR). device To the eliminatefor eff ecteach of environment temperaturesthe effect of weretemperaturesand only analyzed focus and onin mediumonlyterms focus of e ffsignal ect,on medium we quality fixed theeffect, (signal temperature we amplitude fixed the at 18 temperature and◦C. TheSNR). experimental atTo 18 eliminate °C. The environment experimentalthe effect was of temperaturesan indoor environment and only focus with on regular medium ambient effect, light we conditionfixed the temperature and air-conditioned at 18 °C. roomThe experimental temperature

maintained at 18 ◦C. PPG signal examples acquired by the smartphone in the dry and underwater environments are shown in Figure7a,b, respectively while a reference PPG signal example acquired by the NeXus device is shown in Figure7c. The reference PPG signal is always measured in dry Sensors 2019, 19, x FOR PEER REVIEW 9 of 16

environment was an indoor environment with regular ambient light condition and air-conditioned room temperature maintained at 18 °C. PPG signal examples acquired by the smartphone in the dry Sensors 2019, 19, 2846 9 of 16 and underwater environments are shown in Figures 7a,b, respectively while a reference PPG signal example acquired by the NeXus device is shown in Figure 7c. The reference PPG signal is always environmentmeasured in for dry both environment dry and underwater for both dry environments and underwater since environments it is required since to beit is gold-standard. required to be As showngold-standard. in Figure7 Asa,b, shown the wet in environmentFigures 7a,b, changedthe wet environment smartphone changed PPG signals smartphone in terms PPG of shape signals as wellin asterms peak-to-peak of shape intervalas well as (or peak-to-peak heart rate). interval (or heart rate).

(a)

(b)

(c)

Figure 7. Representative PPG signal examples measured in dry and underwater environments. (a) PPG signals measured in dry environment and (b) in underwater environment using Samsung Galaxy Note8, and (c) a reference PPG signal measured using NeXus-10 MKII. Sensors 2019, 19, 2846 10 of 16

The smartphone application has artifact correction algorithm that operates before giving an estimation of the heart rate [31]. Whenever the signal is aﬀected by any type of artifact, the measurement time increases until the application gets the usable signal. Therefore, the moment when the smartphone shows the ﬁnal estimated heart rate value is 10 s in the dry environment but it increases to 20 s in ≈ ≈ the underwater environment as shown in Table1.

Table 1. Mean and standard deviation for the measurement duration in dry and underwater environments.

Dry Environment Underwater Environment Mean STD Mean STD ± ± Time (s) 10 2.7 20 3.4 ± ±

Table2 shows signal amplitude, SNR, and heart rate values derived from the PPG signals of each volunteer in dry and underwater environments. As shown in Table2, average SNR decreases from 12.84 into 4.96 on average and signal amplitude decreases from 0.4 to 0.2 on average. Moreover, heart rate acquired by the smartphone in the dry environment is shown to have 0.05 bpm diﬀerence on average compared to the gold-standard while the average diﬀerence is 4.8 bpm in underwater environment.

Table 2. Heart rate estimation, amplitude and SNR values from the NeXus-10 MKII device and Samsung

Galaxy Note8 for dry and underwater environments at baseline temperature (18 ◦C).

Dry Environment Underwater Environment NeXus NeXus Smartphone Samsung Volunteer Gender Heart Amplitude SNR Heart Amplitude SNR Heart Rate Heart Rate & Age Rate Value (au) (dB) Rate Value (au) (dB) (bpm) (bpm) (bpm) (bpm) 1 F 30 83 84 0.358 12.5 94 100 0.238 4.3 2 M 32 76 76 0.356 12.9 77 84 0.204 4.2 3 M 23 72 72 0.352 12.1 70 74 0.189 4.1 4 F 37 95 94 0.322 11.8 88 92 0.184 5.2 5 M 26 83 83 0.428 12.8 92 85 0.236 5.5 6 M 29 71 72 0.383 12.6 68 73 0.241 5.3 7 F 45 64 64 0.386 11.7 70 66 0.226 5.6 8 M 27 77 78 0.327 12.1 70 73 0.177 4.2 9 M 33 76 76 0.405 13.2 75 71 0.238 4.4 10 M 26 63 63 0.323 13.1 64 68 0.178 4.2 11 M 38 80 81 0.392 12.8 77 77 0.192 4.4 12 M 25 63 63 0.391 13.8 69 65 0.187 4.6 13 M 27 73 74 0.411 11.4 74 68 0.164 4.3 14 F 72 92 92 0.315 14.1 96 91 0.168 4.8 15 F 19 76 76 0.364 14.3 78 84 0.192 5.5 16 F 21 65 65 0.292 14.5 63 69 0.115 5.1 17 F 23 75 76 0.341 13.2 76 72 0.205 4.2 18 F 22 74 74 0.313 12.9 72 80 0.215 4.4 19 M 20 68 69 0.373 13.2 70 77 0.178 4.2 20 M 19 86 86 0.364 13.1 85 94 0.113 4.5 12.8 Mean STD 75.9 9 75.95 8.9 0.39 0.14 75.4 9 80.2 10.2 0.2 0.1 4.9 0.7 ± ± ± ± 1.4 ± ± ± ± ±

Comparison of measurements of the two environments from the smartphone with the NeXus as a reference device was performed using the two-way repeated measures ANOVA using SPSS (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp). The results revealed that there was a significant interaction effect between Device and Environment (F = 69.6, p < 0.05). The interaction effect indicates that the heart rates from smartphone (M = 80.52) were significantly higher than those from NeXus (M = 76.39) in the underwater environment (Mean Difference = 4.13, SE = 1.84, p < 0.05) while the heart rates from the smartphone (M = 76.35) and NeXus (M = 75.70) were not statistically different in the dry environment (Mean Difference = 0.652, SE = 0.59, p = 0.29). Sensors 2019, 19, x FOR PEER REVIEW 11 of 16 Sensors 2019, 19, x FOR PEER REVIEW 11 of 16 69.6, p < 0.05). The interaction effect indicates that the heart rates from smartphone (M = 80.52) were 69.6,significantly p < 0.05). higherThe interaction than those effect from indicates NeXus that (M the = 76.39)heart ratesin the from underwater smartphone environment (M = 80.52) (Meanwere significantlyDifference = higher4.13, SE than = 1.84, those p < 0.05) from while NeXus the heart(M = rates76.39) from in the smartphoneunderwater (Menvironment = 76.35) and (Mean NeXus SensorsDifference(M = 201975.70), 19 = ,were4.13, 2846 SEnot = statistically 1.84, p < 0.05) different while the in theheart dry rates environment from the smartphone (Mean Difference (M = 76.35) = 0.652, and SE NeXus11 = of0.59, 16 (Mp = = 0.29). 75.70) were not statistically different in the dry environment (Mean Difference = 0.652, SE = 0.59, p = 0.29).Figure 8 shows the plot of each measurement’s amplitude for all participants between the dry andFigure underwater8 8 shows shows the environments.the plot plot of of each each measurement’s Ameasurement’s paired-samples amplitude amplitude t-test for allforwas participantsall conductedparticipants between tobetween compare the dry the and drythe underwaterandmeasurement’s underwater environments. amplitude environments. Aof paired-samplesheart A ratespaired-samples in thet-test dry was andt conducted-test underwater was toconducted compare environments. theto measurement’scompare There wasthe a amplitudemeasurement’ssignificant of difference heart amplitude rates in in amplitude the of dryheart and forrates underwater the in dry the envi dry environments.ronment and underwater (M = There 0.37, SD wasenvironments. = a0.04) significant and the There di underwaterfference was ina amplitudesignificantenvironment fordifference the(M dry= 0.18, in environment amplitude SD = 0.04); for (Mt (22) the= 0.37, = dry 17.9, SDenvi p=

Figure 8. Amplitude plot: left plot shows the amplitude of the dry environment from NeXus-10 MKII Figureand right 8. Amplitude plot shows plot: the leftamplitude left plot plot shows shows of underwat the the amplitud amplitudeer environmente of the dry fromenvironment Samsung from Galaxy NeXus-10 Note8. MKII and right plotplot showsshows thethe amplitudeamplitude ofof underwaterunderwater environmentenvironment fromfrom SamsungSamsung GalaxyGalaxy Note8.Note8. The Bland–Altman plots in Figure 9 show the agreement between the heart rates obtained from The Bland–Altman plots in Figure9 show the agreement between the heart rates obtained from the Thesmartphone Bland–Altman and NeXus plots inin dryFigure and 9 underwatershow the ag reementconditions. between Figure the 9a heartshows rates a bias obtained between from the the smartphone and NeXus in dry and underwater conditions. Figure9a shows a bias between the themean smartphone differences and of NeXus0.65 in heartin dry rates and ofunderwater smartphone conditions. and NeXus Figure in dry 9a condition shows a withbias between95% limits the of mean differences of 0.65 in heart rates of smartphone and NeXus in dry condition with 95% limits of meanagreement differences interval of 0.65of the in meanheart ratesdifferences. of smartphone On the otherand NeXus hand, inFigure dry condition9b shows witha bias 95% between limits theof agreement interval of the mean differences. On the other hand, Figure9b shows a bias between the agreementmean difference interval of of 4.13 the from mean smartphone differences. in On underwater the other hand, and NeXus Figure in 9b dry shows condition, a bias betweenas well as the an mean difference of 4.13 from smartphone in underwater and NeXus in dry condition, as well as an meanagreement difference interval of 4.13 with from 95% smartphone of the mean in differences. underwater This and implies NeXus that in dry the condition,bias between as wellheart as rates an agreement interval with 95% of the mean differences. This implies that the bias between heart rates agreementwith smartphone interval and with NeXus 95% of are the larger mean in differences. underwater This condition implies than that in the dry bias condition. between heart rates withwith smartphonesmartphone andand NeXusNeXus areare largerlarger inin underwaterunderwater conditioncondition thanthan inin drydry condition.condition.

(a) (a) Figure 9. Cont.

Sensors 2019, 19, x FOR PEER REVIEW 12 of 16

SensorsSensors2019 2019, 19, 19, 2846, x FOR PEER REVIEW 1212 of of 16 16

(b)

Figure 9. Bland–Altman plots of heart rate measurements(b) after signal processing. (a) Heart rate from Samsung Galaxy Note8 and NeXus-10 MKII in dry environment, and (b) Heart rate from NeXus-10 FigureFigure 9. 9.Bland–Altman Bland–Altman plots plots of of heart heart rate rate measurements measurements after after signal signal processing. processing. (a ()a Heart) Heart rate rate from from MKII in dry and Samsung Galaxy Note8 in wet environment. SamsungSamsung Galaxy Galaxy Note8 Note8 and and NeXus-10 NeXus-10 MKII MKII in in dry dry environment, environment, and and (b ()b Heart) Heart rate rate from from NeXus-10 NeXus-10 MKIIMKII in in dry dry and and Samsung Samsung Galaxy Galaxy Note8 Note8 in in wet wet environment. environment. To evaluate the effect of temperature on the PPG signal measurement in underwater environment,ToTo evaluate evaluate we the measured etheﬀect effect of temperaturesignal of temperatureamplitude on the and PPGon heart signalthe ratePPG measurement accuracy signal atmeasurement 45 in underwater°C, 18 °C, andin environment, underwater5 °C. Here, the heart rate accuracy is calculated by Accuracy (%) = 100% − PE where PE is the percentage of error weenvironment, measured signal we measured amplitude signal and amplitude heart rate and accuracy heart rate at 45 accuracy◦C, 18 ◦ atC, 45 and °C, 5 18◦C. °C, Here, and 5 the °C. heart Here, rateand accuracyis derived is by calculated 𝑃𝐸 = by|𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑Accuracy (%) = 100% 𝑣𝑎𝑙𝑢𝑒PE where − 𝑎𝑐𝑡𝑢𝑎𝑙PE is the percentage 𝑣𝑎𝑙𝑢𝑒|/𝑎𝑐𝑡𝑢𝑎𝑙 𝑣𝑎𝑙𝑢𝑒×100%. Figure of 10 error shows and the is the heart rate accuracy is calculated by Accuracy (%)− = 100% − PE where PE is the percentage of error derivedamplitude by PEand= heartmeasured𝑃𝐸 rate = value |𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑accuracyactual of value the /measuredactual 𝑣𝑎𝑙𝑢𝑒 value PPG 100%− 𝑎𝑐𝑡𝑢𝑎𝑙signal. Figure for 10varying shows 𝑣𝑎𝑙𝑢𝑒|/𝑎𝑐𝑡𝑢𝑎𝑙 𝑣𝑎𝑙𝑢𝑒×100% temperature the amplitude in and is derived by| − | × . Figure 10 shows the andunderwateramplitude heart rate and accuracy dryheart environments. ofrate the accuracy measured The of PPG signalthe signal measured amplitude for varying PPG in the temperaturesignal underwater for varying in environment underwater temperature and was dry onin environments.averageunderwater 32%, and18%, The dry signaland environments. 14% amplitude lower at in 45The the °C, signal underwater 18 °C, am andplitude 5 environment °C, inrespectively, the underwater was oncompared average environment those 32%, in 18%, thewas and dry on environment. The accuracy in the underwater environment was on average 5%, 18%, and 14% lower 14%average lower 32%, at 45 18%,◦C, 18 and◦C, 14% and lower 5 ◦C, respectively, at 45 °C, 18 compared°C, and 5 those°C, respectively, in the dry environment. compared those The in accuracy the dry at 45 °C, 18 °C, and 5 °C, respectively, compared those in the dry environment. inenvironment. the underwater The environmentaccuracy in the was underwater on average en 5%,vironment 18%, and was 14% on loweraverage at 5%, 45 ◦ 18%,C, 18 and◦C, and14% 5lower◦C, respectively, at 45 °C, 18 compared°C, and 5 °C, those respectively, in the dry environment.compared those in the dry environment.

(a)

Figure 10.(a)Cont .

Sensors 2019, 19, 2846 13 of 16 Sensors 2019, 19, x FOR PEER REVIEW 13 of 16

(b)

Figure 10.10. SignalSignal amplitude amplitude and and heart heart rate rate accuracy accuracy acquired acquired from smartphonefrom smartphone PPG signals PPG forsignals varying for varyingtemperature temperature in dry (red) in dry and (red) underwater and underwater (blue) environments.(blue) environments. (a) signal (a) signal amplitude, amplitude, and (b and) heart (b) heartrate accuracy. rate accuracy.

5. Discussion Discussion There have been studies investigating the effect effect of didifferentfferent conditions and factors including motion noise artifact, skin tone, nail polishes, polishes, age, age, and and ambient ambient light light on on PPG PPG signal signal amplitude amplitude [8–11]. [8–11]. For heartheart raterate measurements measurements in in underwater underwater condition, condition, several several approaches approaches have beenhave proposedbeen proposed [10,11]. [10,11].Schipke Schipke et al. found et al. that found 5 min that immersions 5 min immersions for divers for in divers 6 ◦C cold in 6 ocean°C cold water ocean resulted water inresulted 10% decrease in 10% in SpO measurement accuracy and 40% decrease in heart rate measurement accuracy. Reyes et al. decrease2 in SpO2 measurement accuracy and 40% decrease in heart rate measurement accuracy. Reyesdeveloped et al. adeveloped compound a ofcompound carbon black of carbon powder black and powder polydimethylsiloxane and polydimethylsiloxane (CB/PDMS) (CB/PDMS) and tested andtheir tested electrode their for electrode underwater for ECGunderwater measurements ECG measurements which had an which average had of 6an bpm average deviation of 6 inbpm the deviationheart rate measurementin the heart rate while measurement in our underwater while in PPG our measurement underwater PPG the average measurement heart rate the deviation average heartwas 5 rate bpm. deviation Their methods was 5 bpm. use ECG Their electrodes methods foruse heart ECG rateelectrodes measurements, for heart andrate itmeasurements, remains to be seenand itif theirremains accuracy to be seen remains if their in salty accuracy and non-saltyremains in water salty conditions. and non-salty Moreover, water conditions. these studies Moreover, provide thesegood estimatesstudies provide of heart good rate measurementsestimates of heart in cold rate water measurements while the e ffinect cold of highwater temperatures while the effect (higher of highthan temperatures 6 ◦C) on heart (higher rate measurement than 6 °C) on was heart not rate studied measurement in [21,23]. was not studied in [21,23]. Khan et al. studied studied the the effects effects of cold and warm temperatures (5 ◦°CC and 45 ◦°CC)) on PPG signal amplitude and heart rate measurements and concluded that temperature drop drop from 45 °C◦C to 5 ◦°CC decreases the PPG signal from 0.4 V to 0.1 V [12]. [12]. In In particular, particular, our our experiment experiment found that decreasing the water temperature decreases the performance. Moreover, Moreover, Khan’s study was only conducted in dry conditions and they did not study the effect effect of different different mediums (e.g., water) on the PPG signal amplitude. Our Our results results indicate indicate that that changing changing th thee medium medium from from air air to to water and decreasing the temperature from from 45 45 °C◦C to 5 °C◦C decreased the the signal signal amplitude amplitude from from 0.561 0.561 to to 0.091. 0.091. To To the the authors’ authors’ knowledge, no no research has studied the effects effects of different different mediums (e.g., water) on PPG sensors signals amplitude and SNR. As it was expected, our results indicate that there was a significantsignificant interaction effect effect between device and environment (F = 69.6,69.6, p < 0.05).0.05). The The interaction interaction effect effect indicates indicates that that the the heart heart rates from dry environment environment (M (M == 80.52)80.52) were were significantly significantly higher higher than than those those from from underwater underwater environment environment (M =(M 76.39)= 76.39) (mean (mean difference difference = 4.13,= 4.13, SE = SE 1.84,= 1.84, p < 0.05).p < 0.05). Our experimental Our experimental result result indicates indicates that PPG that measurementPPG measurement gives gives acceptable acceptable accuracy accuracy in normal in normal water water pressure, pressure, normal normal light light condition condition and temperature maintained maintained at at 18 18 °C.◦C. When the temperat temperatureure decreases, however, however, a noise resilient peak detection is needed to acquire reliable heart rate information whilewhile itit isis notnot inin thethe drydry environment.environment.

6. Conclusions

Sensors 2019, 19, 2846 14 of 16

6. Conclusions In this study, we investigated the effects of underwater and dry environments on signal amplitude and SNR values of PPG signals. Experimental results showed that underwater environment impacts the signal amplitude significantly (p < 0.05), decreases the average amplitude value by 22% and the average accuracy of the heart rate PPG measurement decreases by 7% on average. The underwater temperature also impacts the heart rate measurement. Decreasing the underwater temperature from 45 ◦C to 5 ◦C, increases the difference between the accuracy of underwater and dry environment from 5% to 14% while acceptable accuracy of heart rate estimate provided by FDA approved-device is 10% of value, not to exceed 5 bpm [41]. Experimental results show that heart rate can be acquired in ± underwater environment with 80% accuracy on average compared to dry environment. Moreover, in ≈ underwater condition, experimental results show that temperature significantly affects the accuracy of heart rate estimation as well as signal amplitude. As the temperature decreases from 45 ◦C to 5 ◦C, the signal amplitude is shown to decrease by 12 dB. The average temperature of the ocean surface in Texas coasts (Gulf of Mexico) is about 17 ◦C and it gets as low as 4 ◦C in January and as high as 24 ◦C in August according to the national center for environmental information (NOAA) [42]. According to these information and our experimental results, for example, the heart rate could be measured with an average accuracy of 82% in August (at 24 ◦C) and 60% in January (at 4 ◦C) in sea water at Texas coasts (Gulf of Mexico), and 84% average accuracy in swimming pools (water temperature around 26 ◦C).

Author Contributions: B.A. conceived, designed, and conducted the experiment; gathered and analyzed data; validated the results; conducted literature review; wrote original and revised manuscript; visualized data; and conducted most details of the work. K.J. contributed to empirical analyses and manuscript writing. J.W.C. conceived, designed, and guided all experiments/testing/analysis; analyzed data and results; wrote original and revised manuscript; guided direction and all details of the work. Funding: This work was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award R15 HL121761. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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