Comparing Kinect2 Based Balance Measurement Software to Wii Balance Board
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Preprint: Comparing Kinect2 based Balance Measurement Software to Wii Balance Board
Zhihan Lv, Vicente Penades, Sonia Blasco, Javier Chirivella, Pablo Gagliardo FIVAN, Valencia, Spain [email protected]
ABSTRACT This is the preprint version of our paper on REHAB2015. A In robotics research community, a series of researches have balance measurement software based on Kinect2 sensor is been already done about comparison kinect balance evaluated by comparing to Wii balance board in numerical measurement (KBM) and Vicon balance measurement analysis level, and further improved according to the (VBM) to wii blance board (WBB) [17] [18] [19]. In the consideration of BFP (Body fat percentage) values of the experiment, the WBB was placed on top of the FP to obtain user. Several person with different body types are involved measurement from both devices at the same time, which is into the test. The algorithm is improved by comparing the set as the same as the method1 proposed in kinesiology body type of the user to the ’golden- standard’ body type. research community. The person were instructed to stand on The evaluation results of the optimized algorithm top of the FP and WBB and hold 40 static postures, each preliminarily prove the reliability of the software. lasting 5s. [19] mentioned that the reason to measure the KBM is that the improper lighting, loose fitting clothes, and Categories and Subject Descriptors large objects which surround the subject can adversely influence the skeleton fitting. In our kinect2 based KBM D.2.8 [Software Engineering]: Metrics—complexity measures, method, we find age, height, body type of the person as well performance measures as the error in foot tip position measurement also affect the CoP measurement results slightly. Keywords Virtual Reality, Center of Mass, Balance, Kinect, WBB The purpose of our measurement is different from the previ- ous measurements, since they just compared ’gold-standard’ 1. INTRODUCTION laboratory-grade force plate (FP) to a game controller wii The center of pressure (CoP) is also known as center of mass balance board (WBB). It is undeniable that they proved that (CoM). It is measured by different methods according to WBB can replace Balance Error Scoring System (BESS) different technical levels in different age. The methods which which is used for assessing the balance as subjective method are already applied in clinic include Balance Error Scoring for past years. Even some productions are developed based System (BESS), ’gold-standard’ laboratory-grade force plate their research, such as BtrackS [15] which is proved to reach (FP), wii blance board (WBB). Our research is to measure to the same level as WBB. But our KBM method is based on what extend the kinect balance measurement (KBM) method totally different measurement algorithm theory, imple- can be accepted by clinical utilization. In kinesiology mentation technology and suitable device context. We are research community, currently, there are several kinds of using the range image data captured by kinect2 device in measurements methods for comparison the ’gold-standard’ real-time, and further measure the CoP by our customized laboratory-grade force plate (FP) to wii blance board (WBB) algorithm based on optical theory. The Wii balance board presented. There are some measurement results recorded in can cannot measure any information about the weight and current literature. [42] compared the game scores provided by CoP onside the device, while the kinect can get full infor- Wii to traditional balance measures. [7] wanted to see if mation appeared in the camera view, but is not enable to traditional balance measures (like Center of Pressure velocity) retrieve the weight information since it is a non-contact sen- were reliable on the wii balance board and valid compared to sor. Nonetheless, the same characteristics of both devices are a forceplate, so they bypassed the games and simply collected that they have specialty or potential to measure CoP, but the raw data. [1] performed a standard measurement cannot measure density information by any direct or in- uncertainty analysis to provide the repeatability and accuracy of the WBB force and CoP measurements. In summary, the data from a wii balance board is valid and reliable [16] [4] [7] but the game scores that are produced are not [42]. Moreover, WBB may be useful for low-resolution measurements, but should not be considered as a replacement for laboratory-grade force plates. Therefore, WBB is enough accurate to measure our @home rehabilitation application using kinect, but the customized software is expected to be developed.
direct methods. Therefore, the robotics research community attracts our attention. The ’Center of mass calculation by kinect’ [18] uses SESC as the core algorithm to solve the calculation of CoM. This work has been compared to some other work: 1. FP; 2. WBB; 3. Kinect; 4. Vicon (High quality camera); 5. Winter’s method
2. SYSTEM In our research, we will consider the previous proposed meth- ods and results in both kinesiology research community and robotics research community, and design our particular mea- surement method according to our algorithm theory, device condition and clinical needs. The hardware devices we cur- rently owned include: 1. WBB; 2. Several Kinect2. The algorithm that is similar with our algorithm is mentioned in literature [25] [21], which is so-called segmentation meth- ods, also known as kinematic methods. The problem that ’performed mainly on cadavers or in live, young and fit indi- viduals’ is also mentioned [43] [13]. Some solutions are also discussed [34], which is ’should be adjusted for age, sex and fitness level’.
3. EVALUATION AND IMPROVING The evaluation is conducted along with the improving pro- cess. The evaluation process includes several stages. During the evaluation, the subject should place the stance limb in the center of the Wii balance board and place his or her hands on the hips and eyes open. Next, he or she will reach as far as possible in the 3 reach directions with the contralat- eral limb. Reach distance was defined as the farthest point that an individual could touch without accepting weight and while maintaining balance through the return to a bilateral stance.
Four person with different body type were involved into the Figure 1: The evaluation results for four person. test. The purpose of this session is to find the relation be- tween the body type with the errors of CoM calculation. Body segment inertial parameters (BSIPs) are important data in biomechanics. BSIPs have been measured by differ- ent methods, e.g. [3] [41] employs a whole body dual energy influence biomechanical analysis of human motion, predicted X-ray absorptiometry scan, [5] employs a motion capture joint moment and joint kinetics. system and two forceplates, [8] [9] employs a single kinect, [12] employs several digital cameras, [20] proposed a vali- This research is used to explorer the relationship between dation method to evaluate BSIPs. some metrics with the BSIPs and further improve the cal- culation of CoM. The most important factor is Body type, Our balance measurement algorithm is using segmentation which is also known as body shape. Body shape is affected by method, based on which, the weight of each segment’s CoM body fat distribution, which is correlated to current levels of position is the ratio of the segments to total body mass. sex hormones. There are some existing metrics to evaluate the BSIPs are the paramethers for this purpose. It’s already body type, such as BMI (Body Mass Index), BFP (Body fat proved that BSIPs are depending on the age, gender and percentage), BVI (Body Volume Index), WHR (Waist- hip body type and some researches have modified the classic ratio), WhtR (waist-to-height ratio). The series of im- ages BSIPs based on this theory [45] [13] [14] [34] [36] [2] even of the results indicate that the normalized distances on X-axis concerning the effects of weight loss in obese individuals [32] between the CoM of Kinect and WBB haven’t ob- vious and individuals of different morphology [10], as well as com- regular patterns. While the normalized distances on Y-axis posite concerning. Methods relying on imagery have the between the CoM of Kinect and WBB have the sim- ilar orbit drawback that the segmentation of body parts is complex, that the normalized CoM of WBB is always after the thus affecting significantly the BSIP values [20]. Indeed, the normalized CoM of Kinect. It also reveals that the nor- BSIP depend on the relative amount of bone, muscle and malized distances on Y-axis between the CoM of Kinect and adipose tissue in a body segment which underlie structural WBB depend on BFP. Because the order of the distances for and temporalchanges when aging or caused by pathology or the three subjects are B(m = 67, sd = 11) > C(m = 48, sd = training [37] [24]. [35] [33] [23] have measured the extent to 18) > A(m = 13, sd = 11), which is the same as the order of which errors in predicting body segment parameters could BFP (B(30.96) > C(23.39) > A(19.51)). The order of other metrics (i.e. BMI, WHR, WhtR) have differ- ent order. BFP) with the CoM calculation. We will also consider the possibility to reuse the results of other researches about the BFP is also written as BF %, which is usually measured by relation between body type and BSIPs and further conclude physical device, such as underwater weighing, whole- body the relation between body type and CoM calculation. Some air displacement plethysmography, near-infrared in- novel technology will also be used to improve this research, teractance, dual energy X-ray absorptiometry. In our appli- e.g., Sensors [44], Virtual Rehabilitation [28] [27] [29] [30], cation scene, the physical device is not suitable. So we em- Video Game [31] [6], Control [46], Database [40], Distributed ployes the formula derived from previous researches’ statistic Computing [39] [26] [22], Optimization Algorithm [11]. results. The calculation of BFP is as followed. NEW BMI = 1.3 × weight(kg)/height(m)2.5 [38] Acknowledgment The authors thank to LanPercept, a Marie Curie Initial Adultbodyfat% =(1.20×BMI)+(0.23×Age)−(10.8×sex)−5.4 where Training Network funded through the 7th EU Framework sex is 1 for males and 0 for females. Programme (316748).
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