Development of Squat Posture Guidance System Using Kinect and Wii Balance Board

Abstract

This study designs a squat posture recognition system that can provide correct squat posture guidelines. This system comprises two modules: a Kinect camera for monitoring users' body movements and a Wii Balance Board(WBB) for measuring balanced postures with legs. Squat posture recognition involves two states: "Stand" and "Squat." Further, each state is divided into two postures: correct and incorrect. The incorrect postures of the Stand and Squat states were classified into three and two different types of postures, respectively. The factors that determine whether a posture is incorrect or correct include the difference between shoulder width and ankle width, knee angle, and coordinate of center of pressure(CoP). An expert and 10 participants participated in experiments, and the three factors used to determine the posture were measured using both Kinect and WBB. The acquired data from each device show that the expert's posture is more stable than that of the subjects. This data was classified using a support vector machine (SVM) and $na{\ddot{i}}ve$ Bayes classifier. The classification results showed that the accuracy achieved using the SVM and $na{\ddot{i}}ve$ Bayes classifier was 95.61% and 81.82%, respectively. Therefore, the developed system that used Kinect and WBB could classify correct and incorrect postures with high accuracy. Unlike in other studies, we obtained the spatial coordinates using Kinect and measured the length of the body. The balance of the body was measured using CoP coordinates obtained from the WBB, and meaningful results were obtained from the measured values. Finally, the developed system can help people analyze the squat posture easily and conveniently anywhere and can help present correct squat posture guidelines. By using this system, users can easily analyze the squat posture in daily life and suggest safe and accurate postures.

Fig. 1. Overall procedure of posture recognition system.

Fig. 2. Measuring difference between shoulder width and ankle width.

Fig. 3. Determination of knee angle: measuring the knee angle (θ) using coordinates of the three points.

Fig. 4. Coordinate axis and pressure sensor recognized by Wii Balance Board.

Fig. 5. Stand posture, Normal Stand posture: (a) posture, Incorrect Posture: (b)~(d) posture. (a), (b), (c) and (d) from the left. The solid line indicates the position of the user's feet, and the dotted line indicates the center of the body.

Fig. 6. Squat posture, Normal Squat Posture: (e) posture, Knee Protruding posture: (f) posture. (e) and (f) from the left. The solid line indicates the position of the user's knee, and the dotted line indicates the position of the knee in the correct posture.

Fig. 7. Comparison of CoP trajectory between expert and experiment subject: (e) posture.

Fig. 8. FFT results of CoP-Origin distance comparison between experts and subject.

Table 1. Items to be measured between experiments

Table 2. The paired t-test result of the difference in the ${\parallel}\overrightarrow{SLSR}{\parallel}$ - ${\parallel}\overrightarrow{ALAR}{\parallel}$ values and θ(Knee) values for expert and subjects in (a)~(f) posture

Table 3. Mean values of CoP-Origin distance comparison between experts and subject

Table 4. Accuracy (%) of SVM and Naïve Bayes classification