Human Motion Tracking Performance Evaluation Method Based on IMU/TOA Fusion
XU Cheng1,2, HE Jie1,2, ZHANG Xiao-tong1,2, YAO Cui1,2, DUAN Shi-hong1,2, QI Yue1,2
1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2. Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
Abstract:With the rapid development of the Internet of things and the body area network,the human motion tracking technology has been widely used in medical,security and many other fields.A human motion tracking method based on IMU/TOA fusion is proposed to solve the problem of error accumulation and drift in a single IMU human motion tracking system.On this basis,the effectiveness of IMU/TOA fusion method is proved theoretically by deducing the cramer-rao lower bound (CRLB) of fusion system.The experimental results show that the proposed human motion tracking method based on IMU/TOA fusion has great improvement in both spatial and temporal performance.
[1] Cao J,Li W,Ma C,et al.Optimizing multi-sensor deployment via ensemble pruning for wearable activity recognition[J].Information Fusion,2018,41(5):68-79.
[2] Xu C,He J,Zhang X,et al.Detection of freezing of gait using template-matching-based approaches[J].Journal of Sensors,2017,2017(2):1-8.
[3] Bulling A,Blanke U,Schiele B.A tutorial on human activity recognition using body-worn inertial sensors[J].ACM Computing Surveys (CSUR),2014,46(3):33.
[4] Tian Y,Hamel W R,Tan J.Accurate human navigation using wearable monocular visual and inertial sensors[J].IEEE Transactions on Instrumentation and Measurement,2014,63(1):203-213.
[5] Chavarriaga R,Sagha H,Calatroni A,et al.The opportunity challenge:a benchmark database for on-body sensor-based activity recognition[J].Pattern Recognition Letters,2013,34(15):2033-2042.
[6] Yang X,Tian Y L.Super normal vector for human activity recognition with depth cameras[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(5):1028-1039.
[7] Xu C,He J,Zhang X,et al.Geometrical kinematic modeling on human motion using method of multi-sensor fusion[J].Information Fusion,2018,41(5):243-254.
[8] Mosenia A,Sur-Kolay S,Raghunathan A,et al.Wearable medical sensor based system design:a survey[J].IEEE Transactions on Multi-Scale Computing Systems,2017,3(2):124-138.
[9] Zihajehzadeh S,Yoon P K,Kang B S,et al.UWB-aided inertial motion capture for lower body 3-D dynamic activity and trajectory tracking[J].IEEE Transactions on Instrumentation and Measurement,2015,64(12):3577-3587.
[10] Gravina R,Alinia P,Ghasemzadeh H,et al.Multi-sensor fusion in body sensor networks:state-of-the-art and research challenges[J].Information Fusion,2017,35(5):68-80.
[11] Janidarmian M,Roshan Fekr A,Radecka K,et al.A comprehensive analysis on wearable acceleration sensors in human activity recognition[J].Sensors,2017,17(3):529.
[12] Cornacchia M,Ozcan K,Zheng Y,et al.A survey on activity detection and classification using wearable sensors[J].IEEE Sensors Journal,2017,17(2):386-403.
[13] Bao S D,Meng X L,Xiao W,et al.Fusion of inertial/magnetic sensor measurements and map information for pedestrian tracking[J].Sensors,2017,17(2):340.
[14] Tichavsky P,Muravchik C H,Nehorai A.Posterior Cramér-Rao bounds for discrete-time nonlinear filtering[J].IEEE Transactions on Signal Processing,1998,46(5):1386-1396.
[15] Horn R A,Jhonson C R.Matrix Analysis[M].Cambridge,U K:Cambridge University Press,1990.