Abnormalities Detection of IMU based on PCA in motion monitoring
Inertial measurement units (IMU) are used as an affordable and effective remote measurement method for health monitoring in body sensor networks (BSNs) based on tracking peoples daily motions and activities. These inertial sensors are mostly microelectro-mechanical systems with a combination of multi-axis combinations of precision gyroscopes, accelerometers, and magnetometers to sense multiple degrees of freedom (DoF).Unfortunately in the process of motion monitoring actual sensor outputs may contain some abnormalities, which might result in the misinterpretations of activities. In this paper, we use Principal component analysis (PCA) combined with Hotellings T2 and SPE statistic to detect abnormal data in the process of motion monitoring with IMU to ensure the reliability and accuracy in application. The simulated results prove this method is effective and feasible.
Inertial Measurement Units Principal Component Analysis Body Sensor Networks Motion Monitoring
Jing Zhou Steven Su Ai Huang Guo Wei Dong Chen
School of Electronics and Information, Tongji University, China Faculty of Engineering and Informati Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia School of Electronics and Information, Tongji University, China School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, China
国际会议
黄山
英文
533-538
2012-07-10（万方平台首次上网日期，不代表论文的发表时间）