Title: Design of human activity recognition algorithms based on a single wearable IMU sensor

Authors: Wei Zhuang; Yi Chen; Jian Su; Baowei Wang; Chunming Gao

Addresses: School of Computer and software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' School of Computer and software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' School of Computer and software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' School of Computer and software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' School of Engineering and Technology, University of Washington Tacoma, Tacoma, WA 98402, USA

Abstract: In recent years, with the rapid development of inertial measurement unit (IMU) technology, wireless body area network and pattern recognition theory, human motion recognition based on wearable technology has gradually gained the attention of researchers. In this paper, the human activity recognition method based on wearable sensor motion information fusion is studied. On the existing wearable system platform, the time domain analysis and frequency domain analysis of human motion information are used to distinguish the daily activity of the human body, and based on the human motion data acquisition experiment, time domain features, frequency domain features and attitude angles of the human motion data are used as identification features. On that basis multi-classification activity recognition algorithm based on support vector machine is proposed and human motion pattern recognition is carried out. The experimental results show that the system can accurately identify the daily activity of the human body.

Keywords: IMU; inertial measurement unit; wearable technology; attitude angle estimation; posture recognition; support vector machine.

DOI: 10.1504/IJSNET.2019.100218

International Journal of Sensor Networks, 2019 Vol.30 No.3, pp.193 - 206

Received: 26 Feb 2019
Accepted: 07 Mar 2019
Published online: 18 Jun 2019 *

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