JACIII Vol.27 No.5 pp. 915-922
doi: 10.20965/jaciii.2023.p0915

Research Paper:

Human Motion Capture and Recognition Based on Sparse Inertial Sensor

Huailiang Xia*1, Xiaoyan Zhao*1,*2,†, Yan Chen*2, Tianyao Zhang*2, Yuguo Yin*3, and Zhaohui Zhang*1,*2,*4

*1Shunde Innovation Institute, University of Science and Technology Beijing
Fo Shan 528399, China

*2School of Automation and Electrical Engineering, University of Science and Technology Beijing
30# Xueyuan Road, Haidian District, Beijing 100083, China

*3Shandong Start Measurement and Control Equipment Co., Ltd.
No.600 Xinyi Road, Weifang, Shandong 261101, China

*4Beijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology Beijing
30# Xueyuan Road, Haidian District, Beijing 100083, China

Corresponding author

February 27, 2023
May 22, 2023
September 20, 2023
deep learning, inertial measurement unit (IMU), spare sensors, long and short-term memory (LSTM), motion capture

The field of human motion capture technology represents an emergent and multifaceted domain that encapsulates various disciplines, including but not limited to computer graphics, ergonomics, and communication technology. A distinct network platform within its domain has been established to ensure the reliability and stability of data transmission. Moreover, a sink node has been configured to facilitate sensor data reception through two distinct channels. Notably, the simplicity of the measurement system is directly proportional to the limited number of sensors used. This study focuses on accurately estimating uncertain human 3D movements via a sparse arrangement of wearable inertial sensors, utilizing only six sensors within the system. The methodology is based on a time series sequence throughout the motion process, wherein a series of discontinuous actions constitute the sequential motion. Deep learning methodologies, specifically recurrent neural networks, were employed to refine the regression parameters. Our approach integrated both historical and present sensor data to forecast future sensor data. These data were amalgamated into a superposed input vector, which was fed back into a shallow neural network to estimate human motion. Our experimental results demonstrate the viability of this approach: the six sensors could accurately replicate representative poses. This finding carries significant implications for advancing and applying wearable devices within the realm of motion capture, offering the potential for widespread adoption and implementation.

Real-time human motion capture

Real-time human motion capture

Cite this article as:
H. Xia, X. Zhao, Y. Chen, T. Zhang, Y. Yin, and Z. Zhang, “Human Motion Capture and Recognition Based on Sparse Inertial Sensor,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.5, pp. 915-922, 2023.
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Last updated on Apr. 22, 2024