Title: Multi-modal human motion recognition based on behaviour tree

Authors: Qin Yang; Zhenhua Zhou

Addresses: School of Physical Education, Hunan City University, Yi'yang, 413000, China ' School of Physical Education of Hunan Normal University, Chang'sha, 410012, China

Abstract: Since the efficiency and accuracy of existing methods are low in complex multi-modal human motion recognition, this paper studies the multi-modal human motion recognition method based on behaviour tree. Firstly, Kinect sensor is used to collect multi-modal motion data of human body, and convolutional neural network is used to denoise the collected motion data. On the basis of denoising data, wavelet packet decomposition is used to extract its features. Finally, according to the extracted multi-modal human motion features, a behaviour tree model is constructed to traverse the recognised human motion and achieve accurate and efficient multi-modal human motion recognition according to the degree of feature matching. The experimental results show that the recognition accuracy of the proposed method can reach 98%, the highest recall rate is 96%, the highest F1 is 0.97, and the longest recognition time is only 4.65 seconds, which indicates that the proposed method has high practicability.

Keywords: Kinect sensor; behaviour tree; convolutional neural network; multimodal human motion recognition.

DOI: 10.1504/IJBM.2024.138239

International Journal of Biometrics, 2024 Vol.16 No.3/4, pp.381 - 398

Received: 29 Jun 2023
Accepted: 14 Sep 2023
Published online: 30 Apr 2024 *

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