Human Action Recognition Based on Skeleton Information and Multi-Feature Fusion

Author:

Wang Li12ORCID,Su Bo13,Liu Qunpo13,Gao Ruxin13ORCID,Zhang Jianjun13,Wang Guodong4ORCID

Affiliation:

1. School of Electrical Engineering & Automation, Henan Polytechnic University, Jiaozuo 454003, China

2. Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Jiaozuo 454003, China

3. Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment, Jiaozuo 454003, China

4. Computer Science Department, Massachusetts College of Liberal Arts, North Adams, MA 01247, USA

Abstract

Action assessment and feedback can effectively assist fitness practitioners in improving exercise benefits. In this paper, we address key challenges in human action recognition and assessment by proposing innovative methods that enhance performance while reducing computational complexity. Firstly, we present Oct-MobileNet, a lightweight backbone network, to overcome the limitations of the traditional OpenPose algorithm’s VGG19 network, which exhibits a large parameter size and high device requirements. Oct-MobileNet employs octave convolution and attention mechanisms to improve the extraction of high-frequency features from the human body contour, resulting in enhanced accuracy with reduced model computational burden. Furthermore, we introduce a novel approach for action recognition that combines skeleton-based information and multiple feature fusion. By extracting spatial geometric and temporal characteristics from actions, we employ a sliding window algorithm to integrate these features. Experimental results show the effectiveness of our approach, demonstrating its ability to accurately recognize and classify various human actions. Additionally, we address the evaluation of traditional fitness exercises, specifically focusing on the BaDunJin movements. We propose a multimodal information-based assessment method that combines pose detection and keypoint analysis. Label sequences are obtained through a pose detector and each frame’s keypoint coordinates are represented as pose vectors. Leveraging multimodal information, including label sequences and pose vectors, we explore action similarity and perform quantitative evaluations to help exercisers assess the quality of their exercise performance.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Research on IMU-Based Motion Attitude Acquisition and Motion Recognition;IEEE Sensors Journal;2024-07-01

2. Yoga Pose Classification Using CNN with PReLU Activation;2024 International Conference on Advancements in Power, Communication and Intelligent Systems (APCI);2024-06-21

3. Advanced Yoga Pose Estimation: Enhancing PoseNet with Adaptive Key Point Elimination;2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI);2024-04-17

4. Single-Stage Pose Estimation and Joint Angle Extraction Method for Moving Human Body;Electronics;2023-11-14

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