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Analysis of aerobic training posture using machine vision for body area networks

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Abstract

The application of wireless sensor networks (WSNs) for monitoring vital signs of a human body, i.e., Wireless body sensor networks (WBSNs), has attained significant attention. The realization of WBSNs has cemented its place among a wide range of communication networks for monitoring various postures of a human being. One of the fundamental ways to increase athletes’ competitive level in modern sports is to accurately analyze aerobics workout postures using technical tools. The current generative models of three-dimensional (3D) human motion performs poorly and faces difficulties in analyzing the new movements of an athlete. Therefore, accurate prediction and analysis of aerobics training pose need to be made in real-time. This study uses deep learning (DL) and WBSN approaches to build artificial intelligence (AI)-based system for predicting and analyzing athletes’ posture. The solution uses an Arduino embedded development board as the basis, equipped with multiple inertial measurement units (IMUs) and vision sensors. Further, using stepper motors in combination, a system for collecting accurate human motion data such as velocity and acceleration is established. The proposed configuration yields accurate data on human motion. This study builds a DL model based on the symmetric, time-scale, and structural coding to identify human motion models precisely. These models’ performance was assessed in terms of classification error using the H3.5 M and CMU datasets. The experimental results verify the functional performance of the system, and the comparison with the existing latest encoder-recurrent-decoder (ERD) classification algorithms shows that the system has a low classification error and high real-time performance, which has good prospects for secondary development.

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  1. SIAS University, Zhengzhou, Henan, China

    Tiantian Cao

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  1. Tiantian Cao
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Cao, T. Analysis of aerobic training posture using machine vision for body area networks. Wireless Netw 29, 1611–1620 (2023). https://doi.org/10.1007/s11276-022-03123-5

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