Journal Published Online: 04 August 2022
Volume 51, Issue 3

Physiological Feature Recognition Algorithm of Human Exercise-Induced Fatigue Based on Parameter Analysis of Nanomachine Simulation for a Future Smart World

CODEN: JTEVAB

Abstract

Traditional methods cannot fully reflect the fatigue degree of human exercise through a single parameter, which has the problems of long recognition time, high recognition coincidence, and poor anti-interference. Therefore, a physiological feature recognition algorithm of human exercise-induced fatigue based on parameter analysis of nanomachine simulation is proposed. The output value of body weight and a three-axis acceleration sensor are used to calculate the energy consumption parameters. By judging the time series period of fatigue physiological features data, the physiological features of human exercise-induced fatigue are extracted, and the feature extraction results are fused. According to the fusion results, the feature recognition is realized by analyzing the simulation parameters of nanomachine. The experimental results show that the shortest feature recognition time of the proposed method is 2.35 s, which is significantly lower than that of the traditional method, the feature recognition coincidence degree of the proposed method is lower, and the anti-interference performance is better, which fully shows that the method can accurately judge the degree of human exercise-induced fatigue.

Author Information

Chen, Yujue
College of Physical Education and Sport Academy, Qinghai Normal University, Xining, Qinghai Province, China
Hu, He
College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, China
Li, Qiang
College of Physical Education and Sport Academy, Qinghai Normal University, Xining, Qinghai Province, China
Pages: 12
Price: $25.00
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Details
Stock #: JTE20220087
ISSN: 0090-3973
DOI: 10.1520/JTE20220087