Premium
Action recognition and correction by using EMG signal for health sports
Author(s) -
Meng Huan,
Wang Jianzhi,
Lei Chen,
Zhang Hongbao
Publication year - 2020
Publication title -
internet technology letters
Language(s) - English
Resource type - Journals
ISSN - 2476-1508
DOI - 10.1002/itl2.241
Subject(s) - spectrogram , action (physics) , computer science , signal (programming language) , convolutional neural network , channel (broadcasting) , constructive , speech recognition , tree (set theory) , pattern recognition (psychology) , artificial intelligence , mathematics , telecommunications , mathematical analysis , physics , process (computing) , quantum mechanics , programming language , operating system
When a person perform daily sports or exercises, his or her actions may not be standard. If the actions are non‐standard, the effect of sports or exercises would degrade seriously. Thus, recognizing non‐standard actions is critical to provide constructive suggestions for daily exercises. In order to recognize actions and correct the non‐standard ones during exercises and sports, we propose a method based on EMG signal to provide exercise suggestions. We first divide each channel of EMG signal into fixed‐size segments in the form of sliding windows, second use short‐time Fourier Transform to convert EMG signal segments as spectrograms, third input the spectrograms into a convolutional neural network to recognize the actions, fourth use a decision tree to determine whether the action is standard or non‐standard and provide exercise suggestions if the action is non‐standard. The experimental results show that the proposed method could identify most of the actions and recognize the non‐standard action.