Action recognition and correction by using EMG signal for health sports. Issue 3 (5th October 2020)

• Record Type: Journal Article
• Authors: Meng, Huan; Wang, Jianzhi; Lei, Chen; Zhang, Hongbao
• Is Part Of: Internet technology letters. Volume 4:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-05
• Subjects: action recognition and correction -- convolutional neural network -- decision tree -- electromyogram; Internet -- Periodicals; 004.67805
• DOI: 10.1002/itl2.241
• Languages: English
• ISSNs: 2476-1508

Abstract

Abstract : 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.