Exploration of new community fitness mode using intelligent life technology and AIoT. (5th March 2022)

• Record Type: Journal Article
• Authors: Cao, Li; Zhan, Chongjiang
• Is Part Of: International journal of grid and utility computing. Volume 13:Number 1(2022)
• Page Start: 57
• Page End: 65
• Publication Date: 2022-03-05
• Subjects: AIoT -- motion recognition -- intelligent life technology -- intelligent wearable device; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

Abstract

The purpose is to better mine the fitness motion data for intelligent wearable devices and promote the development of the new community fitness mode. First, the defects of the traditional fitness motion recognition system are analysed. Then, software engineering technology and Deep Learning (DL) technology are used to build a multi-layer fitness motion monitoring system. Finally, the data of running, riding, race walking, and rope skipping in the PAMAP2 data set are used for system evaluation. The results show that the proposed motion data monitoring system has an average accuracy of 97.622%, an average precision of 96.322% and a recall rate of 96.021% for fitness data recognition. The experimental results suggest that intelligent wearable devices with the proposed monitoring system can effectively mine wears' motion data and promote the development of the new community fitness mode.