A study on multiple wearable sensors for activity recognition

Yu Chuan Huang, Tsi-Ui Ik, Wen-Chih Peng, Hsing Chen Lin, Ching Yu Huang

研究成果: Conference contribution同行評審

14 引文 斯高帕斯(Scopus)

摘要

In the past few years, human activity recognition is an active area of machine learning. The possible applications include daily activity monitoring for elders, exercise and fitness workout assistant systems, life style analysis, etc. In this work, tri-axial accelerometers were worn at the right wrist, left wrist and waist to collect motion data for activity recognition. Three supervised machine learning algorithms including random forests, decision trees and support vector machines were implemented to classify daily activities into running, walking, standing, sitting and dining from inertial data. The purposes of this study are to understand how good the machine learning algorithms can achieve and how the wearing location and number of sensors impact the recognition accuracy. Our results showed that the multi-sensors achieve the accuracy of 81%, and dominant hand sensor achieves the accuracy of 80%, which is 7% higher than non-dominant hand sensor.

原文English
主出版物標題2017 IEEE Conference on Dependable and Secure Computing
發行者Institute of Electrical and Electronics Engineers Inc.
頁面449-452
頁數4
ISBN(電子)9781509055692
DOIs
出版狀態Published - 18 10月 2017
事件2017 IEEE Conference on Dependable and Secure Computing - Taipei, Taiwan
持續時間: 7 8月 201710 8月 2017

出版系列

名字2017 IEEE Conference on Dependable and Secure Computing

Conference

Conference2017 IEEE Conference on Dependable and Secure Computing
國家/地區Taiwan
城市Taipei
期間7/08/1710/08/17

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