A study on multiple wearable sensors for activity recognition

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2017 IEEE Conference on Dependable and Secure Computing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages449-452
Number of pages4
ISBN (Electronic)9781509055692
DOIs
StatePublished - 18 Oct 2017
Event2017 IEEE Conference on Dependable and Secure Computing - Taipei, Taiwan
Duration: 7 Aug 201710 Aug 2017

Publication series

Name2017 IEEE Conference on Dependable and Secure Computing

Conference

Conference2017 IEEE Conference on Dependable and Secure Computing
CountryTaiwan
CityTaipei
Period7/08/1710/08/17

Keywords

  • Activity recognition
  • Machine learning
  • Multi-sensors

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  • Cite this

    Huang, Y. C., Ik, T-U., Peng, W-C., Lin, H. C., & Huang, C. Y. (2017). A study on multiple wearable sensors for activity recognition. In 2017 IEEE Conference on Dependable and Secure Computing (pp. 449-452). [8073827] (2017 IEEE Conference on Dependable and Secure Computing). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DESEC.2017.8073827