Research on Human Activity Recognition Method Based on Wearable Sensors

Yu Li, Yougen Xu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Wearable sensors have become essential platforms for research on human activity recognition (HAR) due to their advantages such as compact size, low power consumption, and non-invasive nature, ensuring continuous and privacy-conscious data acquisition. In this paper, a HAR architecture using Machine Learning (ML) technique based on data collected from wearable sensors is proposed to perform high performance for accurate recognition of human activities in real-life scenarios. To address the challenge of accurately distinguishing similar forms of daily activities, a feature library consisting of 55 feature functions has been constructed, and the Maximum Relevance Minimum Redundancy (mRMR) algorithm is employed to select the most informative and relevant features for activity classification. Experimental results indicate that the combination of data from multiple sensors and the dynamic selection of features significantly improve the performance of the HAR system, as they provide a more comprehensive and diverse set of information. The numerical results show that the human activity recognition framework proposed in this paper can achieve an accuracy of 98.53% on the self-collecting dataset.

Original languageEnglish
Title of host publicationFourth International Conference on Sensors and Information Technology, ICSI 2024
EditorsLijun Wu, Zhongpan Qiu
PublisherSPIE
ISBN (Electronic)9781510678682
DOIs
Publication statusPublished - 2024
Event4th International Conference on Sensors and Information Technology, ICSI 2024 - Xiamen, China
Duration: 5 Jan 20247 Jan 2024

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume13107
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference4th International Conference on Sensors and Information Technology, ICSI 2024
Country/TerritoryChina
CityXiamen
Period5/01/247/01/24

Keywords

  • feature selection
  • human activity recognition
  • Random Forest
  • wearable sensors

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