Multimodel Lightweight Transformer Framework for Human Activity Recognition

Wen Qi*, Chengwei Lin, Kun Qian*

*Corresponding author for this work

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

Abstract

Human Activity Recognition (HAR) finds extensive application across diverse domains. Yet, its integration into healthcare remains challenging due to disparities between prevailing HAR systems optimized for rudimentary actions in controlled settings and the nuanced behaviors and dynamic conditions pertinent to medical diagnostics. Furthermore, prevailing sensor technologies and deployment scenarios present formidable hurdles regarding wearability and adaptability to heterogeneous environments. While navigating these constraints, this investigation evaluates the requisite monitoring simplicity and system adaptability crucial for medical contexts. A HAR framework is proposed, leveraging a Lightweight Transformer architecture with a multi-sensor fusion strategy employing five Inertial Measurement Units (IMUs) as sensors. A Real-world HAR dataset is assembled to authenticate the system's suitability, and a comprehensive array of experiments is conducted to showcase its potential utility.

Original languageEnglish
Title of host publication46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350371499
DOIs
Publication statusPublished - 2024
Event46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Orlando, United States
Duration: 15 Jul 202419 Jul 2024

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024
Country/TerritoryUnited States
CityOrlando
Period15/07/2419/07/24

Keywords

  • Human Activity Recognition
  • Lightweight Transformer
  • Multiple Data Fusion

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