TY - GEN
T1 - Multimodel Lightweight Transformer Framework for Human Activity Recognition
AU - Qi, Wen
AU - Lin, Chengwei
AU - Qian, Kun
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Human Activity Recognition
KW - Lightweight Transformer
KW - Multiple Data Fusion
UR - http://www.scopus.com/inward/record.url?scp=85214972842&partnerID=8YFLogxK
U2 - 10.1109/EMBC53108.2024.10781743
DO - 10.1109/EMBC53108.2024.10781743
M3 - Conference contribution
C2 - 40040102
AN - SCOPUS:85214972842
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
BT - 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024
Y2 - 15 July 2024 through 19 July 2024
ER -