TY - GEN
T1 - Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare
AU - Monjur, Mahathir
AU - Liu, Jia
AU - Xu, Jingye
AU - Zhang, Yuntong
AU - Wang, Xiaomeng
AU - Li, Chengdong
AU - Park, Hyejin
AU - Wang, Wei
AU - Shieh, Karl
AU - Munir, Sirajum
AU - Wang, Jing
AU - Song, Lixin
AU - Nirjon, Shahriar
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper examines the application of WiFi signals for real-world monitoring of daily activities in home health-care scenarios. While the state-of-the-art of WiFi-based activity recognition is promising in lab environments, challenges arise in real-world settings due to environmental, subject, and system configuration variables, affecting accuracy and adaptability. The research involves deploying systems in various settings and analyzing data shifts. It aims to guide realistic development of robust, context-aware WiFi sensing systems for elderly care. The findings suggest that a shift in WiFi data can come from various sources such as unseen environment and user, degrading the performance of WiFi-based activity sensing systems. While conventional domain shift techniques can partially mitigate data shift effects, further research is warranted to bridge the gap between academic research and practical applications.
AB - This paper examines the application of WiFi signals for real-world monitoring of daily activities in home health-care scenarios. While the state-of-the-art of WiFi-based activity recognition is promising in lab environments, challenges arise in real-world settings due to environmental, subject, and system configuration variables, affecting accuracy and adaptability. The research involves deploying systems in various settings and analyzing data shifts. It aims to guide realistic development of robust, context-aware WiFi sensing systems for elderly care. The findings suggest that a shift in WiFi data can come from various sources such as unseen environment and user, degrading the performance of WiFi-based activity sensing systems. While conventional domain shift techniques can partially mitigate data shift effects, further research is warranted to bridge the gap between academic research and practical applications.
KW - activity recognition
KW - real-world dataset shift
KW - WiFi sensing
UR - http://www.scopus.com/inward/record.url?scp=85203699593&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85203699593&partnerID=8YFLogxK
U2 - 10.1109/ICHI61247.2024.00037
DO - 10.1109/ICHI61247.2024.00037
M3 - Conference contribution
AN - SCOPUS:85203699593
T3 - Proceedings - 2024 IEEE 12th International Conference on Healthcare Informatics, ICHI 2024
SP - 228
EP - 233
BT - Proceedings - 2024 IEEE 12th International Conference on Healthcare Informatics, ICHI 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Healthcare Informatics, ICHI 2024
Y2 - 3 June 2024 through 6 June 2024
ER -