Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare

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.