HAR70+W: Integrating Thigh-Back and Wrist Sensor Data for Enhanced Elderly Activity Monitoring

Human Activity Recognition (HAR) systems for elderly mon- itoring often rely on single-sensor datasets, limiting their ability to cap- ture full-body movement patterns critical for detecting functional de- cline. While datasets like HAR70+ (thigh/back sensors) excel in lower- body activities (e.g., walking, sitting) and WEDA-Fall (wrist sensors) captures arm gestures (e.g., clapping, knocking), no prior work inte- grates these complementary sources for a comprehensive analysis. To bridge this gap, we present HAR70 + W, the first unified HAR frame- work combining both datasets through video-assisted label remapping to resolve inconsistencies. Our integrated approach enables comprehen- sive monitoring of both gross motor activities and fine hand movements, establishing a foundation for robust, multi-sensor HAR systems tailored to aging-related mobility assessment. Experimental results demonstrate improved activity recognition accuracy compared to single-dataset ap- proaches, highlighting its potential for early detection of functional de- cline in elderly populations.