Fine-Grained Recognition of Activities of Daily Living through Structural Vibration and Electrical Sensing

Abstract

Fine-grained non-intrusive monitoring of activities of daily living (ADL) enables various smart building applications, including ADL pattern assessments for older adults at risk for loss of safety or independence. Prior work in this area has focused on coarsegrained ADL recognition at the activity level (e.g., cooking, cleaning, sleeping), and/or course-grained (hourly or minutely) activity duration segmentation. It also typically relies on a high-density deployment of a variety of sensors. Finer-grained (sub-second-level and event-based) ADL recognition, could provide more detailed ADL information, which is crucial for enabling the assessment of patients' activity patterns and potential changes in behavior. To achieve this fine-grained ADL monitoring, we present a system that combines two emerging non-intrusive sparse sensing mechanisms: 1) vibration sensors to capture the action-induced structural vibration and 2) electrical sensor to capture appliance usage. To evaluate our system, we conducted real-world experiments with multiple human subjects to demonstrate the complementary information from these two sensing modalities. Results show that our system achieved an average 90% accuracy in recognizing activities, which is up to 2.6X higher than baseline systems considering each state-of-the-art sensing modality separately.