Fine-Grained Activity of Daily Living (ADL) Recognition Through Heterogeneous Sensing Systems With Complementary Spatiotemporal Characteristics - Pan, Shijia; Berges, Mario; Rodakowski, Juleen; Zhang, Pei; Noh, Hae Young (Frontiers in Built Environment, 2020)
Non-intrusive monitoring of fine-grained activities of daily living (ADL) enables various smart healthcare applications. For example, ADL pattern analysis for older adults at risk can be used to assess their loss of safety or independence. Prior work in the area of ADL recognition has focused on coarse-grained 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 heterogeneous multi-modal cyber-physical system, where we use 1) distributed vibration sensors to capture the action-induced structural vibrations and their spatial characteristics for information aggregation, and 2) single point electrical sensor to capture appliance usage with high temporal resolution. To evaluate our system, we conducted real-world experiments with multiple human subjects to demonstrate the complementary information from these two sensing modalities. 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.