Abstract
This research paper introduces the Smart Plug Hub (SPH), a non-invasive system designed to accurately estimating a patient's Activities of Daily Living (ADL). Traditional methods for measuring ADL include interviews, remote video systems, and wearable devices that track behavior. However, these approaches have limitations, such as patient memory dependency, privacy violations, and careless device management. To address these limitations, SPH utilizes sensor fusion to analyze time-series environmental signals and accurately estimate a patient's ADL. We have effectively optimized the utilization of computing resources through the implementation of "device collaboration" in SPH to receive event data and segments portions of the time-series environmental signal. By segmenting the data into smaller segments, we extracted an analyzable dataset, which was processed by an edge device-SPH. We have conducted several experiments with the SPH, and our research has resulted in a significant 75% accuracy in the classification of patients' kitchen ADLs and an 85% accuracy in the classification of toilet ADLs. These activities include actions such as eating activities in the kitchen and typical activities performed in the toilet. These findings have substantial implications for the progress of healthcare and patient care, highlighting the potential uses of the SPH technology in the monitoring and improvement of daily living activities.