Using Artificial Intelligence for Some Activity Recognition and Anomaly Identification Using a Multi-Sensor Based Smart Home System

Abstract

Ambient Assisted Living (AAL) research frequently contends with limitations including reliance on supervised data, lack of personalization and interpretability, and evaluations in artificial laboratory settings. This study aimed to address these gaps by developing an unsupervised, personalized, and interpretable AAL system using low-cost sensors for long- term, real-world activity recognition and behavioural anomaly detection. A multi-modal sensor network (including contact, vibration, outlet, air quality sensors) was deployed in a single participant’s apartment for over 90 days. Primarily unsupervised machine learning techniques, augmented with interpretability methods (SHAP), were used to identify key ac- tivities (cooking, couch-sitting, showering) and detect personalized behavioural deviations. Minimally supervised approaches for showering detection were also accurately achieved using humidity data to address the shortcomings of unsupervised showering model. More importantly, the system effectively identified interpretable anomalies demonstrating the model’s capability to learn the individual’s normal behaviour in the home and identify anomalies, representing significant deviations from the participant’s established routines. In addition, the model was also able to be interpretable that allowed for the participant to understand why each anomaly occured. This study confirms the feasibility of leveraging unsupervised, interpretable methods with affordable sensors for personalized, ecologically valid AAL, significantly reducing labelling dependence and enhancing system trustworthi- ness for scalable, unobtrusive health monitoring.