Unsupervised, Personalized, and Interpretable Activity Recognition and Anomaly Detection in a Real-World Smart Home
1
School of Public Health Sciences, University of Waterloo, Waterloo, Canada
2
Department of Bio-Mechanics and Bio-Engineering, University of Technology of Compiègne, Compiegne, France
Popul. Med. 2026;8(Supplement Supplement 1):A912
ABSTRACT
INTRODUCTION:
Ambient Assisted Living (AAL) research frequently contends with limitations, including a reliance on supervised data, a lack of personalization and interpretability, and evaluations in artificial laboratory settings [1]. These challenges hinder real-world deployment by requiring burdensome data labelling and failing to capture individual-specific routines [2]. The primary study aim was to use low-cost, multi-modal sensors for long-term, real-world activity recognition (cooking, couch-sitting, showering) and behavioural anomaly detection in a genuine home environment [3].
METHODS:
A multi-modal sensor network, including contact, vibration, smart outlet, and air quality sensors, was deployed in a single participant's apartment for over 90 days, ensuring high ecological validity. Primarily unsupervised machine learning techniques were employed. K-means clustering, validated by the Silhouette Coefficient, was used to identify appliance states (for cooking) and to cluster bathroom events (showering). Isolation Forest models were used to detect personalized behavioural deviations in each room. These models were augmented with interpretability methods (SHAP values) to explain anomaly flags. A minimally supervised Random Forest, evaluated using Accuracy, Precision, Recall, and F1-score, was also compared.
RESULTS:
The unsupervised K-means clustering for appliance states showed high cluster separation (e.g., Silhouette Scores > 0.93) and high fidelity (e.g., >99.6% agreement). The unsupervised shower model achieved a Silhouette Score of 0.867 and 99% accuracy. In comparison, the minimally supervised Random Forest model (trained on ~20 days of data) achieved 99.8% accuracy and an F1-score of 0.989. The system effectively identified interpretable anomalies, such as abnormally long non-shower bathroom stays (over 60 minutes), atypical late-night bedroom activity, and unusually long cooking durations.
CONCLUSIONS:
This study confirms the feasibility of leveraging unsupervised, interpretable methods with affordable, non-intrusive sensors for personalized and ecologically valid AAL. By providing clear, human-readable explanations for detected anomalies, the system enhances trustworthiness, addressing a key barrier to AAL adoption.
Ambient Assisted Living (AAL) research frequently contends with limitations, including a reliance on supervised data, a lack of personalization and interpretability, and evaluations in artificial laboratory settings [1]. These challenges hinder real-world deployment by requiring burdensome data labelling and failing to capture individual-specific routines [2]. The primary study aim was to use low-cost, multi-modal sensors for long-term, real-world activity recognition (cooking, couch-sitting, showering) and behavioural anomaly detection in a genuine home environment [3].
METHODS:
A multi-modal sensor network, including contact, vibration, smart outlet, and air quality sensors, was deployed in a single participant's apartment for over 90 days, ensuring high ecological validity. Primarily unsupervised machine learning techniques were employed. K-means clustering, validated by the Silhouette Coefficient, was used to identify appliance states (for cooking) and to cluster bathroom events (showering). Isolation Forest models were used to detect personalized behavioural deviations in each room. These models were augmented with interpretability methods (SHAP values) to explain anomaly flags. A minimally supervised Random Forest, evaluated using Accuracy, Precision, Recall, and F1-score, was also compared.
RESULTS:
The unsupervised K-means clustering for appliance states showed high cluster separation (e.g., Silhouette Scores > 0.93) and high fidelity (e.g., >99.6% agreement). The unsupervised shower model achieved a Silhouette Score of 0.867 and 99% accuracy. In comparison, the minimally supervised Random Forest model (trained on ~20 days of data) achieved 99.8% accuracy and an F1-score of 0.989. The system effectively identified interpretable anomalies, such as abnormally long non-shower bathroom stays (over 60 minutes), atypical late-night bedroom activity, and unusually long cooking durations.
CONCLUSIONS:
This study confirms the feasibility of leveraging unsupervised, interpretable methods with affordable, non-intrusive sensors for personalized and ecologically valid AAL. By providing clear, human-readable explanations for detected anomalies, the system enhances trustworthiness, addressing a key barrier to AAL adoption.
| eISSN: | 2654-1459 |
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