Activities of Daily Living Detection through Energy Consumption Data and Machine Learning to Support Independent Aging

Abstract

The aging population presents significant challenges for healthcare and social services, emphasizing the need for innovative solutions that support independent living. This study explores the feasibility of identifying Instrumental Activities of Daily Living (IADLs) through power consumption data collected from smart plug-based system. Using a combination of unsupervised and supervised machine learning techniques, including K-Means clustering and Long Short-Term Memory (LSTM) networks, we developed a method to classify and predict IADLs based on energy usage patterns. The REFIT dataset was used to train and validate the models, ensuring generalizability across different households. Results demonstrate that K-means clustering effectively group energy consumption patterns with Silhouette & DB algorithms in a reasonable time (Silhouette score of 0.88 and a Davies-Bouldin Index of 0.29), while LSTM models trained on monthly household data, demonstrated high rates of activities classified over time (with F1-Score of 0.99). IADLs like cooking, cleaning, and entertainment showed the highest classification accuracy due to their distinct energy features. This approach enables non-intrusive monitoring of daily routines, offering potential applications in Ambient Assisted Living (AAL) environments. Despite limitations in detecting activities without direct energy consumption, this study highlights the potential of energy-based activity recognition for promoting independent aging. Future work will focus on refining abnormal behavior detection and integrating additional contextual factors to improve accuracy.

Keywords: Ambient assisted living; Independent aging; Instrumental activities of daily living; Load monitoring; Long short-term memory; Machine learning.