Toward Intelligent Homecare for Older Adults: Deep Learning-Based Activity and Routine Deviation Detection Using SDHAR-HOME Data

Abstract

The global growth of the older adult’s population highlights the urgent need for intelligent privacy-preserving homecare systems that can monitor daily activities and detect behavioral deviations. We propose a comprehensive framework that combines a Transformer-based deep learning model for human activity recognition with a rule-based, interpretable routine deviation detection system. Leveraging the SDHAR-HOME dataset, which contains multi-sensor time series data from two users, the framework first classifies daily activities using a transformer encoder and then constructs a personalized behavioral baseline to identify deviations such as missed meals, sleep disturbances, and unusual hygiene habits. Results demonstrate high classification accuracy (up to 98.5%) and validate the effectiveness of conventional monitoring methods through detailed visualization and semantic deviation labeling. This dual-strategy framework is particularly suitable for assistive monitoring applications in homecare settings.

Keywords: Homecare, Human Activity Recognition, Personalized Modeling, Deep Learning, Behavioral Monitoring

DOI: 10.54941/ahfe1006802