Comparison of LSTM and Transformer Models for Activities of Daily Living Recognition using In-Home Ambient Sensors

Abstract This study evaluated Long Short-Term Memory (LSTM) and Transformer artificial intelligence (AI) models for recognizing Activities of Daily Living (ADLs) using data collected from a low-cost, non-invasive ambient in-home sensor system. Motion, temperature, luminance, and door-contact sensors were deployed in a two-participant home for 22 days, with ground truth established through volunteer logs and expert validation. Missing data were handled using Akima and linear interpolation. Models were trained using a 16/3-day train–validation split with the last three days reserved for testing to avoid temporal leakage. Performance assessments included participant-specific modeling, sequence-length variation, and hyperparameter tuning, using micro-accuracy and AUC-ROC as evaluation metrics. The Transformer consistently outperformed the LSTM, particularly for Participant 2 (AUC 0.91 vs. 0.87), and demonstrated superior adaptability to irregular time-series data. Findings underscore the feasibility of combining ambient sensing with AI for accurate ADL recognition and its potential to enable early detection of cognitive decline, reduce hospitalization risk, and alleviate caregiver burden.