Deep learning for early detection of mild cognitive impairment using smart home ambient sensor data

Objective This study uses a temporal convolutional network with contrastive pretraining (TCN-CL) on smart home ambient sensor data to classify older adults into healthy and mild cognitive impairment (MCI) categories. We aim to overcome limitations of traditional machine learning (ML) methods, logistic regression, and decision tree, by employing deep learning to capture complex temporal dependencies and extract high-level features. We ­hypothesize that a pre-trained deep network will improve diagnostic accuracy across diverse, multi-resident environments.