Deep-Learning-Based System for Assisting People with Alzheimer’s Disease

• Published in: Electronics (Basel) Vol. 11; no. 19; p. 3229
• Main Authors: Munteanu, Dan, Bejan, Catalina, Munteanu, Nicoleta, Zamfir, Cristina, Vasic, Mile, Petrea, Stefan-Mihai, Cristea, Dragos
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Activity recognition; Algorithms; Alzheimer's disease; Artificial intelligence; Brain research; Cameras; Care and treatment; Caregivers; Computer vision; Data mining; Deep learning; Dehydration; Dementia; Image classification; Machine learning; Machine vision; Malnutrition; Meals; Medical imaging; Memory; Neural networks; Nutrition; Object recognition; Patients; Smartphones; Streaming media; Surveillance; Training; Verbal communication; Video data; Romania
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11193229

People with Alzheimer’s disease are at risk of malnutrition, overeating, and dehydration because short-term memory loss can lead to confusion. They need a caregiver to ensure they adhere to the main meals of the day and are properly hydrated. The purpose of this paper is to present an artificial intelligence system prototype based on deep learning algorithms aiming to help Alzheimer’s disease patients regain part of the normal individual comfort and independence. The proposed system uses artificial intelligence to recognize human activity in video, being able to identify the times when the monitored person is feeding or hydrating, reminding them using audio messages that they forgot to eat or drink or that they ate too much. It also allows for the remote supervision and management of the nutrition program by a caregiver. The paper includes the study, search, training, and use of models and algorithms specific to the field of deep learning applied to computer vision to classify images, detect objects in images, and recognize human activity video streams. This research shows that, even using standard computational hardware, neural networks’ training provided good predictive capabilities for the models (image classification 96%, object detection 74%, and activity analysis 78%), with the training performed in less than 48 h, while the resulting model deployed on the portable development board offered fast response times—that is, two seconds. Thus, the current study emphasizes the importance of artificial intelligence used in helping both people with Alzheimer’s disease and their caregivers, filling an empty slot in the smart assistance software domain.