Multimodal Detection of Agitation in People With Dementia in Clinical Settings: Observational Pilot Study

• Published in: JMIR aging Vol. 8; p. e68156
• Main Authors: Badawi, Abeer, Elmoghazy, Somayya, Choudhury, Samira, Elgazzar, Sara, Elgazzar, Khalid, Burhan, Amer M
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 15.07.2025
• Subjects: Accuracy; Aged; Aged, 80 and over; Aggression - psychology; Artificial Intelligence; Behavior; Biomarkers; Biometrics; Camcorders; Cameras; Caregivers; Datasets; Deep Learning; Dementia; Dementia - complications; Dementia - psychology; Dementia and Cognitive Decline; Female; Geriatrics; Global health; Health sciences; Humans; Integrated approach; Machine Learning; Male; Mental health; Mental Health Issues in Elderly Patients and Geriatric Psychiatry; mHealth for Data Collection and Research; Middle Aged; Mobile Health (mhealth); Monitoring systems; Neural networks; Original Paper; Physiology; Pilot Projects; Privacy; Psychomotor Agitation - diagnosis; Sensors; Usability and Technology Use Studies with Elder Subjects; Wearable Devices and Sensors; Wearable Electronic Devices; Canada; agitation; dementia; multimodal sensing; wearable sensors; artificial intelligence; video detection
• ISSN: 2561-7605; 2561-7605
• DOI: 10.2196/68156

Dementia is a progressive neurodegenerative condition that affects millions worldwide, often accompanied by agitation and aggression (AA), which contribute to patient distress and increased health care burden. Existing assessment methods for AA rely heavily on caregiver reporting, introducing subjectivity and inconsistency. This study proposes a novel, multimodal system for predicting AA episodes in individuals with severe dementia, integrating wearable sensor data and privacy-preserving video analytics. A pilot study involving 10 participants was conducted at Ontario Shores Mental Health Institute. The system combines digital biomarkers collected from the EmbracePlus (Empatica Inc) wristband with video-based behavioral monitoring. Facial features in video frames were anonymized using a masking tool, and a deep learning model was used for AA detection. To determine optimal performance, various machine learning and deep learning models were evaluated for both wearable and video data streams. The Extra Trees model achieved up to 99% accuracy for personalized wristband data, while the multilayer perceptron model performed best in general models with 98% accuracy. For video analysis, the gated recurrent unit model achieved 95% accuracy and 99% area under the curve, and the long short-term memory model demonstrated superior response time for real-time use. Importantly, the system predicted AA episodes at least 6 minutes in advance in all participants based on wearable data. The findings demonstrate the system's potential to autonomously and accurately detect and predict AA events in real-time. This approach represents a significant advancement in the proactive management of behavioral symptoms in dementia care.