Unsupervised Deep Learning to Detect Agitation From Videos in People With Dementia

Behavioural symptoms of dementia present a significant risk within Long Term Care (LTC) homes, which face difficulties supporting residents and monitoring their safety with limited staffing resources. Many LTC facilities have installed video surveillance systems in common areas that can help staff to observe residents; however, typically these video streams are not monitored. In this paper, we present the development of a computer vision algorithm to use these video streams to detect episodes of clinically important agitation in people with dementia. Given that episodes of agitation are rare in comparison to normal behaviours, we formulated this as an anomaly detection problem. This involves using the video camera to monitor the scene rather than tracking individuals. We developed a customized spatio-temporal convolution autoencoder that is trained on the normal behaviours and then identified agitation during testing as anomalous behaviour. We present a proof-of-concept using video data collected from a specialized dementia unit and annotated for agitation events. We trained the unsupervised neural network on approximately 24 hours of normal activities and tested on 11 hours of videos containing both normal activities and agitation events, and obtained an area under the curve of the receiver operating characteristic curve of 0.754. This research paves the way for leveraging existing surveillance infrastructure in LTC and other mental health settings to detect agitation or aggression, with the potential for improved health and safety.