Privacy-protecting behaviours of risk detection in people with dementia using videos

BioMedical Engineering OnLine

Research

Kristine Newman¹  Pratik K. Mishra²  Alex Mihailidis²  Shehroz S. Khan²  Bing Ye³  Andrea Iaboni⁴ 

[1] Daphne Cockwell School of Nursing, Ryerson University, Toronto, Canada;Institute of Biomedical Engineering, University of Toronto, Toronto, Canada;KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Canada;KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Canada;KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Canada;Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada;

关键词: Skeleton;    Semantic segmentation;    Behaviours of risk;    People with dementia;    Convolutional autoencoder;    Anomaly detection;    Video;   

DOI  :  10.1186/s12938-023-01065-3

 received in 2022-11-13, accepted in 2023-01-09,  发布年份 2023

来源: Springer

【 摘 要 】

Background People living with dementia often exhibit behavioural and psychological symptoms of dementia that can put their and others’ safety at risk. Existing video surveillance systems in long-term care facilities can be used to monitor such behaviours of risk to alert the staff to prevent potential injuries or death in some cases. However, these behaviours of risk events are heterogeneous and infrequent in comparison to normal events. Moreover, analysing raw videos can also raise privacy concerns. Purpose In this paper, we present two novel privacy-protecting video-based anomaly detection approaches to detect behaviours of risks in people with dementia. Methods We either extracted body pose information as skeletons or used semantic segmentation masks to replace multiple humans in the scene with their semantic boundaries. Our work differs from most existing approaches for video anomaly detection that focus on appearance-based features, which can put the privacy of a person at risk and is also susceptible to pixel-based noise, including illumination and viewing direction. We used anonymized videos of normal activities to train customized spatio-temporal convolutional autoencoders and identify behaviours of risk as anomalies. Results We showed our results on a real-world study conducted in a dementia care unit with patients with dementia, containing approximately 21 h of normal activities data for training and 9 h of data containing normal and behaviours of risk events for testing. We compared our approaches with the original RGB videos and obtained a similar area under the receiver operating characteristic curve performance of 0.807 for the skeleton-based approach and 0.823 for the segmentation mask-based approach. Conclusions This is one of the first studies to incorporate privacy for the detection of behaviours of risks in people with dementia. Our research opens up new avenues to reduce injuries in long-term care homes, improve the quality of life of residents, and design privacy-aware approaches for people living in the community.

【 授权许可】

CC BY   
© The Author(s) 2023

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