The behavioural and psychological symptoms of dementia, such as agitation and aggression, present a significant health and safety risk in residential care settings. Many care facilities have video cameras in place for digital monitoring of public spaces, which can be leveraged to develop an automated behaviours of risk detection system that can alert the staff to enable timely intervention and prevent the situation from escalating. However, one of the challenges in our previous study was the presence of false alarms due to obstruction of view by activities happening close to the camera. To address this issue, we proposed a novel depth-weighted loss function to train a customized convolutional autoencoder to enforce equivalent importance to the events happening both near and far from the cameras; thus, helping to reduce false alarms and making the method more suitable for real-world deployment. The proposed method was trained using data from nine participants with dementia across three cameras situated in a specialized dementia unit and achieved an area under the curve of receiver operating characteristic of $0.852$, $0.81$ and $0.768$ for the three cameras. Ablation analysis was conducted for the individual components of the proposed method and the performance of the proposed method was investigated for participant-specific and sex-specific behaviours of risk detection. The proposed method performed reasonably well in detecting behaviours of risk in people with dementia motivating further research toward the development of a behaviours of risk detection system suitable for deployment in video surveillance systems in care facilities.

本研究解决了在老年痴呆症护理中，由于行为表现如焦虑和攻击性导致的健康与安全风险的问题。我们提出了一种新颖的深度加权损失函数，训练定制的卷积自编码器，从而减少因视野被遮挡引起的误报，提高了风险行为检测的可靠性，显示出在实际监控系统中的应用潜力。

基于深度加权的老年痴呆症患者风险行为检测研究