# Depth-Weighted Detection of Behaviours of Risk in People with Dementia using Cameras

arXiv: https://arxiv.org/abs/2408.15519 · HTML: https://mlsanity.com/paper/2408.15519/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems](https://mlsanity.com/paper/2012.03448/) (2020)
- [Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection](https://mlsanity.com/paper/2209.12148/) (2022)
- [Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate](https://mlsanity.com/paper/2010.07110/) (2020)
- [Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions](https://mlsanity.com/paper/2301.00114/) (2022)
- [Privacy-Protecting Behaviours of Risk Detection in People with Dementia using Videos](https://mlsanity.com/paper/2212.10682/) (2022)
- [Minimum Variance Embedded Auto-associative Kernel Extreme Learning Machine for One-class Classification](https://mlsanity.com/paper/2011.12208/) (2020)
- [Temporal Shift -- Multi-Objective Loss Function for Improved Anomaly Fall Detection](https://mlsanity.com/paper/2311.02863/) (2023)

## Abstract (arXiv)

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 disparate importance of events based on distance. To address this issue, we proposed a novel depth-weighted loss to enforce equivalent importance to the events happening both near and far from the cameras; thus, helping to reduce false alarms. We further propose to utilize the training outliers to determine the anomaly threshold. The data from nine dementia participants across three cameras in a specialized dementia unit were used for training. The proposed approach obtained the best area under receiver operating characteristic curve performance of 0.852, 0.81 and 0.768, respectively, for the three cameras. Ablation analysis was conducted for the individual components of the proposed approach and effect of frame size and frame rate. The performance of the proposed approach was investigated for cross-camera, participant-specific and sex-specific behaviours of risk detection. The proposed approach performed reasonably well in reducing false alarms. This motivates further research to make the system more suitable for deployment in care facilities.