Undersampling and Cumulative Class Re-decision Methods to Improve Detection of Agitation in People with Dementia

Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver's safety at risk. Developing objective agitation detection approaches is important to support health and safety of PwD living in a residential setting. In a previous study, we collected multimodal wearable sensor data from 17 participants for 600 days and developed machine learning models for predicting agitation in one-minute windows. However, there are significant limitations in the dataset, such as imbalance problem and potential imprecise labels as the occurrence of agitation is much rarer in comparison to the normal behaviours. In this paper, we first implement different undersampling methods to eliminate the imbalance problem, and come to the conclusion that only 20% of normal behaviour data are adequate to train a competitive agitation detection model. Then, we design a weighted undersampling method to evaluate the manual labeling mechanism given the ambiguous time interval (ATI) assumption. After that, the postprocessing method of cumulative class re-decision (CCR) is proposed based on the historical sequential information and continuity characteristic of agitation, improving the decision-making performance for the potential application of agitation detection system. The results show that a combination of undersampling and CCR improves best F1-score by 26.6% and other metrics to varying degrees with less training time and data used, and inspires a way to find the potential range of optimal threshold reference for clinical purpose.

Privacy-Protecting Behaviours of Risk Detection in People with Dementia using Videos

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, analyzing raw videos can also raise privacy concerns. In this paper, we present two novel privacy-protecting video-based anomaly detection approaches to detect behaviours of risks in people with dementia. We either extracted body pose information as skeletons and use 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. We show our results on a real-world study conducted in a dementia care unit with patients with dementia, containing approximately 21 hours of normal activities data for training and 9 hours of data containing normal and behaviours of risk events for testing. We compared our approaches with the original RGB videos and obtained an equivalent 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. This is one of the first studies to incorporate privacy for the detection of behaviours of risks in people with dementia.

Multi Visual Modality Fall Detection Dataset

Falls are one of the leading cause of injury-related deaths among the elderly worldwide. Effective detection of falls can reduce the risk of complications and injuries. Fall detection can be performed using wearable devices or ambient sensors; these methods may struggle with user compliance issues or false alarms. Video cameras provide a passive alternative; however, regular RGB cameras are impacted by changing lighting conditions and privacy concerns. From a machine learning perspective, developing an effective fall detection system is challenging because of the rarity and variability of falls. Many existing fall detection datasets lack important real-world considerations, such as varied lighting, continuous activities of daily living (ADLs), and camera placement. The lack of these considerations makes it difficult to develop predictive models that can operate effectively in the real world. To address these limitations, we introduce a novel multi-modality dataset (MUVIM) that contains four visual modalities: infra-red, depth, RGB and thermal cameras. These modalities offer benefits such as obfuscated facial features and improved performance in low-light conditions. We formulated fall detection as an anomaly detection problem, in which a customized spatio-temporal convolutional autoencoder was trained only on ADLs so that a fall would increase the reconstruction error. Our results showed that infra-red cameras provided the highest level of performance (AUC ROC=0.94), followed by thermal (AUC ROC=0.87), depth (AUC ROC=0.86) and RGB (AUC ROC=0.83). This research provides a unique opportunity to analyze the utility of camera modalities in detecting falls in a home setting while balancing performance, passiveness, and privacy.

Tracking agitation in people living with dementia in a care environment

Agitation is a symptom that communicates distress in people living with dementia (PwD), and that can place them and others at risk. In a long term care (LTC) environment, care staff track and document these symptoms as a way to detect when there has been a change in resident status to assess risk, and to monitor for response to interventions. However, this documentation can be time-consuming, and due to staffing constraints, episodes of agitation may go unobserved. This brings into question the reliability of these assessments, and presents an opportunity for technology to help track and monitor behavioural symptoms in dementia. In this paper, we present the outcomes of a 2 year real-world study performed in a dementia unit, where a multi-modal wearable device was worn by $20$ PwD. In line with a commonly used clinical documentation tool, this large multi-modal time-series data was analyzed to track the presence of episodes of agitation in 8-hour nursing shifts. The development of a baseline classification model (AUC=0.717) on this dataset and subsequent improvement (AUC= 0.779) lays the groundwork for automating the process of annotating agitation events in nursing charts.