Wandering is a common behavioral disorder in the community-dwelling elderly. More than two-thirds of caregivers believe that wandering would cause falls. While physical restraint is a common measure ...to address wandering, it could trigger challenging behavior in approximately 80% of the elderly with dementia. This study aims to develop a virtual restraint using a night monitoring system (eNightLog) to provide a safe environment for the elderly and mitigate the caregiver burden. The eNightLog system consisted of remote sensors, including a near infra-red 3D time-of-flight sensor and ultrawideband sensors. An alarm system was controlled by customized software and algorithm based on the respiration rate and body posture of the elderly. The performance of the eNightLog system was evaluated in both single and double bed settings by comparing to that of a pressure mat and an infrared fence system, under simulated bed-exiting scenarios. The accuracy and precision for the three systems were 99.0%, 98.8%, 85.9% and 99.2%, 97.8%, 78.6%, respectively. With higher accuracy, precision, and a lower false alarm rate, eNightLog demonstrated its potential as an alternative to physical restraint to remedy the workload of the caregivers and the psychological impact of the elderly.
Older people are increasingly dependent on others to support their daily activities due to geriatric symptoms such as dementia. Some of them stay in long-term care facilities. Elderly people with ...night wandering behaviour may lose their way, leading to a significant risk of injuries. The eNightLog system was developed to monitor the night-time bedside activities of older people in order to help them cope with this issue. It comprises a 3D time-of-flight near-infrared sensor and an ultra-wideband sensor for detecting human presence and to determine postures without a video camera. A threshold-based algorithm was developed to classify different activities, such as leaving the bed. The system is able to send alarm messages to caregivers if an elderly user performs undesirable activities. In this study, 17 sets of eNightLog systems were installed in an elderly hostel with 17 beds in 9 bedrooms. During the three-month field test, 26 older people with different periods of stay were included in the study. The accuracy, sensitivity and specificity of detecting non-assisted bed-leaving events was 99.8%, 100%, and 99.6%, respectively. There were only three false alarms out of 2762 bed-exiting events. Our results demonstrated that the eNightLog system is sufficiently accurate to be applied in the hostel environment. Machine learning with instance segmentation and online learning will enable the system to be used for widely different environments and people, with improvements to be made in future studies.
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