Affiliation:
1. The University of Edinburgh, UK
Abstract
A new application for real-time monitoring of the lack of movement in older adults’ own homes is proposed, aiming to support people’s lives and independence in their later years. A lightweight camera monitoring system, based on an RGB-D camera and a compact computer processor, was developed and piloted in community homes to observe the daily behavior of older adults. Instances of body inactivity were detected in everyday scenarios anonymously and unobtrusively. These events can be explained at a higher level, such as a loss of consciousness or physiological deterioration. The accuracy of the inactivity monitoring system is assessed, and statistics of inactivity events related to the daily behavior of older adults are provided. The results demonstrate that our method achieves high accuracy in inactivity detection across various environments and camera views. It outperforms existing state-of-the-art vision-based models in challenging conditions like dim room lighting and TV flickering. However, the proposed method does require some ambient light to function effectively.
Publisher
Association for Computing Machinery (ACM)
Reference37 articles.
1. Jack Andrews, Meghana Kowsika, Asad Vakil, and Jia Li. 2020. A motion induced passive infrared (PIR) sensor for stationary human occupancy detection. In 2020 IEEE/ION position, location and navigation symposium (PLANS). IEEE, 1295–1304.
2. Comparative study of background subtraction algorithms
3. Accuracy and repeatability of the Microsoft Azure Kinect for clinical measurement of motor function
4. John T Cacioppo and Stephanie Cacioppo. 2014. Older adults reporting social isolation or loneliness show poorer cognitive function 4 years later. Evidence-based nursing 17, 2 (2014), 59–60.
5. WiFi-Based Detection of Human Subtle Motion for Health Applications