Intelligent video monitoring to improve safety of older persons.
This paper presents the application of computer vision and machine learning technologies to a clinical task of paramount importance, improving safety of older persons. We propose an intelligent monitoring system equipped with a camera network and an automatic elopement detection algorithm to reduce the risks of un-witnessed elopements from a dementia unit in order to avoid their potential catastrophic consequences. The camera network employs 23 cameras to record daily activities in our test bed, which includes 15 residents, 4 registered and licensed practical nurses and a number of certified nursing assistants. An elopement detector is then built by using computer vision algorithms and a machine learning algorithm to automatically detect elopements and alert caregivers. The experiments demonstrate that the proposed system leverages the advantages of monitoring from multiple cameras and is able to detect elopements with almost 100% accuracy.
Chen
Datong
D
Computer Scientist Department at Carnegie Mellon University, Pittsburgh, PA 15213, USA. datong@cs.cmu.edu
Bharucha
Ashok J
AJ
Wactlar
Howard D
HD
D000203:Activities of Daily Living; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003704:Dementia; D005260:Female; D006707:Homes for the Aged; D006801:Humans; D008297:Male; D008991:Monitoring, Physiologic; D010363:Pattern Recognition, Automated; D012449:Safety; D013690:Television