Related Experiment Video
Updated: Jul 10, 2026

Using Motion Capture Technology in the Instrumented Timed Up and Go Test to Detect the Risk of Falling in Aged Adults
Published on: October 25, 2024
Intelligent video monitoring to improve safety of older persons
Datong Chen1, Ashok J Bharucha, Howard D Wactlar
1Computer Scientist Department at Carnegie Mellon University, Pittsburgh, PA 15213, USA. datong@cs.cmu.edu
Abstract:
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.
