Pose Estimation and Movement Detection for Mobility Assessment of Elderly People in an Ambient Assisted Living Application

Abstract

In European countries, the increasing number of elderly with dementia causes serious problems for the society, especially with regard to the caring sector. As technical support systems can be of assistance to caregivers and patients, a mobility assessment system for demented people is presented. The grade of mobility is measured by means of the person's pose and movements in a monitored area. For this purpose, pose estimation and movement detection algorithms have been developed. These algorithms process 3-D data, which are provided by an optical stereo sensor installed in a living environment. The experiments demonstrated that the algorithms work robustly. In connection with a human machine interface, the system facilitates a mobilisation as well as a more valid assessment of the patient's medical condition than it is presently the case. Moreover, recent advances with regard to action recognition as well as an outlook about necessary developments are presented.

Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia

“…Previous studies which address mobility assessment of older adults in assisted living homes have made use of human pose estimation to classify of movement type such as if the person is standing, sitting, or lying [32]. Other studies have used specialized depth sensors such as the Microsoft Kinect v2 sensor and its skeleton tracking capabilities to measure spatiotemporal and kinematic variables of gait [33][34][35].…”