Gait Parameter Estimation of Elderly People using 3D Human Pose Estimation in Early Detection of Dementia.

Early detection of dementia is becoming increasingly important as it plays a crucial role in handling the patients and offering better treatment. Many of the recent studies concluded a tight relationship between dementia and gait disorders. For this purpose, identification of gait abnormalities is key factor. Novel technologies provide many options such as wearable and non-wearable approaches for analysis of gait. As the occurrence of dementia is more prominent in elderly people, wearable technology is considered out of scope for this work. The gait data of several elderly people over 80 years is acquired over certain intervals during the scope of the project. The elderly people are classified into three study groups namely cognitively healthy individuals (CHI), subjectively cognitively impaired persons (SCI) and possible mildly cognitively impaired persons due to inconclusive test results (pMCI) based on their cognitive status. The gait data is acquired using Kinect sensor. The acquired data consists of both RGB image sequences and depth data of the test persons. 3D human pose estimation is performed on this gait data and gait analysis is done. The transformations in the gait cycles are observed and the health condition of the individual is analyzed. From the analysis, the patterns in the gait abnormalities are correlated with the above-mentioned classification and are used in the detection of dementia in advance. The obtained results look promising and further analysis of gait parameters is under progress.