Machine learning based identification of elderly persons with cognitive impairment using dynamic time warping - Kondragunta Jyothsna (De Gruyter, 2020)
Cognitive changes in general occur with normal aging. This may lead to the prevalence and effect of age associated diseases. The understanding and identification of these age-related cognitive impairments is an important aspect in elderly population. This leads in the simple case, supporting a functional independence of the elderly and in a complex case, an early identification of dementia in advance. One important change with normal aging is the decline in gait functionality. The decline in gait is more visible in the elderly with more cognitive impairment during dual cognitive tasks, multi-tasking exercises. For the classification of the healthy elderly from the elderly having cognitive impairments, the gait data of the elderly is acquired through Kinect V2. A waking trial of 5m long is used to collect the gait data. 3D based pose estimation using the depth data is performed. Gait parameters and gait cycles of the individual elderly are estimated. In this paper, Dynamic Time Warping (DTW) algorithm is used to compare the patterns of the gait cycles of the individual in different trails such as Regular Gait 1 (RG1), Regular Gait 2 (RG2), Counting Backward 1 (CB1), Counting Backward 3 (CB3), Fast Gait (FG) and Words with Special Letters (WSPL). The identified cross levels along with the estimated gait parameters are used for training the machine learning algorithm. Support Vector Machines (SVM) were used for the classification of the elderly persons with or without cognitive impairments. The experiment results proved that such a classification of cognitive impairment levels using 3D pose estimation and machine learning helps in future for the identification of dementia in advance.