Estimating in-home walking speed distributions for unobtrusive detection of mild cognitive impairment in older adults.

Timely recognition of cognitive impairment such as Alzheimer's disease is of great significance. Many smart systems, developed to continuously monitor older adults' health and cognition, use a number of predefined measures associated with the older adults' activity in their homes. However, this approach has been demonstrated to focus on idiosyncratic nuances of the individual subjects, and thus could potentially not perform as well when tested on new subjects. In this paper, we address this problem by building proper statistical models of older adults' in-home walking speed. Using the data pertaining to 15 older adults monitored for an average period of 3 years, we used linear regression with a Gaussian likelihood to model the subjects' in-home walking speed, and we used dynamic time warping to demonstrate significant difference between the walking speed distributions of the subjects when cognitively intact and when having mild cognitive impairment (MCI). Using a simple thresholding approach of the dynamic time warping costs, we were able to detect MCI in older adults with areas under the ROC curve and the precision-recall curve of 0.906 and 0.790, respectively, using a time frame of 12 weeks.