Clustering Home Activity Distributions for Automatic Detection of Mild Cognitive Impairment in Older Adults.

The public health implications of growing numbers of older adults at risk for dementia places pressure on identifying dementia at its earliest stages so as to develop proactive management plans. The prodromal dementia phase commonly identified as mild cognitive impairment is an important target for this early detection of impending dementia amenable to treatment. In this paper, we propose a method for home-based automatic detection of mild cognitive impairment in older adults through continuous monitoring via unobtrusive sensing technologies.

Unobtrusive Detection of Mild Cognitive Impairment in Older Adults Through Home Monitoring.

The early detection of dementias such as Alzheimer's disease can in some cases reverse, stop, or slow cognitive decline and in general greatly reduce the burden of care. This is of increasing significance as demographic studies are warning of an aging population in North America and worldwide. Various smart homes and systems have been developed to detect cognitive decline through continuous monitoring of high risk individuals.

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.

Autonomous unobtrusive detection of mild cognitive impairment in older adults.

The current diagnosis process of dementia is resulting in a high percentage of cases with delayed detection. To address this problem, in this paper, we explore the feasibility of autonomously detecting mild cognitive impairment (MCI) in the older adult population. We implement a signal processing approach equipped with a machine learning paradigm to process and analyze real-world data acquired using home-based unobtrusive sensing technologies.

Generalized Linear Models of home activity for automatic detection of mild cognitive impairment in older adults.

With a globally aging population, the burden of care of cognitively impaired older adults is becoming increasingly concerning. Instances of Alzheimer's disease and other forms of dementia are becoming ever more frequent. Earlier detection of cognitive impairment offers significant benefits, but remains difficult to do in practice.