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Unobtrusive Detection of Mild Cognitive Impairment

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 asignal processing approach equipped with a machine learning paradigm to process and analyze real world data acquired using home-based unobtrusive sensing technologies. Using the sensor and clinical data pertaining to 97 subjects, acquired over an average period of 3 years, a number of measures associated with the subjects’ walking speed and general activity in the home were calculated.

Different time spans of these measures were used to generate feature vectors to train and test two machine learning algorithms namely support vector machines and random forests. We were able to autonomously detect MCI in older adults with an area under the ROC curve of 0.97 and an area under the precisionrecall curve of 0.93 using a time window of 24 weeks. This work is of great significance since it can potentially assist in the early detection of cognitive impairment in older adults.

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