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Automatic assessment of cognitive impairment through electronic observation of object usage
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
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In this paper, we describe an approach to developing an ecologically valid framework for performing automated cognitive assessment. To automate assessment, we use a machine learning approach that builds a model of cognitive health based on observations of activity performance and uses lab-based assessment to provide ground truth for training and testing the learning algorithm. To evaluate our approach, we recruited older adults to perform a set of activities in our smart home test-bed. While participants perform activities, sensors placed in the smart home unobtrusively capture the progress of the activity. During analysis, we extract features that indicate how well participants perform the activities. Our machine-learning algorithm accepts these features as input and outputs the cognitive status of the participants as belonging to one of two groups: Cognitively healthy or Dementia. We conclude that machine-learning algorithms can distinguish between cognitively healthy older adults and older adults with dementia given adequate features that represent how well they have performed the activity.