Automatic segmentation of video to aid the study of faucet usability for older adults

Assessing the usability of objects for a specific population can be laborious and time consuming. Furthermore, for the older adult population, the usability of objects involved in the completion of tasks of daily living is critical to ‘aging-in-place’ and the preservation of independence. This paper explores the automation of the process of observing older adults with Alzheimer's as they use various types of faucets. Features extracted from video and audio signals encode the subjects' progression through a hand-washing task and temporal segmentation is used to determine the state of the process at each video frame. Histograms of optical flow, a hand-tracking particle filter, and a water detection algorithm are used to extract features encoding the state of the handwashing process. A Hidden Markov Support Vector Machine is used to label each video frame of the handwashing process as belonging to one of five states with an overall accuracy of 93.58%.

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