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%.