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We present the development and evaluation of a hand tracking algorithm basedon single depth images captured from an overhead perspective for use in theCOACH prompting system. We train a random decision forest body part classifierusing approximately 5,000 manually labeled, unbalanced, partially labeledtraining images. The classifier represents a random subset of pixels in eachdepth image with a learned probability density function across all trained bodyparts. A local mode-find approach is used to search for clusters present in theunderlying feature space sampled by the classified pixels. In each frame, bodypart positions are chosen as the mode with the highest confidence. User handpositions are translated into hand washing task actions based on proximity toenvironmental objects. We validate the performance of the classifier and taskaction proposals on a large set of approximately 24,000 manually labeledimages.

Depth image hand tracking from an overhead perspective using partially labeled, unbalanced data: Development and real-world testing