Hand keypoints detection and pose estimation has numerous applications in
computer vision, but it is still an unsolved problem in many aspects. An
application of hand keypoints detection is in performing cognitive assessments
of a subject by observing the performance of that subject in physical tasks
involving rapid finger motion...
As a part of this work, we introduce a novel
hand key-points benchmark dataset that consists of hand gestures recorded
specifically for cognitive behavior monitoring. We explore the state of the art
methods in hand keypoint detection and we provide quantitative evaluations for
the performance of these methods on our dataset. In future, these results and
our dataset can serve as a useful benchmark for hand keypoint recognition for
rapid finger movements.