A-MAL: Automatic motion assessment learning from properly performed motions in 3D skeleton videos

Assessment of motion quality has recently gained high demand in a variety of domains. The ability to automatically assess subject motion in videos that were captured by cheap devices, such as Kinect cameras, is essential for monitoring clinical rehabilitation processes, for improving motor skills and for motion learning tasks. The need to pay attention to low-level details while accurately tracking the motion stages, makes this task very challenging. In this work, we introduce A-MAL, an automatic, strong motion assessment learning algorithm that only learns from properly-performed motion videos without further annotations, powered by a deviation time-segmentation algorithm, a parameter relevance detection algorithm, a novel time-warping algorithm that is based on automatic detection of common temporal points-of-interest and a textual-feedback generation mechanism. We demonstrate our method on motions from the Fugl-Meyer Assessment (FMA) test, which is typically held by occupational therapists in order to monitor patients' recovery processes after strokes.