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.
|Title of host publication||Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - Oct 2019|
|Event||17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 - Seoul, Korea, Republic of|
Duration: 27 Oct 2019 → 28 Oct 2019
|Name||Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019|
|Conference||17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019|
|Country/Territory||Korea, Republic of|
|Period||27/10/19 → 28/10/19|
Bibliographical notePublisher Copyright:
© 2019 IEEE.
ASJC Scopus subject areas
- Computer Science Applications
- Computer Vision and Pattern Recognition