TY - GEN
T1 - Riemannian Geometric Approaches for Measuring Movement Quality
AU - Som, Anirudh
AU - Anirudh, Rushil
AU - Wang, Qiao
AU - Turaga, Pavan
N1 - Funding Information:
This work was supported by NSF grant 1320267.
PY - 2016/12/16
Y1 - 2016/12/16
N2 - A growing set of applications in home-based interactive physical therapy require the ability to monitor, inform and assess the quality of everyday movements. Interactive therapy requires both real-time feedback of movement quality, as well as summative feedback of quality over a period of time. Obtaining labeled data from trained experts is the main limitation, since it is both expensive and time consuming. Motivated by recent studies in motor-control, we propose an unsupervised approach that measures movement quality of simple actions by considering the deviation of a trajectory from an ideal movement path in the configuration space. We use two different configuration spaces to demonstrate this idea - the product space S1×S1 to model the interaction of two joint angles, and SE(3)×SE(3) to model the movement of two joints, for two different applications in movement quality estimation. We also describe potential applications of these ideas to assess quality in real-time.
AB - A growing set of applications in home-based interactive physical therapy require the ability to monitor, inform and assess the quality of everyday movements. Interactive therapy requires both real-time feedback of movement quality, as well as summative feedback of quality over a period of time. Obtaining labeled data from trained experts is the main limitation, since it is both expensive and time consuming. Motivated by recent studies in motor-control, we propose an unsupervised approach that measures movement quality of simple actions by considering the deviation of a trajectory from an ideal movement path in the configuration space. We use two different configuration spaces to demonstrate this idea - the product space S1×S1 to model the interaction of two joint angles, and SE(3)×SE(3) to model the movement of two joints, for two different applications in movement quality estimation. We also describe potential applications of these ideas to assess quality in real-time.
UR - http://www.scopus.com/inward/record.url?scp=85010192516&partnerID=8YFLogxK
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U2 - 10.1109/CVPRW.2016.129
DO - 10.1109/CVPRW.2016.129
M3 - Conference contribution
AN - SCOPUS:85010192516
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 1005
BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
PB - IEEE Computer Society
T2 - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
Y2 - 26 June 2016 through 1 July 2016
ER -