TY - GEN
T1 - Measuring Glide-Reflection Symmetry in Human Movements Using Elastic Shape Analysis
AU - Wang, Qiao
AU - Potaraju, Chaitanya
AU - Turaga, Pavan
N1 - Funding Information:
This research was supported by NSF grant 1617999.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/22
Y1 - 2017/8/22
N2 - Human bodies and movements exhibit inherent symmetry. However, an important class of everyday movements, such as walking, does not maintain symmetry at every time instance. The symmetry in these movements is a spatiotemporal glide-reflection symmetry. The ability to measure this type of symmetry will provide us opportunities for various computer-aided applications including health monitoring, rehabilitation, and athletic training. In this paper we propose a method that uses the tools from elastic shape analysis to provide continuous symmetry scores which measure the degree of glide-reflection symmetry in movements. These scores can be updated online after each frame, and easily combined to drive comprehensible feedback. Our preliminary experiment demonstrates that our symmetry scores can well distinguish between a normal gait and simulated stroke and Parkinsonian gaits. Our results also suggest that using the Riemannian elastic metric provides better scores than Euclidean approaches.
AB - Human bodies and movements exhibit inherent symmetry. However, an important class of everyday movements, such as walking, does not maintain symmetry at every time instance. The symmetry in these movements is a spatiotemporal glide-reflection symmetry. The ability to measure this type of symmetry will provide us opportunities for various computer-aided applications including health monitoring, rehabilitation, and athletic training. In this paper we propose a method that uses the tools from elastic shape analysis to provide continuous symmetry scores which measure the degree of glide-reflection symmetry in movements. These scores can be updated online after each frame, and easily combined to drive comprehensible feedback. Our preliminary experiment demonstrates that our symmetry scores can well distinguish between a normal gait and simulated stroke and Parkinsonian gaits. Our results also suggest that using the Riemannian elastic metric provides better scores than Euclidean approaches.
UR - http://www.scopus.com/inward/record.url?scp=85030219457&partnerID=8YFLogxK
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U2 - 10.1109/CVPRW.2017.100
DO - 10.1109/CVPRW.2017.100
M3 - Conference contribution
AN - SCOPUS:85030219457
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 709
EP - 716
BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
PB - IEEE Computer Society
T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
Y2 - 21 July 2017 through 26 July 2017
ER -