HMM assessment of quality of movement trajectory in laparoscopic surgery
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Generalization of a vision-based computational model of mind-reading
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Instructing people for training gestural interactive systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ClimbAX: skill assessment for climbing enthusiasts
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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Watching athletes allows coaches to provide both vital feedback on how well they are performing and on ways to improve their technique without causing or aggravating injuries. The thoroughness and accuracy of this traditional observation method are limited by human ability and availability. Supplementing coaches with sensor systems that generate accurate feedback on any technical aspect of the performance gives athletes a fall back if they do not have enough confidence in their coach's assessment. A system is presented to model the quality of arbitrary aspects of rowing technique found to be inconsistently well performed by a set of novice rowers when using an ergometer. Using only the motion of the handle, tracked using a high-fidelity motion capture system, a coach trains the system with their idea of the skill-level exhibited during each performance, by labeling example trajectories. Misclassification of unseen performances is encouragingly low, even for unknown performers.