Title: Accurate Measurement and Modeling of Human Motion for Rehabilitation and Sports Training Abstract: In this talk, I will describe our work developing systems for on-line measurement and analysis of human movement that can be used to provide feedback to patients and clinicians/coaches during the performance of rehabilitation and sports training exercises. The system consists of wearable inertial measurement unit (IMU) sensors attached to the patient’s limbs. The IMU data is processed to estimate joint positions. I will describe an approach to improve the accuracy of pose estimation via on-line learning of the dynamic model of the movement, customized to each patient. Next, the pose data is segmented into exercise segments, identifying the start and end of each motion repetition automatically. We have traditionally performed this task using kinematic features; recently we have been investigating the use of inverse optimal control to provide segmentation hypotheses. The pose and segmentation data is visualized in a user interface, allowing the patient to simultaneously view their own movement overlaid with an animation of the ideal movement. We will present results of user studies analyzing the system capabilities for gait measurement of stroke patients undergoing gait rehabilitation, and demonstrating the significant benefits of feedback with patients undergoing rehabilitation following hip and knee replacement surgery. We will also highlight recent results applying deep learning techniques to understand and classify large-scale sports training data.

Bio: Dana Kulić received the combined B.A.Sc. and M.Eng. degrees in electromechanical engineering, and the Ph.D. degree in mechanical engineering from the University of British Columbia, Canada, in 1998 and 2005, respectively. From 2006 to 2009, she was a JSPS Postdoctoral Fellow and a Project Assistant Professor at the Nakamura Laboratory at the University of Tokyo. She is currently an Associate Professor at the Electrical and Computer Engineering Department at the University of Waterloo, Canada. She is a founding co-chair of the IEEE RAS Technical Committee on Human Movement Understanding and an Associate Editor with the IEEE Transactions on Robotics.In 2014, she was awarded Ontario’s Early Researcher award for her work on rehabilitation and human-robot interaction. Her research interests include human motion analysis, robot learning, humanoid robots, and human-machine interaction.