Jonathan Lin is a post-doctorate fellow in the Adaptive Systems Lab. His research focues on the development of human motion pose estimation, motion segmentation, and metric tracking by applying techniques from machine learning and optimal control to human motion analysis.
In 2010, Dr. Lin received the B.A.Sc. degree in Electrical Engineering at the University of Waterloo. In 2012, Dr. Lin received the M.A.Sc. degree in Electrical Engineering at the University of Waterloo under the supervision of Dr. Dana Kulić, where his research focused on motion tracking using inertial measurement units (IMUs) and automated motion segmentation in real-time for rehabilitation clinical applications. He used IMUs to estimate lower-body joint angles using the extended Kalman filter (EKF) and employed hidden Markov models (HMMs) in conjunction with velocity profiles to perform motion segmentation.
In 2017, Dr. Lin received the Ph.D. degree in Electrical Engineering at the University of Waterloo under the supervision of Dr. Dana Kulić, where his research focused on using machine learning to detect segment edge points to reduce dependency on feature-specific characteristics such as zero-velocity crossings. He also investigated applications of features derived from optimal control, where the cost function of a given motion trajectory can be extracted and used for segmentation purposes.