Jonathan Feng-Shun Lin
University of Waterloo, Sepuluh Nopember Institute of Technology
Papers
11
Total Citations
229
H-Index
7
About
Jonathan Feng-Shun Lin’s research sits at the intersection of human movement analysis, robotics, and assistive technology, with a focus on enabling machines to understand and replicate complex human motions. His foundational work on movement primitive segmentation (106 citations) provides a framework for breaking down long sequences of human movement into smaller, analyzable components—a technique critical for applications in gesture recognition, exercise monitoring, and human-robot interaction. Lin has also made significant contributions to inverse optimal control, developing methods to infer the underlying objectives driving multi-phase and time-varying human movements, such as jumping and affective motion during functional tasks. His work on the SkyWalker robot (2022) demonstrates a practical application of these principles, designing a mobile assistive robot to support walking and sit-to-stand transfers for older adults, with real-time phase classification using 3D visual skeleton recognition (2025). With over 200 total citations, Lin’s research bridges theoretical modeling and real-world deployment, advancing both the science of human motion understanding and the development of intelligent, responsive assistive robots.
Research Focus
Key Achievements
Top Papers
- 1
- 2Inverse Optimal Control for Multiphase Cost Functions51 citations · 2019
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- 4Full-body multi-primitive segmentation using classifiers11 citations · 2014
- 5IMU based single stride identification of humans8 citations · 2013
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- 10Generalized Hebbian algorithm for wearable sensor rotation estimation3 citations · 2017