I study how humans produce and control movement, and I build computational and wearable tools to measure it outside the lab. My research spans neuromechanics, rehabilitation robotics, markerless motion capture, and wearable sensing, with applications in clinical rehabilitation, injury prevention, and sport performance.
I hold a PhD in Engineering Science from Simon Fraser University and have held research positions at Harvard, Meta, Lululemon, and UBC. My work has appeared in Science Translational Medicine (1000+ citations), Nature Scientific Reports, and PLoS ONE. I currently teach biomechanics at UBC and supervise undergraduate thesis projects in computer vision and ML.
My research combines experimental biomechanics, computer vision, and machine learning to democratize access to movement analysis. I believe everyone deserves access to high-quality movement data — not just those with access to expensive labs. I build open-source tools that bring research-grade analysis to any camera, any clinic, and any community. I am passionate about creating opportunities for students to develop new technologies and address fundamental questions about how humans move.
Standard video cameras are everywhere — in phones, clinics, and gyms. I use video as the primary medium for collecting movement data, then apply computer vision and machine learning to extract physiological metrics that traditionally require expensive lab equipment: ground reaction forces, joint kinematics, and full-body kinetics. The goal is research-grade biomechanics from a single camera.
Video-driven health tools for real-world deployment. 3D body scanning from pose estimation enables rapid anatomical reconstruction. Real-time biofeedback systems support fall prevention, stroke recovery, and sport rehabilitation.
Characterizing how the nervous system controls leg forces and adapts across the lifespan. Custom experimental rigs quantify force control limits. Physics-based models reveal fundamental constraints on movement performance.
Wearable robot improving walking speed and distance after stroke. Published in Science Translational Medicine. 1000+ citations. IEEE ICRA Best Paper Award.
Wearable biofeedback device for real-time biomechanics analysis during ACL rehabilitation. $65K in funding secured. LiDAR and IMU sensor fusion.
Physics-based models predicting ground reaction forces during vertical jumping. Revealed force-velocity constraints on performance.
Multiple deep neural network architectures benchmarked for automated ECG signal classification on the MIT-BIH database.
12 oral presentations at international conferences. Posters and symposium presentations listed separately.
Invited lectures and guest talks at universities and research programs.
19 conference posters from ISB, Dynamic Walking, IEEE BioRob, ASB, WeRob, NCM, and other venues. Click any poster to view full size.
Technical reports from graduate coursework and independent research projects.
A review of deep learning frameworks for markerless motion capture, pose estimation, and video-based biomechanical analysis.
Compared neural network architectures for classifying pathological ECG signals, evaluated data augmentation and transfer learning approaches.
Developed computational models to measure the forces your body produces when walking, running, and jumping — without needing expensive lab equipment. Combined biomechanical modelling, machine learning, and wearable sensors to predict ground reaction forces from simple motion data.
Built a low-cost system using a Microsoft Kinect depth camera to estimate the mass, center of mass, and inertia of individual body segments — measurements traditionally requiring expensive motion capture labs. Validated the approach against gold-standard methods for use in clinical and field settings.
I design courses that connect engineering fundamentals to real research problems. Students collect and analyze their own biomechanics data, build computational models, and present findings — the same workflow they will use as engineers and researchers. I also supervise undergraduate thesis projects in computer vision and machine learning.
Application of mechanics to biological systems. Three major units: statics in biomechanics (free body diagrams, joint forces, muscle force estimation), dynamics in biomechanics (kinematics, kinetics, inverse dynamics, gait analysis), and tissue mechanics (bone, cartilage, ligament, tendon). Developed original content and organized 5 hands-on labs.
Advanced biomechanics: 3D rigid-body statics and dynamics, 3D gait analysis, indeterminate systems and optimization, biological tissue mechanics (ligaments, tendons, bone, cartilage, spinal discs), computational modeling (musculoskeletal and finite element), and biomechanical experimental methods. Flipped classroom with group activities. Developed original content and organized 4 labs.
Supervised student research projects across computer vision, pose estimation, and biomechanics. Students proposed research questions, conducted literature reviews, collected and analyzed data, and presented findings. Deliverables included a research proposal, final report, and oral presentation.
Co-instructed course covering human anatomy and physiology from an engineering perspective. Integrated structure-function relationships across the musculoskeletal, cardiovascular, and nervous systems.
Supervised engineering design groups through fourth-year capstone projects. Students developed biomedical devices from needs finding through functional prototype, including design controls, user testing, and stakeholder presentations.
Teaching assistant for 11 terms. Covered neural basis of movement control, rehabilitation strategies for neurological conditions, and motor learning principles. Supervised labs and led tutorial sessions.
Laboratory instructor for graduate course covering experimental design, physiological data acquisition, signal processing, and statistical analysis of human physiology data.
Laboratory instructor covering anthropometric measurement techniques, body composition analysis, and estimation of body segment parameters for biomechanical modeling.
Facilitated first-year engineering design teams through structured design process. Guided students in problem scoping, prototyping, and technical communication.
Led tutorial sessions teaching programming fundamentals to first-year engineering students. Covered problem solving, algorithm design, and implementation in MATLAB.
Supervised 10+ undergraduate thesis students across UBC, SFU, and Harvard. Projects span computer vision for sports biomechanics, pose estimation, golf kinematics, exosuit gait analysis, and wearable sensor validation.
More details coming soon.
I combine biomechanics, engineering, and data-driven modeling to augment, restore, and deepen our understanding of human mobility. Over 10+ years at Meta, Harvard, Lululemon, and UBC, I have collected data on 1000+ participants across 15+ research protocols and co-authored findings in Science Translational Medicine with 1000+ citations.
I earned my PhD in Engineering Science from Simon Fraser University, where I developed experiments in the Locomotion Lab to quantify the nervous system's control of external forces. Before that, I worked as a research engineer at Harvard University's Biodesign Lab and the Wyss Institute for Biologically Inspired Engineering, quantifying the rehabilitative effects of soft robotic exosuits for stroke survivors.
I co-founded Core Motion, a medical device startup building wearable biofeedback for ACL rehabilitation ($65,000 in funding secured). I also teach biomechanics at UBC and supervise undergraduate research projects in computer vision and machine learning.
Outside the lab, I pursue endurance sports: trail ultra-marathons, ski mountaineering, climbing, and mountain biking in British Columbia's Coast Mountains.
Developing wearable IMU-based systems for clinical gait assessment and fall risk prediction in older adults.
Led research on sport injury biomechanics and co-founded Core Motion, a wearable biofeedback device for ACL rehabilitation.
Built computational models to predict ground reaction forces during walking, running, and jumping using wearable sensors and machine learning — removing the need for expensive force plates.
Developed a low-cost depth-camera system to estimate body segment mass and inertia properties, validated against gold-standard methods for clinical use.
Quantified the rehabilitative effects of soft robotic exosuits for stroke survivors at the Biodesign Lab and Wyss Institute for Biologically Inspired Engineering.
Foundation in mechanical design, dynamics, and human biomechanics. Capstone project in ergonomic analysis and motion capture.
