Jeremy D. Wong

This study develops a low-cost, AI-driven markerless motion capture system for functional gait assessment, validated against traditional methods and showing strong correlation in key metrics.

This paper shows that short gait tests underestimate steady walking speed and proposes a method to correct for this bias using multiple distance measurements.

This paper introduces an Energy-Time trade-off model to explain the smoothness, speed, and individuality of human reaching movements, showing it outperforms previous models.

The paper develops a new muscle model that integrates force development dynamics, finding that conventional processes cannot explain force production speed, and proposes 'force facilitation dynamics' as a key rate-limiting factor.

This study shows that smooth human arm movements are energetically optimal, as jerkier motions require more metabolic energy due to increased muscle force production rates.

This study tests if steeper gradients of energetic cost during walking trigger adaptation, finding no spontaneous adaptation to steeper gradients but adaptation when cost savings are explicitly induced.

This paper presents a mechatronic system that applies controlled forces to study how the nervous system optimizes energy cost during walking.

This study investigates how the nervous system initiates and converges on optimal gaits during energy optimization using human experiments and reinforcement learning models.