Neuromechanics Tremor Modeling

Using robotics principles, advanced dynamic system modeling, and statistical analysis, I identified characteristics of the peripheral neuromusculoskeletal system that govern upper-limb tremor intensity. I integrated complex neuromuscular parameters, including muscle activation dynamics, mechanical properties of the upper limb, and joint kinematics. Cross-team collaboration expanded the model’s applicability across diverse movement conditions and research objectives.

I transformed an upper-limb biomechanical model from 15 to 50 muscles, generating three times more simulation data and enabling comprehensive analysis of muscle coordination, force generation, and joint dynamics. To support this expansion, I reengineered the entire codebase, reducing simulation runtime 85×—from 170 hours to 2—allowing rapid iteration, parameter sweeps, and large-scale testing of complex motion scenarios. These improvements made it possible to explore high-dimensional musculoskeletal behaviors that were previously computationally prohibitive.

As an active co-author and editor on a forthcoming research paper, I translated high-performance simulations into actionable insights for experimental design and biomechanical analysis. My work combined computational optimization, biomechanical rigor, and robotics principles to create a robust, high-fidelity tool for studying human movement, supporting predictive analyses for rehabilitation strategies and the development of assistive robotics.

This paper is expected to be published in early 2026. 

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