A Holistic Human Motor Control Model for Predictive Control of Assistive Robots

EasyChair Preprint 13335

Abstract

Humans are highly dexterous and agile in their interactions with the environment. To develop assistive robots that aid humans in their interactions, the first step is to understand, and model, the human motor control system. Here I present a holistic mathematical model for human motor control, which encompasses multiple levels of control, from high-level decision-making to low-level muscle and skeletal dynamics. This holistic model incorporates various known neural and biomechanical processes, which increases its biofidelity and predictive power. The model also runs faster than real-time, making it a suitable choice for the predictive control of assistive robots.

Keyphrases: Musculoskeletal biomechanics, Predictive Simulation, motor control, real-time simulation

@booklet{EasyChair:13335,
  author    = {Reza Sharif Razavian},
  title     = {A Holistic Human Motor Control Model for Predictive Control of Assistive Robots},
  howpublished = {EasyChair Preprint 13335},
  year      = {EasyChair, 2024}}