An Optimization-Based Model for Full-body Reaching Movements

TL;DR

This paper presents a simulation model for full-body reaching movements that predicts trajectories and joint excursions by optimizing a cost function balancing task accuracy and physiological effort, validated against experimental data.

Contribution

It introduces an inverse dynamics-based simulation with adaptive weighting to accurately model human full-body reaching movements, incorporating physiological cost terms.

Findings

Best fit achieved by minimizing task error, joint power, and COM displacement

Model effectively predicts final postures and movement trajectories

Method validated with experimental data from 15 participants

Abstract

Background The development of a simulation model of full body reaching tasks that can predict endeffector trajectories and joint excursions consistent with experimental data is a non-trivial task. Because of the kinematic redundancy inherent in these multi-joint tasks there are an infinite number of postures that could be adopted to complete them. By developing models to simulate full-body reaching movements in 3D space we can begin to explore cost functions that may be used by the central nervous system to plan and execute these movements. Methods A robust simulation model was developed using 1) graphic-based modeling tools to generate an inverse dynamics controller (SimMechanics), 2) controller parameterization methods, and 3) cost function criteria. An adaptive weight coefficient based on the final motor task error (i.e. distance between end-effector and target at the end of movement) was proposed to balance motor task error and physiological cost terms (e.g. joint power). The output of the simulation models using different cost controller functions based on motor task error or motor task error and various physiological cost terms (e.g. joint power, center of mass displacement) were compared to experimental data from 15 healthy participants performing full body reaching movements. Results In sum, the best fit to the experimental data was obtained by minimizing motor task error, joint power, and center of mass displacement. Simulation and experimental results demonstrated that the proposed method is effective for the simulation of large-scale human skeletal systems. Conclusions This method can reasonably predict the whole body reaching movements including final postures, joint power and movement of COM using simple algebraic calculations of inverse dynamics and forward kinematics.