Agent-based Modeling and Simulation of Human Muscle For Development of Human Gait Analyzer Application

TL;DR

This paper presents an agent-based human muscle model and a biomechanical algorithm to develop a gait analyzer application that helps clinicians identify unhealthy muscles during movement.

Contribution

It introduces a novel agent-based muscle model and Boots algorithm for reverse dynamics, enabling the development of a clinical gait analysis tool.

Findings

The application accurately calculates neural stimuli for each muscle during gait.

It can distinguish between healthy and unhealthy muscles effectively.

The model supports medical education and prosthesis research.

Abstract

Despite the fact that only a small portion of muscles are affected in motion disease and disorders, medical therapies do not distinguish between healthy and unhealthy muscles. In this paper, a method is devised in order to calculate the neural stimuli of the lower body during gait cycle and check if any group of muscles are not acting properly. For this reason, an agent-based model of human muscle is proposed. The agent is able to convert neural stimuli to force generated by the muscle and vice versa. It can be used in many researches including medical education and research and prosthesis development. Then, Boots algorithm is designed based on a biomechanical model of human lower body to do a reverse dynamics of human motion by computing the forces generated by each muscle group. Using the agent-driven model of human muscle and boots algorithm, a user-friendly application is developed which can calculate the number of neural stimuli received by each muscle during gait cycle. The application can be used by clinical experts to distinguish between healthy and unhealthy muscles.