Computer simulation based parameter selection for resistance exercise

TL;DR

This paper introduces a computer simulation framework that uses video-based data extraction and neuromuscular modeling to optimize resistance training programs tailored to individual athletes.

Contribution

It presents a novel integration of computer vision, inverse dynamics, and simulation to personalize and improve resistance exercise planning.

Findings

Effective extraction of training data from video sequences

Successful fitting of neuromuscular models to individual athletes

Ability to simulate and predict training outcomes

Abstract

In contrast to most scientific disciplines, sports science research has been characterized by comparatively little effort investment in the development of relevant phenomenological models. Scarcer yet is the application of said models in practice. We present a framework which allows resistance training practitioners to employ a recently proposed neuromuscular model in actual training program design. The first novelty concerns the monitoring aspect of coaching. A method for extracting training performance characteristics from loosely constrained video sequences, effortlessly and with minimal human input, using computer vision is described. The extracted data is subsequently used to fit the underlying neuromuscular model. This is achieved by solving an inverse dynamics problem corresponding to a particular exercise. Lastly, a computer simulation of hypothetical training bouts, using athlete-specific capability parameters, is used to predict the effected adaptation and changes in performance. The software described here allows the practitioner to manipulate hypothetical training parameters and immediately see their effect on predicted adaptation for a specific athlete. Thus, this work presents a holistic view of the monitoring-assessment-adjustment loop.