Patterns of Selection of Human Movements I: Movement Utility, Metabolic Energy, and Normal Walking Gaits

TL;DR

This paper develops a formal framework for understanding human movement patterns by modeling movement selection as an optimization process that balances utility and metabolic energy, specifically applied to walking gaits.

Contribution

It introduces a movement utility formalism incorporating metabolic energy, and demonstrates its application to modeling and predicting normal walking gait patterns.

Findings

The model predicts the relationship between walking speed and step length.

A new estimator for metabolic energy of walking gaits is proposed.

The utility-based model aligns with observed human walking behaviors.

Abstract

The biomechanics of the human body allow humans a range of possible ways of executing movements to attain specific goals. Nevertheless, humans exhibit significant patterns in how they execute movements. We propose that the observed patterns of human movement arise because subjects select those ways to execute movements that are, in a rigorous sense, optimal. In this project, we show how this proposition can guide the development of computational models of movement selection and thereby account for human movement patterns. We proceed by first developing a movement utility formalism that operationalizes the concept of a best or optimal way of executing a movement using a utility function so that the problem of movement selection becomes the problem of finding the movement that maximizes the utility function. Since the movement utility formalism includes a contribution of the metabolic energy of the movement (maximum utility movements try to minimize metabolic energy), we also develop a metabolic energy formalism that we can use to construct estimators of the metabolic energies of particular movements. We then show how we can construct an estimator for the metabolic energies of normal walking gaits and we use that estimator to construct a movement utility model of the selection of normal walking gaits and show that the relationship between avg. walking speed and avg. step length predicted by this model agrees with observation. We conclude by proposing a physical mechanism that a subject might use to estimate the metabolic energy of a movement in practice.