Sparse signals for the control of human movements using the infinity norm

TL;DR

This paper introduces a novel optimal control method using the infinity norm to generate sparse, spike-like control signals that can model human reaching movements with simpler, biologically interpretable signals.

Contribution

It proposes a new approach to optimal control by replacing traditional cost norms with the infinity norm, producing sparse, spike-like control signals that resemble neuronal activity.

Findings

Sparse control signals can effectively model human reaching movements.

The resulting control signals are continuous, smooth, and biologically interpretable.

The method simplifies control signals while maintaining movement accuracy.

Abstract

Optimal control models have been successful in describing many aspects of human movement. The interpretation of such models regarding neuronal implementation of the human motor system is not clear. An important aspects of optimal control policies is the notion of cost. Optimal control seeks to minimize a notion of cost, while meeting certain goals. We offer a method to transform current methods in the literature from their traditional form by changing the norm by which cost is assessed. We show how sparsity can be introduced into current optimal approaches that use continuous control signals. We assess cost using the infinity norm. This results in optimal signals which can be represented by a small amount of Dirac delta functions. Sparsity has played an important role in theoretical neuroscience for information processing (such as vision). In this work, to obtain sparse control signals, the infinity norm is used as a penalty on the control signal, which is then encoded with Dirac delta functions. We show, for a basic physical system, a point mass can be moved between two points in a way that resembles human fast reaching movements. Despite the sparse nature of the control signal, the movements that result are continuous and smooth. These control signals are simpler than their non-sparse counterparts, yet yield comparable results when applied towards modeling reaching movements. In addition, such sparse control signals resemble a sequence of spikes, giving this approach a biological interpretation. Actual neuronal implementations are more complex. However, this work shows, in principle, that sparsely encoded control signals are a plausible implementation for the control of reaching movements. Leading techniques for modeling human movements can easily be adjusted in order to introduce sparsity, with a biological interpretation and the simplified information content of the control signal.