Performance augmentation in hybrid bionic systems: techniques and experiment

TL;DR

This paper explores techniques for enhancing performance in hybrid bionic systems through sensorimotor learning and physiological adaptation, using environmental perturbation methods to induce and measure these changes.

Contribution

It introduces a novel approach combining environmental perturbation with physiological assessment to improve performance augmentation in hybrid bionic systems.

Findings

Regulation occurs at morphological, neural, and muscular levels

Sensorimotor learning is linked to physiological changes

Homeostasis and adaptation are conceptually connected

Abstract

Recent developments in hybrid biological-technological systems (hybrid bionic systems) has made clear the need for evaluating ergonomic fit in such systems, especially as users first become adjusted to using such systems. This training is accompanied by physiological adaptation, and can be thought of computationally as a relative degree of matching between prosthetic devices, physiology, and behavior. Achieving performance augmentation involves two features of performance: a specific form of learning, memory, and mechanotransduction called sensorimotor learning, and physiological adaptation to novel physical information imposed by the augmented environment of hybrid bionic systems. A method borrowed from environmental medicine involving perturbing the environment for a range of internal physiological conditions was used to induce sensorimotor learning and memory associated physiological changes. In addition, features of the adult phenotype were considered as a mitigator of learning-related adaptations. Using a series of statistical tests and techniques, the results demonstrate than three forms of regulation are at work related to morphological, neural, and muscular control. A discussion of the conceptual relationship between homeostasis and adaptation will then be discussed in addition to potential applications to performance augmentation strategies.