Co-Adaptive Control of Bionic Limbs via Unsupervised Adaptation of Muscle Synergies.

Dennis Yeung,Irene Mendez Guerra,Emilia Siponen,Dario Farina,Ivan Vujaklija,Ian Barner-Rasmussen

doi:10.1109/tbme.2022.3150665

Abstract

In this work, we present a myoelectric interface that extracts natural motor synergies from multi-muscle signals and adapts in real-time with new user inputs. With this unsupervised adaptive myocontrol (UAM) system, optimal synergies for control are continuously co-adapted with changes in user motor control, or as a function of perturbed conditions via online non-negative matrix factorization guided by physiologically informed sparseness constraints in lieu of explicit data labelling. UAM was tested in a set of virtual target reaching tasks completed by able-bodied and amputee subjects. Tests were conducted under normative and electrode perturbed conditions to gauge control robustness with comparisons to non-adaptive and supervised adaptive myocontrol schemes. Furthermore, UAM was used to interface an amputee with a multi-functional powered hand prosthesis during standardized Clothespin Relocation Tests, also conducted in normative and perturbed conditions. In virtual tests, UAM effectively mitigated performance degradation caused by electrode displacement, affording greater resilience over an existing supervised adaptive system for amputee subjects. Induced electrode shifts also had negligible effect on the real world control performance of UAM with consistent completion times (23.91 ±1.33 s) achieved across Clothespin Relocation Tests in the normative and electrode perturbed conditions. UAM affords comparable robustness improvements to existing supervised adaptive myocontrol interfaces whilst providing additional practical advantages for clinical deployment. The proposed system uniquely incorporates neuromuscular control principles with unsupervised online learning methods and presents a working example of a freely co-adaptive bionic interface.

Highlights

U PPER limb loss caused by an accident, underlying disorder or a genetic condition can result in a reduction of function that impacts almost all aspects of daily living
To address the aforementioned limitations, we propose an unsupervised adaptive myocontrol system with an online learning algorithm that is automatically administered in realtime
negative matrix factorization (NMF) for simultaneous and proportional myocontrol. Both unsupervised adaptive myocontrol (UAM) and non-adaptive base model (NAM) rely on an underlying myocontrol model based on NMF in which a series of muscle activation patterns, X, can be considered as an instantaneous linear mixture of command primitives, F, and their basis functions, W, which are analogous to muscle synergies [5], [13]

Summary

Introduction

U PPER limb loss caused by an accident, underlying disorder or a genetic condition can result in a reduction of function that impacts almost all aspects of daily living. With bionic restoration of human motor function, patients are fitted with robotic prostheses designed to mimic the capabilities of a missing extremity. In order to ensure the successful embodiment of these devices, the user’s motor intentions must be reliably and faithfully interpreted [1]. Surface electromyography (EMG) from residual muscles is usually employed for establishing this human-machine interfacing [2], [3]. Whilst robust and effective in simple control tasks, conventional direct control no longer facilitates an interface that can efficiently engage the multiple grip patterns and wrist articulations that modern hand prostheses offer. By employing more sophisticated machine learning-based methods such as multichannel pattern recognition, users can directly access different degrees-of-freedom (DoFs) of their device [4]

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Methods

Results

Conclusion