A Myoelectric Postural Control Algorithm for Persons With Transradial Amputations: A Consideration of Clinical Readiness

Progressive unsupervised control of myoelectric upper limbs

Objective.Unsupervised myocontrol methods aim to create control models for myoelectric prostheses while avoiding the complications of acquiring reliable, regular, and sufficient labeled training data. A limitation of current unsupervised methods is that they fix the number of controlled prosthetic functions a priori, thus requiring an initial assessment of the user's motor skills and neglecting the development of novel motor skills over time.Approach.We developed a progressive unsupervised myocontrol (PUM) paradigm in which the user and the control model coadaptively identify distinct muscle synergies, which are then used to control arbitrarily associated myocontrol functions, each corresponding to a hand or wrist movement. The interaction starts with learning a single function and the user may request additional functions after mastering the available ones, which aligns the evolution of their motor skills with an increment in system complexity. We conducted a multi-session user study to evaluate PUM and compare it against a state-of-the-art non-progressive unsupervised alternative. Two participants with congenital upper-limb differences tested PUM, while ten non-disabled control participants tested either PUM or the non-progressive baseline. All participants engaged in myoelectric control of a virtual hand and wrist.Main results.PUM enabled autonomous learning of three myocontrol functions for participants with limb differences, and of all four available functions for non-disabled subjects, using both existing or newly identified muscle synergies. Participants with limb differences achieved similar success rates to non-disabled ones on myocontrol tests, but faced greater difficulties in internalizing new motor skills and exhibited slightly inferior movement quality. The performance was comparable with either PUM or the non-progressive baseline for the group of non-disabled participants.Significance.The PUM paradigm enables users to autonomously learn to operate the myocontrol system, adapts to the users' varied preexisting motor skills, and supports the further development of those skills throughout practice.

Recalibration of myoelectric control with active learning

Introduction

Improving the robustness of myoelectric control to work over many months without the need for recalibration could reduce prosthesis abandonment. Current approaches rely on post-hoc error detection to verify the certainty of a decoder's prediction using predefined threshold value. Since the decoder is fixed, the performance decline over time is inevitable. Other approaches such as supervised recalibration and unsupervised self-recalibration entail limitations in scaling up and computational resources. The objective of this paper is to study active learning as a scalable, human-in-the-loop framework, to improve the robustness of myoelectric control.

Method

Active learning and linear discriminate analysis methods were used to create an iterative learning process, to modify decision boundaries based on changes in the data. We simulated a real-time scenario. We exploited least confidence, smallest margin and entropy reduction sampling strategies in single and batch-mode sample selection. Optimal batch-mode sampling was considered using ranked batch-mode active learning.

Results

With only 3.2 min of data carefully selected by the active learner, the decoder outperforms random sampling by 4-5 and ~2% for able-bodied and people with limb difference, respectively. We observed active learning strategies to systematically and significantly enhance the decoders adaptation while optimizing the amount of training data on a class-specific basis. Smallest margin and least confidence uncertainty were shown to be the most supreme.

Discussion

We introduce for the first time active learning framework for long term adaptation in myoelectric control. This study simulates closed-loop environment in an offline manner and proposes a pipeline for future real-time deployment.

A Network-Enabled Myoelectric Platform for Prototyping Research Outside of the Lab

We present a network-enabled myoelectric platform for performing research outside of the laboratory environment. A low-cost, flexible, modular design based on common Internet of Things connectivity technology allows home-based research to be piloted. An outline of the platform is presented followed by technical results obtained from ten days of home-based tests with three participants. Results show the system enabled collection of close to 12,000 trials during around 28 cumulative hours of use. Home-based testing of multiple participants in parallel offers efficiency gains and provides a intuitive route toward long-term testing of upper-limb prosthetic devices in more naturalistic settings.Clinical relevance- In-home myoelectric training reduces clinician time. Network-enabled systems with back-end dashboards allow clinicians to monitor patients myoelectric ability over time and will provide a new way of accessing information about how upper-limb prosthetics are commonly used.

A more human prosthetic hand

Co-creation leads the way for bioinspired prosthetics with improved design and performance.