Directional Forgetting for Stable Co-Adaptation in Myoelectric Control

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.

Optimal Motor Unit Subset Selection for Accurate Motor Intention Decoding: Towards Dexterous Real-Time Interfacing

OBJECTIVE

Motor unit (MU) discharge timings encode human motor intentions to the finest degree. Whilst tapping into such information can bring significant gains to a range of applications, current approaches to MU decoding from surface signals do not scale well with the demands of dexterous human-machine interfacing (HMI). To optimize the forward estimation accuracy and time-efficiency of such systems, we propose the inclusion of task-wise initialization and MU subset selection.

METHODS

Offline analyses were conducted on data recorded from 11 non-disabled subjects. Task-wise decomposition was applied to identify MUs from high-density surface electromyography (HD-sEMG) pertaining to 18 wrist/forearm motor tasks. The activities of a selected subset of MUs were extracted from test data and used for forward estimation of intended motor tasks and joint kinematics. To that end, various combinations of subset selection and estimation algorithms (both regression and classification-based) were tested for a range of subset sizes.

RESULTS

The mutual information-based minimum Redundancy Maximum Relevance (mRMR-MI) criterion retained MUs with the highest predicative power. When the portion of tracked MUs was reduced down to 25%, the regression performance decreased only by 3% (R2=0.79) while classification accuracy dropped by 2.7% (accuracy = 74%) when kernel-based estimators were considered.

CONCLUSION AND SIGNIFICANCE

Careful selection of tracked MUs can optimize the efficiency of MU-driven interfacing. In particular, prioritization of MUs exhibiting strong nonlinear relationships with target motions is best leveraged by kernel-based estimators. Hence, this frees resources for more robust and adaptive MU decoding techniques to be implemented in future.

Toward higher-performance bionic limbs for wider clinical use

Most prosthetic limbs can autonomously move with dexterity, yet they are not perceived by the user as belonging to their own body. Robotic limbs can convey information about the environment with higher precision than biological limbs, but their actual performance is substantially limited by current technologies for the interfacing of the robotic devices with the body and for transferring motor and sensory information bidirectionally between the prosthesis and the user. In this Perspective, we argue that direct skeletal attachment of bionic devices via osseointegration, the amplification of neural signals by targeted muscle innervation, improved prosthesis control via implanted muscle sensors and advanced algorithms, and the provision of sensory feedback by means of electrodes implanted in peripheral nerves, should all be leveraged towards the creation of a new generation of high-performance bionic limbs. These technologies have been clinically tested in humans, and alongside mechanical redesigns and adequate rehabilitation training should facilitate the wider clinical use of bionic limbs.

Co-adaptive control of bionic limbs via unsupervised adaptation of muscle synergies

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

UAM affords comparable robustness improvements to existing supervised adaptive myocontrol interfaces whilst providing additional practical advantages for clinical deployment.

SIGNIFICANCE

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.

A Framework for Optimizing Co-adaptation in Body-Machine Interfaces

The operation of a human-machine interface is increasingly often referred to as a two-learners problem, where both the human and the interface independently adapt their behavior based on shared information to improve joint performance over a specific task. Drawing inspiration from the field of body-machine interfaces, we take a different perspective and propose a framework for studying co-adaptation in scenarios where the evolution of the interface is dependent on the users' behavior and that do not require task goals to be explicitly defined. Our mathematical description of co-adaptation is built upon the assumption that the interface and the user agents co-adapt toward maximizing the interaction efficiency rather than optimizing task performance. This work describes a mathematical framework for body-machine interfaces where a naïve user interacts with an adaptive interface. The interface, modeled as a linear map from a space with high dimension (the user input) to a lower dimensional feedback, acts as an adaptive “tool” whose goal is to minimize transmission loss following an unsupervised learning procedure and has no knowledge of the task being performed by the user. The user is modeled as a non-stationary multivariate Gaussian generative process that produces a sequence of actions that is either statistically independent or correlated. Dependent data is used to model the output of an action selection module concerned with achieving some unknown goal dictated by the task. The framework assumes that in parallel to this explicit objective, the user is implicitly learning a suitable but not necessarily optimal way to interact with the interface. Implicit learning is modeled as use-dependent learning modulated by a reward-based mechanism acting on the generative distribution. Through simulation, the work quantifies how the system evolves as a function of the learning time scales when a user learns to operate a static vs. an adaptive interface. We show that this novel framework can be directly exploited to readily simulate a variety of interaction scenarios, to facilitate the exploration of the parameters that lead to optimal learning dynamics of the joint system, and to provide an empirical proof for the superiority of human-machine co-adaptation over user adaptation.

Assistance Robotics and Biosensors 2019

This Special Issue is focused on breakthrough developments in the field of assistive and rehabilitation robotics. The selected contributions include current scientific progress from biomedical signal processing and cover applications to myoelectric prostheses, lower-limb and upper-limb exoskeletons and assistive robotics.

Myoelectric Control for Upper Limb Prostheses

State-of-the-art high-end prostheses are electro-mechanically able to provide a great variety of movements. Nevertheless, in order to functionally replace a human limb, it is essential that each movement is properly controlled. This is the goal of prosthesis control, which has become a growing research field in the last decades, with the ultimate goal of reproducing biological limb control. Therefore, exploration and development of prosthesis control are crucial to improve many aspects of an amputee’s life. Nowadays, a large divergence between academia and industry has become evident in commercial systems. Although several studies propose more natural control systems with promising results, basic one degree of freedom (DoF), a control switching system is the most widely used option in industry because of simplicity, robustness and inertia. A few classification controlled prostheses have emerged in the last years but they are still a low percentage of the used ones. One of the factors that generate this situation is the lack of robustness of more advanced control algorithms in daily life activities outside of laboratory conditions. Because of this, research has shifted towards more functional prosthesis control. This work reviews the most recent literature in upper limb prosthetic control. It covers commonly used variants of possible biological inputs, its processing and translation to actual control, mostly focusing on electromyograms as well as the problems it will have to overcome in near future.