Unsupervised Myocontrol of a Virtual Hand Based on a Coadaptive Abstract Motor Mapping

Abstract

Applications of simultaneous and proportional control for upper-limb prostheses typically rely on supervised machine learning to map muscle activations to prosthesis movements. This scheme often poses problems for individuals with limb differences, as they may not be able to reliably reproduce the training activations required to construct a natural motor mapping. We propose an unsupervised myocontrol paradigm that eliminates the need for labeled data by mapping the most salient muscle synergies in arbitrary order to a number of predefined prosthesis actions. The paradigm is coadaptive, in the sense that while the user learns to control the system via interaction, the system continually refines the identification of the user's muscular synergies. Our evaluation consisted of eight subjects without limb-loss performing target achievement control tasks of four actions of the hand and wrist. The subjects achieved comparable performance using the proposed unsupervised myocontrol paradigm and a supervised benchmark method, despite reporting increased mental load with the former.

Progressive unsupervised control of myoelectric upper limbs

“…Unsupervised myocontrol is a desirable alternative to supervised myocontrol, as it eliminates the need for hard-to-obtain labeled training data. Existing unsupervised myocontrol approaches derive lowdimensional approximations of the muscular input, corresponding to distinct muscle coactivation patterns, and employ them as control commands for the kinematic or kinetic variables of interest [19][20][21][22]. This is based on the neuromotor control principle that the human nervous system efficiently realizes movement by recruiting and coordinating nonredundant muscle synergies [23][24][25].…”

“…Research shows that humans can learn such arbitrary mappings, including muscle synergy-based ones, through closed-loop interaction with a myocontrol system, making them a viable approach for prosthetic control [24,32]. Abstract motor mappings based on muscle synergies provide flexibility and robustness, enabling users to control complex hand actions with comfortable, reliable, and stable muscle activations [22,33], while also being more resistant to variations in myoelectric signals due to the muscle synergies' focus on underlying muscle coactivation structures [24].…”

“…Gigli et al [22] used abstract motor mappings to devise a fully unsupervised coadaptive simultaneous and proportional myocontrol paradigm for hand and wrist actions. Similarly to the method from Yeung et al [21], this also originated as an adaptive extension of the work of Lin et al [20].…”

“…The result is a coevolving and coadaptive interaction dynamics between the user and the system. To achieve this, we extend the adaptive NMF from [22] with an algorithmic procedure to increase the number of components while preserving the existing ones without explicitly storing historical data. Moreover, we adjust the loss function to reduce the overlap between the identified components and to improve their stability over time.…”

“…We specifically investigate the performance of individuals with limb differences (LD), who stand to benefit the most from an unsupervised myocontrol paradigm, in comparison to non-disabled (ND) participants, who represent the best-case scenario for myocontrol due to their more extensive motor skills. Moreover, we examine how PUM compares to a non-progressive unsupervised myocontrol (UM) paradigm, based on that of [22], which serves as a baseline for identifying potential advantages and limitations of our approach. Our assessments and comparisons are based on the workload associated with the progressive learning of motor skills, as well as the evolution and retention of myocontrol performance in a series of target achievement control (TAC) tests involving a virtual hand.…”