Online Natural Myocontrol of Combined Hand and Wrist Actions Using Tactile Myography and the Biomechanics of Grasping.

Mathilde Connan,Claudio Castellini,Risto Kõiva

doi:10.3389/fnbot.2020.00011

Mathilde Connan, Claudio Castellini + Show 1 more

Open Access

https://doi.org/10.3389/fnbot.2020.00011

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Abstract

Objective: Despite numerous recent advances in the field of rehabilitation robotics, simultaneous, and proportional control of hand and/or wrist prostheses is still unsolved. In this work we concentrate on myocontrol of combined actions, for instance power grasping while rotating the wrist, by only using training data gathered from single actions. This is highly desirable since gathering data for all possible combined actions would be unfeasibly long and demanding for the amputee.Approach: We first investigated physiologically feasible limits for muscle activation during combined actions. Using these limits we involved 12 intact participants and one amputee in a Target Achievement Control test, showing that tactile myography, i.e., high-density force myography, solves the problem of combined actions to a remarkable extent using simple linear regression. Since real-time usage of many sensors can be computationally demanding, we compare this approach with another one using a reduced feature set. These reduced features are obtained using a fast, spatial first-order approximation of the sensor values.Main results: By using the training data of single actions only, i.e., power grasp or wrist movements, subjects achieved an average success rate of 70.0% in the target achievement test using ridge regression. When combining wrist actions, e.g., pronating and flexing the wrist simultaneously, similar results were obtained with an average of 68.1%. If a power grasp is added to the pool of actions, combined actions are much more difficult to achieve (36.1%).Significance: To the best of our knowledge, for the first time, the effectiveness of tactile myography on single and combined actions is evaluated in a target achievement test. The present study includes 3 DoFs control instead of the two generally used in the literature. Additionally, we define a set of physiologically plausible muscle activation limits valid for most experiments of this kind.

Highlights

The umbrella term myocontrol denotes, in contemporary assistive robotics, the control of a mechatronic device exerted by a human subject using coordinated muscle contractions
In this study on 12 intact subjects, we demonstrated the feasibility of using a new technology called tactile myography for combined control of 2 combined degrees of freedom (DoF) in highly complex online Target Achievement Control (TAC)-test with simultaneous control of 3 DoFs (6 actions trained including rest), instead of the configuration of 2 DoFs, typically used in the literature
We show that tactile myography (TMG) is a viable alternative to EMG as a sensing device for gesture recognition

Summary

Introduction

The umbrella term myocontrol denotes, in contemporary assistive robotics, the control of a mechatronic device exerted by a human subject using coordinated muscle contractions. Reliably means that the prosthesis should be able to execute what the amputee desires, exactly when he/she desires it, and in a transparent (natural) way To this aim, the keywords natural control (Castellini et al, 2014; Ortiz-Catalan and Branemark, 2014) and simultaneous and proportional control (Jiang et al, 2009; Muceli et al, 2014) have appeared in literature, denoting continuous, real-time, and graded control over many degrees of freedom (DoF) of the prosthesis—potentially, all of them. Since Craelius et al.’s experiments in the early 2000s (Curcie et al, 2001; Craelius, 2002) it has been clear that each pattern of muscle activation corresponding to a desired action corresponds to a specific, repeatable pattern of external forearm pressure produced by the volumetric variation of the underlying muscles Such a deformation could be detected by force/pressure sensors and associated to the action, thereby used as an alternative or parallel technique to surface electromyography. Examples of comparisons and mixtures of the two techniques can already be found in literature (Fang et al, 2015; Cho et al, 2016; Connan et al, 2016; Castellini et al, 2018)

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