Paper Info
Title
Online Natural Myocontrol of Combined Hand and Wrist Actions Using Tactile Myography and the Biomechanics of Grasping.
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%).
Year
DOI
Venue
2020
10.3389/fnbot.2020.00011
FRONTIERS IN NEUROROBOTICS
Keywords
DocType
Volume
myocontrol,tactile myography,prosthetics,combined actions,grip strength,high-density force myography (HD-FMG),biomechanics of grasping
Journal
14
ISSN
Citations 
PageRank 
1662-5218
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Mathilde Connan100.34
Risto Kõiva2678.00
Claudio Castellini344833.56