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      Assessment of a Wearable Force- and Electromyography Device and Comparison of the Related Signals for Myocontrol

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          Abstract

          In the frame of assistive robotics, multi-finger prosthetic hand/wrists have recently appeared, offering an increasing level of dexterity; however, in practice their control is limited to a few hand grips and still unreliable, with the effect that pattern recognition has not yet appeared in the clinical environment. According to the scientific community, one of the keys to improve the situation is multi-modal sensing, i.e., using diverse sensor modalities to interpret the subject's intent and improve the reliability and safety of the control system in daily life activities. In this work, we first describe and test a novel wireless, wearable force- and electromyography device; through an experiment conducted on ten intact subjects, we then compare the obtained signals both qualitatively and quantitatively, highlighting their advantages and disadvantages. Our results indicate that force-myography yields signals which are more stable across time during whenever a pattern is held, than those obtained by electromyography. We speculate that fusion of the two modalities might be advantageous to improve the reliability of myocontrol in the near future.

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          Myoelectric control systems—A survey

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            Electromyography data for non-invasive naturally-controlled robotic hand prostheses

            Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs. This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark scientific database. The database is targeted at studying the relationship between surface electromyography, hand kinematics and hand forces, with the final goal of developing non-invasive, naturally controlled, robotic hand prostheses. The validation section verifies that the data are similar to data acquired in real-life conditions, and that recognition of different hand tasks by applying state-of-the-art signal features and machine-learning algorithms is possible.
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              Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography

              One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)–Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human–machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.
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                Author and article information

                Contributors
                Journal
                Front Neurorobot
                Front Neurorobot
                Front. Neurorobot.
                Frontiers in Neurorobotics
                Frontiers Media S.A.
                1662-5218
                17 November 2016
                2016
                : 10
                : 17
                Affiliations
                Cognitive Robotics, Institute of Robotics and Mechatronics, German Aerospace Center (DLR) Wessling, Germany
                Author notes

                Edited by: Ricardo Chavarriaga, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland

                Reviewed by: Sebastian Amsuess, Otto Bock, Germany; Francesco Clemente, Scuola Superiore Sant'Anna, Italy

                *Correspondence: Bernhard Vodermayer bernhard.vodermayer@ 123456dlr.de
                Article
                10.3389/fnbot.2016.00017
                5112250
                27909406
                e6323aa5-546c-45db-a103-04a29e0efccd
                Copyright © 2016 Connan, Ruiz Ramírez, Vodermayer and Castellini.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 18 July 2016
                : 21 October 2016
                Page count
                Figures: 12, Tables: 7, Equations: 0, References: 47, Pages: 13, Words: 8681
                Funding
                Funded by: Deutsches Zentrum für Luft- und Raumfahrt 10.13039/501100002946
                Categories
                Neuroscience
                Original Research

                Robotics
                surface electromyography,force myography,multi-modal intent detection,machine learning,human-machine interfaces,rehabilitation robotics

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