Electromyography data for non-invasive naturally controlled robotic hand prostheses

Atzori, Manfredo (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Gijsberts, Arjan (Institute de Recherche Idiap, Rue Marconi 19, 1920 Martigny, Switzerland) ; Castellini, Claudio (Robotics and Mechatronics Center, DLR—German Aerospace Center, Muenchener Strasse 20, 82234 Oberpfaffenhofen, Germany) ; Mittaz Hager, Anne-Gabrielle (School of Health Sciences, HES-SO Valais-Wallis, Leukerbad, Switzerland) ; Caputo, Barbara (Department of Computer, Control, and Management Engineering, University of Rome La Sapienza, via Ariosto 25, 00185 Roma, Italy) ; Elsig, Simone (School of Health Sciences, HES-SO Valais-Wallis, Leukerbad, Switzerland) ; Giatsidis, Giorgio (Clinic of Plastic Surgery, Padova University Hospital, Via Giustiniani 2, 35128 Padova, Italy) ; Bassetto, Franco (Clinic of Plastic Surgery, Padova University Hospital, Via Giustiniani 2, 35128 Padova, Italy) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

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.


Keywords:
Article Type:
scientifique
Faculty:
Economie et Services
School:
HEdS-VS
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Date:
2014-12
Published in:
Scientific data
Numeration (vol. no.):
vol. 1, no. 140053, pp. 2-13
DOI:
ISSN:
2052-4463
Appears in Collection:



 Record created 2015-11-23, last modified 2019-11-28

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