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 prosthese...

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Bibliographic Details
Published inScientific data Vol. 1; no. 1; p. 140053
Main Authors Atzori, Manfredo, Gijsberts, Arjan, Castellini, Claudio, Caputo, Barbara, Hager, Anne-Gabrielle Mittaz, Elsig, Simone, Giatsidis, Giorgio, Bassetto, Franco, Müller, Henning
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 23.12.2014
Nature Publishing Group
Subjects
639/166/985
692/308/575
Algorithms
Amputation
Data Descriptor
Databases, Factual
Electromyography
Hand - surgery
Humanities and Social Sciences
Humans
multidisciplinary
Prostheses and Implants
Robotics - methods
Science
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Summary: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. Design Type(s) in vivo design • injury design • observation design • parallel group design Measurement Type(s) muscular activity Technology Type(s) electromyography Factor Type(s) Injury Sample Characteristic(s) Homo sapiens • adult organism Machine-accessible metadata file describing the reported data (ISA-Tab format)
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Manfredo Atzori created the acquisition protocol, did data acquisition, performed data analysis and wrote the paper. Arjan Gijsberts created the data acquisition software and reviewed the paper. Claudio Castellini ideated the project, created the data acquisition software and reviewed the paper. Barbara Caputo ideated and planned the project and reviewed the paper. Anne Gabrielle Mittaz-Hager created the acquisition protocol, participated to data acquisition and reviewed the paper. Simone Elsig created the acquisition protocol, recruited subjects, participated to data acquisition and reviewed the paper. Giorgio Giatsidis recruited subjects, participated to data acquisition and reviewed the paper. Franco Bassetto recruited subjects, participated to data acquisition and reviewed the paper. Henning Müller ideated and planned the project and reviewed the paper.
ISSN:2052-4463
2052-4463
DOI:10.1038/sdata.2014.53