Literature DB >> 26737941

The Ninapro database: A resource for sEMG naturally controlled robotic hand prosthetics.

Manfredo Atzori, Henning Muller.   

Abstract

The dexterous natural control of robotic prosthetic hands with non-invasive techniques is still a challenge: surface electromyography gives some control capabilities but these are limited, often not natural and require long training times; the application of pattern recognition techniques recently started to be applied in practice. While results in the scientific literature are promising they have to be improved to reach the real needs. The Ninapro database aims to improve the field of naturally controlled robotic hand prosthetics by permitting to worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark database. Currently, the Ninapro database includes data from 67 intact subjects and 11 amputated subject performing approximately 50 different movements. The data are aimed at permitting the study of the relationships between surface electromyography, kinematics and dynamics. The Ninapro acquisition protocol was created in order to be easy to be reproduced. Currently, the number of datasets included in the database is increasing thanks to the collaboration of several research groups.

Mesh:

Year:  2015        PMID: 26737941     DOI: 10.1109/EMBC.2015.7320041

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  4 in total

1.  A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models.

Authors:  Pranesh Gopal; Amandine Gesta; Abolfazl Mohebbi
Journal:  Sensors (Basel)       Date:  2022-05-11       Impact factor: 3.847

2.  Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures.

Authors:  Mehmet Akif Ozdemir; Deniz Hande Kisa; Onan Guren; Aydin Akan
Journal:  Data Brief       Date:  2022-02-04

Review 3.  A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding.

Authors:  Anany Dwivedi; Helen Groll; Philipp Beckerle
Journal:  Sensors (Basel)       Date:  2022-08-23       Impact factor: 3.847

4.  U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions.

Authors:  Giuseppe Averta; Federica Barontini; Vincenzo Catrambone; Sami Haddadin; Giacomo Handjaras; Jeremia P O Held; Tingli Hu; Eike Jakubowitz; Christoph M Kanzler; Johannes Kühn; Olivier Lambercy; Andrea Leo; Alina Obermeier; Emiliano Ricciardi; Anne Schwarz; Gaetano Valenza; Antonio Bicchi; Matteo Bianchi
Journal:  Gigascience       Date:  2021-06-18       Impact factor: 6.524

  4 in total

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