Literature DB >> 23365946

FFLS: an accurate linear device for measuring synergistic finger contractions.

Risto Kõiva1, Barbara Hilsenbeck, Claudio Castellini.   

Abstract

After decades of theoretical study in physiology and neurology communities, the paradigm of muscle synergies is now being explored in rehabilitation robotics as a strategy to control mechanical artifacts with many degrees-of-freedom (DoF) in a simple yet effective and human-like way. In particular, muscle synergies during grasping and in graded-force tasks are of great interest for the control of dexterous hand prostheses. To this end, we have designed and tested a novel device to accurately and simultaneously measure fingertip forces. The device, called FFLS (Finger-Force Linear Sensor), measures the forces applied by the human fingertips in both directions (flexion and extension of index, middle, ring and little finger plus thumb rotation and abduction/adduction). It is suited for several different hand sizes, enforces high accuracy in the measurement and its signal is guaranteed to be linear in a high range of forces (100N in both directions for each finger). It outputs six analog voltages (±10V), suited for processing with a DAQ card.

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Year:  2012        PMID: 23365946     DOI: 10.1109/EMBC.2012.6345985

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


  5 in total

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

Authors:  Manfredo Atzori; Arjan Gijsberts; Claudio Castellini; Barbara Caputo; Anne-Gabrielle Mittaz Hager; Simone Elsig; Giorgio Giatsidis; Franco Bassetto; Henning Müller
Journal:  Sci Data       Date:  2014-12-23       Impact factor: 6.444

2.  A comparative analysis of three non-invasive human-machine interfaces for the disabled.

Authors:  Vikram Ravindra; Claudio Castellini
Journal:  Front Neurorobot       Date:  2014-10-27       Impact factor: 2.650

3.  Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework.

Authors:  Tara Baldacchino; William R Jacobs; Sean R Anderson; Keith Worden; Jennifer Rowson
Journal:  Front Bioeng Biotechnol       Date:  2018-02-26

4.  Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.

Authors:  Manfredo Atzori; Matteo Cognolato; Henning Müller
Journal:  Front Neurorobot       Date:  2016-09-07       Impact factor: 2.650

5.  The LET Procedure for Prosthetic Myocontrol: Towards Multi-DOF Control Using Single-DOF Activations.

Authors:  Markus Nowak; Claudio Castellini
Journal:  PLoS One       Date:  2016-09-08       Impact factor: 3.240

  5 in total

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