Building the NINAPRO Database: A Resource for the Biorobotics Community

1Manfredo Atzori, 2Arjan Gijsberts, 3Simone Heynen, 3Anne-Gabrielle Mittaz Hager, 4Olivier Deriaz, 5Patrick van der Smagt,

5Claudio Castellini, 2Barbara Caputo, and 1Henning Müller

1Dept. Business Information Systems, HES-SO Valais, Switzerland 2 Institute de Recherche Idiap, Switzerland

3 Department of Physical Therapy, HES-SO Valais, Switzerland 4 Institut de recherche en réadaptation, Suvacare, Switzerland

5 Institute of Robotics and Mechatronics, DLR (German Aerospace Centre), Germany

1. Introduction: what is electromyography Electromyography (EMG) is the measurement of electrical activity that creates muscle contractions

The signal path:

•  Originates in a motor neuron

•  Travels to the target muscle(s)

•  Starts a series of electrochemical changes that leads to an action potential

•  Is detected by one or more electrodes

2 (Jessica Zarndt, The Muscle Physiology of Electromyography, UNLV)

1. Introduction: electromyography controlled prosthetics •  2-3 degrees of freedom •  Few programmed movements •  Very coarse force control •  No dexterous control •  No natural Control •  Long training times

In contrast to recent advances in mechatronics

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1. Introduction: sEMG Data Bases

•  NO large scale public sEMG databases, only private ones

•  NO common sEMG acquisition protocol

•  NO common sEMG storage protocol

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(Fukuda, 2003; Tsuji 1993; Ferguson, 2002; Zecca, 2002; Chan, 2005; Sebelius, 2005; Castellini, 2008; Jiang, 2009; Tenore, 2009; Castellini, 2009)

1. Introduction: project motivations & goals •  Creation and refinement of the acquisition protocol

•  Acquisition of the database

•  Public release of the database

•  Worldwide test of classification algorithms

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•  Augment dexterity of sEMG prostheses

•  Reduce training time

2. Database: acquisition setup (1)

Laptop: Dell Latitude E5520

Digital Acquisition Card: National Instruments 6023E

sEMG Electrodes: 10 double-differential Otto Bock 13E200

Printed Circuit Board, Cables & Connectors

Data Glove 22 sensors Cyberglove II (Cyberglove Systems)

Inclinometer: Kübler 8.IS40.2341 6

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2. Database: acquisition setup (2)

1.  8 equally spaced electrodes

2.  2 electrodes on finger flexor and extensor muscles

3.  Two axes inclinometer

4.  Data glove

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3. Methods: acquisition procedure Intact subjects: •  The subject is asked to repeat what is shown on the screen

with the right hand.

Amputated subjects: •  The subject is asked to think to repeat what is shown on the

screen with both hands. •  In the meanwhile the subject needs to do the same movement

with remaining hand.

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2. Database: movements

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Hato, 2004 Sebelius, 2005

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Farrel, 2008

Crawford, 2005

Feix, 2008

DASH Score

Exercise 1 12 movements

Exercise 2 17 movements

Exercise 3 23 movements

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2. Database: data Data stored for each subject: •  One XML file with clinical and experimental information •  Unprocessed data (sEMG, Cyberglove, Inclinometer, Movie) •  One preview picture for each exercise •  One picture of the arm without the acquisition setup •  One picture of the arm with the acquisition setup on Subjects: •  Currently stored: 27 intact subjects

•  To be acquired: ~100 intact subjects

~40 amputated subjects

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2. Database: public, with web interface url: http://ninapro.hevs.ch

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3. Analysis: evaluation of the acquisition protocol •  Principal Component Analysis

data that is easily separable visually will often also be easy to classify

•  Classification idea of how discriminative the sEMG signals are for movements and subjects

•  Groups of subjects: 1, 8, 27 subject

•  Sets of movements: 3, 11, 52 movements

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3. Analysis: preprocessing 1.  Synchronization: linear interpolation of all data at 100Hz 2.  Filtering of sEMG signals: Butterworth, zero-phase, 1Hz,

second order 3.  Segmenting: each movement (including rest) is divided into

three equal parts 4.  The data contained in the central segment is averaged for

each electrode

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3. Analysis: Principal Component Analysis Two principal components for each of the nine cases considered

•  Movements are easy to distinguish in cases with few subjects and few movements.

•  Overlap increases combining data from multiple subjects •  Overlap increases increasing the number of movements.

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3. Analysis: Quantitative classification performance Intra-subject classification: •  Multi-class LS-SVM with RBF kernel is trained for each subject •  Training: 5 movement repetitions •  Test: 5 movement repetitions •  Experiment repeated 25 times with different random splits

Inter-subject classification: •  Multi-class LS-SVM with RBF kernel is trained for each subject •  Training: 5 movement repetitions of one subject •  Test: 5 movement repetitions of each of all the other subjects •  Experiment repeated 25 times with different random splits

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3. Analysis: LS-SVM Results Intra-subject classification: •  Errors from 7.5% to 20% •  High standard deviation (performance variability among

different subjects) Inter-subject classification: •  Only marginally above chance level

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5. Conclusions: Database •  Acquisition setup: portable, based on scientific research and

industrial application needs •  Acquisition protocol: complete and easy to be reproduced •  Movements: 52, selected from the scientific literature •  Data: currently 27 intact subjects are stored Data Analysis & Evaluation •  PCA: movements are easy to distinguish in cases with few

movements and few subjects •  Intra-subject classification: results comparable to those found

in the literature with the same number of movements •  Inter-subject classification: classification slightly above chance

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5. Future Work: •  Establishing a standard benchmark

•  Collecting data from a large number of movements

Add a custom-built force-sensing device to acquire dynamic finger/hand/wrist data.

•  Collecting data from a large number subjects Further releases of the database will contain data recorded from a larger number of subjects.

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THANKS FOR THE ATTENTION

For more information: http://www.idiap.ch/project/ninapro/

http://ninapro.hevs.ch

Contacts: manfredo.atzori@hevs.ch

Please, cite: Manfredo Atzori, Arjan Gijsberts, Simone Heynen, Anne-Gabrielle Mittaz Hager, Olivier Deriaz, Patrick Vand der Smagt, Claudio Castellini, Barbara Caputo and Henning Müller, Building the NINAPRO Database: A Resource for the Biorobotics Community, in: Proceedings of the IEEE International Conference on Biomedical Robotics and Biomechatronics, Rome, 2012 Full publication: http://publications.hevs.ch/index.php/publications/show/1172