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: [email protected]
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
