This paper is about (self-powered) advanced hand prosthetics and their control via surface electromyography (sEMG). We hereby introduce to the biorobotics community the first version of the NINAPRO database, containing kinematic and sEMG data from the upper limbs of 27 intact subjects while performing 52 finger, hand and wrist movements of interest. The setup and experimental protocol are distilled from existing literature and thoroughly described; the data are then analysed and the results are discussed. In particular, it is clear that standard analysis techniques are no longer enough when so many subjects and movements are taken into account. The database is publicly available to download in standard ASCII format. The database is an ongoing work lasting several years, which is planned to contain data from more than 100 intact subjects and 50 trans-radial amputees; characteristics of the amputations, phantom limbs and prosthesis usage will be stored. We therefore hope that it will constitute a standard, widely accepted benchmark for all novel myoelectric hand prosthesis control methods, as well as a fundamental tool to deliver insight into the needs of trans-radial amputees.

1. 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

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

3. 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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4. 1. Introduction: sEMG Data Bases
• NO large scale public sEMG databases, only private ones
(Fukuda, 2003; Tsuji 1993; Ferguson, 2002; Zecca, 2002; Chan, 2005; Sebelius, 2005;
Castellini, 2008; Jiang, 2009; Tenore, 2009; Castellini, 2009)
• NO common sEMG acquisition protocol
• NO common sEMG storage protocol
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5. 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
• Augment dexterity of sEMG prostheses
• Reduce training time
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6. 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
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7. 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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8. 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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9. 2. Database: movements
Exercise 1 Hato, 2004
12 movements ! ! ! ! ! ! Sebelius, 2005
Farrel, 2008
! ! ! ! ! !
Crawford, 2005
Feix, 2008
Exercise 2
17 movements ! ! ! ! ! ! ! !
DASH Score
! ! ! !
! ! ! ! !
Exercise 3
23 movements
! ! ! ! ! ! ! !
! ! ! ! ! !
!
! ! ! ! ! ! !
! 9

10. 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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11. 2. Database: public, with web interface
url: http://ninapro.hevs.ch
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12. 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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13. 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
1 2 3 4
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14. 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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15. 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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16. 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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17. 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
level
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18. 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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19. THANKS FOR THE ATTENTION
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
For more information:
http://www.idiap.ch/project/ninapro/
http://ninapro.hevs.ch
Contacts:
manfredo.atzori@hevs.ch