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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


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



                                                       3
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




                                                                                   4
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
                                                         5
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
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




                                                         7
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.




                                                             8
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
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

                                                              10
2. Database: public, with web interface
url: http://ninapro.hevs.ch




                                          11
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


                                                                 12
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
                                                                 13
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.




                                                             14
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

                                                               15
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




                                                            16
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
                                                                  17
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.


                                                               18
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

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Building the NINAPRO Database: A Resource for the Biorobotics Community

  • 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 3
  • 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 4
  • 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 5
  • 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 6
  • 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 7
  • 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. 8
  • 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 10
  • 11. 2. Database: public, with web interface url: http://ninapro.hevs.ch 11
  • 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 12
  • 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 13
  • 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. 14
  • 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 15
  • 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 16
  • 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 17
  • 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. 18
  • 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