Building the NINAPRO Database:A Resource for the Biorobotics Community           1Manfredo Atzori, 2Arjan Gijsberts, 3Simo...
1. Introduction: what is electromyographyElectromyography (EMG) is the measurement of electrical activitythat creates musc...
1. Introduction: electromyography controlled prosthetics•    2-3 degrees of freedom•    Few programmed movements•    Very ...
1. Introduction: sEMG Data Bases•  NO large scale public sEMG databases, only private ones  (Fukuda, 2003; Tsuji 1993; Fer...
1. Introduction: project motivations & goals•  Creation and refinement of the acquisition protocol•  Acquisition of the da...
2. Database: acquisition setup (1)         Laptop: Dell Latitude E5520    !         Digital Acquisition Card: National Ins...
2. Database: acquisition setup (2)1.  8 equally spaced electrodes2.  2 electrodes on finger flexor and extensor muscles3. ...
3. Methods: acquisition procedureIntact subjects:•  The subject is asked to repeat what is shown on the screen   with the ...
2. Database: movementsExercise 1                                                                                          ...
2. Database: dataData stored for each subject:•  One XML file with clinical and experimental information•  Unprocessed dat...
2. Database: public, with web interfaceurl: http://ninapro.hevs.ch                                          11
3. Analysis: evaluation of the acquisition protocol•  Principal Component Analysis   data that is easily separable visuall...
3. Analysis: preprocessing1.  Synchronization: linear interpolation of all data at 100Hz2.  Filtering of sEMG signals: But...
3. Analysis: Principal Component AnalysisTwo principal components for each of the nine cases considered•  Movements are ea...
3. Analysis: Quantitative classification performanceIntra-subject classification:•  Multi-class LS-SVM with RBF kernel is ...
3. Analysis: LS-SVM ResultsIntra-subject classification:•  Errors from 7.5% to 20%•  High standard deviation (performance ...
5. Conclusions:Database•  Acquisition setup: portable, based on scientific research and   industrial application needs•  A...
5. Future Work:•  Establishing a standard benchmark•  Collecting data from a large number of movementsAdd a custom-built f...
THANKS FOR THE ATTENTIONPlease, cite:Manfredo Atzori, Arjan Gijsberts, Simone Heynen, Anne-Gabrielle MittazHager, Olivier ...
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Building the NINAPRO Database: A Resource for the Biorobotics Community

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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.

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Building the NINAPRO Database: A Resource for the Biorobotics Community

  1. 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. 2. 1. Introduction: what is electromyographyElectromyography (EMG) is the measurement of electrical activitythat creates muscle contractionsThe 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. 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 timesIn contrast to recent advances inmechatronics 3
  4. 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. 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. 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. 7. 2. Database: acquisition setup (2)1.  8 equally spaced electrodes2.  2 electrodes on finger flexor and extensor muscles3.  Two axes inclinometer4.  Data glove 7
  8. 8. 3. Methods: acquisition procedureIntact 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. 9. 2. Database: movementsExercise 1 Hato, 200412 movements ! ! ! ! ! ! Sebelius, 2005 Farrel, 2008 ! ! ! ! ! ! Crawford, 2005 Feix, 2008Exercise 217 movements ! ! ! ! ! ! ! ! DASH Score ! ! ! ! ! ! ! ! !Exercise 323 movements ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 9
  10. 10. 2. Database: dataData 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 onSubjects:•  Currently stored: 27 intact subjects•  To be acquired: ~100 intact subjects ~40 amputated subjects 10
  11. 11. 2. Database: public, with web interfaceurl: http://ninapro.hevs.ch 11
  12. 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. 13. 3. Analysis: preprocessing1.  Synchronization: linear interpolation of all data at 100Hz2.  Filtering of sEMG signals: Butterworth, zero-phase, 1Hz, second order3.  Segmenting: each movement (including rest) is divided into three equal parts4.  The data contained in the central segment is averaged for each electrode 1 2 3 4 13
  14. 14. 3. Analysis: Principal Component AnalysisTwo 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. 15. 3. Analysis: Quantitative classification performanceIntra-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 splitsInter-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. 16. 3. Analysis: LS-SVM ResultsIntra-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. 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 storedData 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. 18. 5. Future Work:•  Establishing a standard benchmark•  Collecting data from a large number of movementsAdd a custom-built force-sensing device to acquire dynamicfinger/hand/wrist data.•  Collecting data from a large number subjectsFurther releases of the database will contain data recorded from alarger number of subjects. 18
  19. 19. THANKS FOR THE ATTENTIONPlease, cite:Manfredo Atzori, Arjan Gijsberts, Simone Heynen, Anne-Gabrielle MittazHager, Olivier Deriaz, Patrick Vand der Smagt, Claudio Castellini, BarbaraCaputo and Henning Müller, Building the NINAPRO Database: A Resourcefor the Biorobotics Community, in: Proceedings of the IEEE InternationalConference on Biomedical Robotics and Biomechatronics, Rome, 2012Full 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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