Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Predicting 30-Day Emergency Readmission Risk Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Reinforcement Learning for Hand Grasp with Surface Multi-field Neuroprostheses

Authors : Eukene Imatz-Ojanguren, Eloy Irigoyen, Thierry Keller

Published in: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Hand grasp is a complex system that plays an important role in the activities of daily living. Upper-limb neuroprostheses aim at restoring lost reaching and grasping functions on people suffering from neural disorders. However, the dimensionality and complexity of the upper-limb makes the neuroprostheses modeling and control challenging. In this work we present preliminary results for checking the feasibility of using a reinforcement learning (RL) approach for achieving grasp functions with a surface multi-field neuroprosthesis for grasping. Grasps from 20 healthy subjects were recorded to build a reference for the RL system and then two different award strategies were tested on simulations based on neuro-fuzzy models of hemiplegic patients. These first results suggest that RL might be a possible solution for obtaining grasp function by means of multi-field neuroprostheses in the near future.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Detection of Stress Level and Phases by Advanced Physiological Signal Processing Based on Fuzzy Logic
next chapter Fuzzy Candlesticks Forecasting Using Pattern Recognition for Stock Markets
Literature
1.
Jones, L.A., Lederman, S.J.: Human Hand Function. Oxford University Press, Oxford (2006)CrossRef
2.
Schieber, M.H., Santello, M.: Hand function: peripheral and central constraints on performance. J. Appl. Physiol. 96, 2293–2300 (2004)CrossRef
3.
Ingram, J.N., Kording, K.P., Howard, I.S., Wolpert, D.M.: The statistics of natural hand movements. Exp. Brain Res. 188, 223–236 (2008)CrossRef
4.
Castiello, U.: The neuroscience of grasping. Nat. Rev. Neurosci. 6, 726–736 (2005)CrossRef
5.
Roh, J., Rymer, W.Z., Perreault, E.J., Yoo, S.B., Beer, R.F.: Alterations in upper limb muscle synergy structure in chronic stroke survivors. J. Neurophysiol. 109, 768–781 (2013)CrossRef
6.
Nichols-Larsen, D.S., Clark, P.C., Zeringue, A., Greenspan, A., Blanton, S.: Factors influencing stroke survivors? quality of life during subacute recovery. Stroke 36, 1480–1484 (2005)CrossRef
7.
Peckham, P.H., Knutson, J.S.: Functional electrical stimulation for neuromuscular applications. Annu. Rev. Biomed. Eng. 7, 327–360 (2005)CrossRef
8.
Popović, D.B.: Advances in functional electrical stimulation (FES). J. Electromyogr. Kinesiol. 24, 795–802 (2014)MathSciNetCrossRef
9.
Keller, T., Kuhn, A.: Electrodes for transcutaneous (surface) electrical stimulation. J. Autom. Control. 18, 35–45 (2008)CrossRef
10.
Westerveld, A.J., Schouten, A.C., Veltink, P.H., van der Kooij, H.: Selectivity and resolution of surface electrical stimulation for grasp and release. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 94–101 (2012)CrossRef
11.
Rau, G., Disselhorst-Klug, C., Schmidt, R.: Movement biomechanics goes upwards: from the leg to the arm. J. Biomech. 33, 1207–1216 (2000)CrossRef
12.
Jagodnik, K.M., Blana, D., van den Bogert, A.J., Kirsch, R.F.: An optimized proportional-derivative controller for the human upper extremity with gravity. J. Biomech. 48, 3692–3700 (2015)CrossRef
13.
Ethier, C., Oby, E.R., Bauman, M.J., Miller, L.E.: Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature 485, 368–371 (2012)CrossRef
14.
Popovic, M.B.: Control of neural prostheses for grasping and reaching. Med. Eng. Phys. 25, 41–50 (2003)CrossRef
15.
Popovic, D.B., Popovic, M.B.: Automatic determination of the optimal shape of a surface electrode: selective stimulation. J. Neurosci. Meth. 178, 174–181 (2009)CrossRef
16.
Freeman, C., Rogers, E., Burridge, J.H., Hughes, A., Meadmore, K.: Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation. Springer, Heidelberg (2015)CrossRefMATH
17.
Wright, T.W., Glowczewskie, F., Cowin, D., Wheeler, D.L.: Radial nerve excursion and strain at the elbow and wrist associated with upper-extremity motion. J. Hand Surg. 30, 990–996 (2005)CrossRef
18.
Gillen, G.: Stroke Rehabilitation: A Function-Based Approach. Elsevier Health Sciences, St. Louis (2015)
19.
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT press, Cambridge (1998)MATH
20.
Izawa, J., Kondo, T., Ito, K.: Biological arm motion through reinforcement learning. Biol. Cybern. 91, 10–22 (2004)CrossRefMATH
21.
Imatz-Ojanguren, E., Irigoyen, E., Valencia-Blanco, D., Keller, T.: Neuro-fuzzy models for hand movements induced by functional electrical stimulation in able-bodied and hemiplegic subjects. Med. Eng. Phys. (2016). (In Press)
22.
Yozbatiran, N., Der-Yeghiaian, L., Cramer, S.C.: A standardized approach to performing the action research arm test. Neurorehab. Neural Repair 22, 78–90 (2008)CrossRef
23.
Helwig, N.E., Hong, S., Hsiao-Wecksler, E.T., Polk, J.D.: Methods to temporally align gait cycle data. J. Biomech. 44, 561–566 (2011)CrossRef
24.
Vergara, M., Sancho-Bru, J.L., Gracia-Ibanez, V., Perez-Gonzalez, A.: An introductory study of common grasps used by adults during performance of activities of daily living. J. Hand Ther. 27, 225–234 (2014)CrossRef
Metadata
Title
Reinforcement Learning for Hand Grasp with Surface Multi-field Neuroprostheses
Authors
Eukene Imatz-Ojanguren
Eloy Irigoyen
Thierry Keller
Copyright Year
2017
Book
International Joint Conference SOCO’16-CISIS’16-ICEUTE’16

Print ISBN: 978-3-319-47363-5

Electronic ISBN: 978-3-319-47364-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-47364-2_30

Premium Partner

    Image Credits