Skip to main content
SpringerLink
Log in

The Role of Haptic Interactions with Robots for Promoting Motor Learning

  • Chapter
  • First Online:
Neurorehabilitation Technology

  • 1162 Accesses

Abstract

Robot-assisted haptic training has the potential to facilitate motor learning and neurorehabilitation for a diverse number of motor tasks, ranging from manipulating objects with unknown dynamics to relearning walking using robotic exoskeletons. In this chapter, we provide an overview of current haptic methods evaluated in motor (re)learning studies with the goal to discuss implications for the design of rehabilitation technology. We highlight the challenge point framework as a unifying view on how to guide the design of haptic training paradigms, based on the initial skill level of the learner and the characteristics of the task to be learned. Future work on robot-aided haptic training strategies should focus on adaptive training algorithms, providing more naturalistic congruent multisensory feedback that resembles out-of-the-lab training, and conduct long-term studies to assess the efficacy of haptic training on learning not only the trained task but importantly, on skill transfer to real tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Voelcker-Rehage C. Motor-skill learning in older adults—a review of studies on age-related differences. Eur Rev Aging Phys Act. 2008;5:5–16. https://doi.org/10.1007/s11556-008-0030-9.

    Article  Google Scholar 

  2. Wishart LR, Lee TD. Effects of aging and reduced relative frequency of knowledge of results on learning a motor skill. Percept Mot Skills. 1997;84:1107–22. https://doi.org/10.2466/pms.1997.84.3.1107.

    Article  CAS  PubMed  Google Scholar 

  3. Swinnen SP. Age-related deficits in motor learning and differences in feedback processing during the production of a bimanual coordination pattern. Cogn Neuropsychol. 1998;15:439–66. https://doi.org/10.1080/026432998381104.

    Article  PubMed  Google Scholar 

  4. Basalp E, Wolf P, Marchal-Crespo L. Haptic training: Which types facilitate (re)learning of which motor task and for whom answers by a review. IEEE Trans Haptics 2021:1–1. https://doi.org/10.1109/TOH.2021.3104518.

  5. Sigrist R, Rauter G, Riener R, Wolf P. Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review. Psychon Bull Rev. 2013;20:21–53. https://doi.org/10.3758/s13423-012-0333-8.

    Article  PubMed  Google Scholar 

  6. Marchal-Crespo L, Reinkensmeyer DJ. Review of control strategies for robotic movement training after neurologic injury. J Neuroeng Rehabil. 2009;6:20. https://doi.org/10.1186/1743-0003-6-20.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Levac DE, Huber ME, Sternad D. Learning and transfer of complex motor skills in virtual reality: a perspective review. J Neuroeng Rehabil. 2019;16:121. https://doi.org/10.1186/s12984-019-0587-8.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Dehem S, Gilliaux M, Stoquart G, Detrembleur C, Jacquemin G, Palumbo S, et al. Effectiveness of upper-limb robotic-assisted therapy in the early rehabilitation phase after stroke: a single-blind, randomised, controlled trial. Ann Phys Rehabil Med. 2019;62:313–20. https://doi.org/10.1016/j.rehab.2019.04.002.

    Article  PubMed  Google Scholar 

  9. Rowe JB, Chan V, Ingemanson ML, Cramer SC, Wolbrecht ET, Reinkensmeyer DJ. Robotic assistance for training finger movement using a hebbian model: a randomized controlled trial. Neurorehabil Neural Repair. 2017;31:769–80. https://doi.org/10.1177/1545968317721975.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Wulf G, Shea CH, Whitacre CA. Physical-guidance benefits in learning a complex motor skill. J Mot Behav. 1998;30:367–80. https://doi.org/10.1080/00222899809601351.

    Article  CAS  PubMed  Google Scholar 

  11. Williams CK, Carnahan H. Motor learning perspectives on haptic training for the upper extremities. IEEE Trans Haptics. 2014;7:240–50. https://doi.org/10.1109/TOH.2013.2297102.

    Article  PubMed  Google Scholar 

  12. Winstein CJ, Pohl PS, Lewthwaite R. Effects of physical guidance and knowledge of results on motor learning: support for the guidance hypothesis. Res Q Exerc Sport. 1994;65:316–23. https://doi.org/10.1080/02701367.1994.10607635.

    Article  CAS  PubMed  Google Scholar 

  13. O’Malley MK, Gupta A, Gen M, Li Y. Shared Control in Haptic Systems for Performance Enhancement and Training. J Dyn Sys, Meas, Control. 2005;128:75–85. https://doi.org/10.1115/1.2168160.

    Article  Google Scholar 

  14. Emken JL, Reinkensmeyer DJ. Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification. IEEE Trans Neural Syst Rehabil Eng. 2005;13:33–9. https://doi.org/10.1109/TNSRE.2004.843173.

    Article  PubMed  Google Scholar 

  15. Hertzog MA. Considerations in determining sample size for pilot studies. Res Nurs Health. 2008;31:180–91. https://doi.org/10.1002/nur.20247.

    Article  PubMed  Google Scholar 

  16. Cramer SC, Sur M, Dobkin BH, O’Brien C, Sanger TD, Trojanowski JQ, et al. Harnessing neuroplasticity for clinical applications. Brain. 2011;134:1591–609. https://doi.org/10.1093/brain/awr039.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Patton JL, Stoykov ME, Kovic M, Mussa-Ivaldi FA. Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors. Exp Brain Res. 2006;168:368–83. https://doi.org/10.1007/s00221-005-0097-8.

    Article  PubMed  Google Scholar 

  18. Marchal-Crespo L, Michels L, Jaeger L, Lopez-Oloriz J, Riener R. Effect of error augmentation on brain activation and motor learning of a complex locomotor task. Front Neurosci 2017;11. https://doi.org/10.3389/fnins.2017.00526.

  19. Marchal-Crespo L, Schneider J, Jaeger L, Riener R. Learning a locomotor task: with or without errors? J Neuroeng Rehabil. 2014;11:25. https://doi.org/10.1186/1743-0003-11-25.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Sigrist R, Rauter G, Riener R, Wolf P. Terminal feedback outperforms concurrent visual, auditory, and haptic feedback in learning a complex rowing-type task. J Mot Behav. 2013;45:455–72. https://doi.org/10.1080/00222895.2013.826169.

    Article  PubMed  Google Scholar 

  21. Özen Ö, Buetler KA, Marchal-Crespo L. Promoting motor variability during robotic assistance enhances motor learning of dynamic tasks. Front Neurosci 2020;14. https://doi.org/10.3389/fnins.2020.600059.

  22. Reinkensmeyer DJ, Housman SJ. “If I can’t do it once, why do it a hundred times?”: connecting volition to movement success in a virtual environment motivates people to exercise the arm after stroke. Virtual Rehabilitation. 2007;2007:44–8. https://doi.org/10.1109/ICVR.2007.4362128.

    Article  Google Scholar 

  23. Marchal Crespo L, Reinkensmeyer DJ. Haptic guidance can enhance motor learning of a steering task. J Mot Behav. 2008;40:545–56. https://doi.org/10.3200/JMBR.40.6.545-557.

    Article  PubMed  Google Scholar 

  24. Lüttgen J, Heuer H. The influence of haptic guidance on the production of spatio-temporal patterns. Hum Mov Sci. 2012;31:519–28. https://doi.org/10.1016/j.humov.2011.07.002.

    Article  PubMed  Google Scholar 

  25. Lüttgen J, Heuer H. The influence of robotic guidance on different types of motor timing. J Mot Behav. 2013;45:249–58. https://doi.org/10.1080/00222895.2013.785926.

    Article  PubMed  Google Scholar 

  26. Duarte JE, Reinkensmeyer DJ. Effects of robotically modulating kinematic variability on motor skill learning and motivation. J Neurophysiol. 2015;113:2682–91. https://doi.org/10.1152/jn.00163.2014.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Rauter G, Sigrist R, Marchal-Crespo L, Vallery H, Riener R, Wolf P. Assistance or challenge? Filling a gap in user-cooperative control. In: 2011 IEEE/RSJ international conference on intelligent robots and systems; 2011. p. 3068–73. https://doi.org/10.1109/IROS.2011.6094832.

  28. Chen X, Agrawal SK. Assisting versus repelling force-feedback for learning of a line following task in a wheelchair. IEEE Trans Neural Syst Rehabil Eng. 2013;21:959–68. https://doi.org/10.1109/TNSRE.2013.2245917.

    Article  PubMed  Google Scholar 

  29. Marchal-Crespo L, Rauter G, Wyss D, Zitzewitz J von, Riener R. Synthesis and control of an assistive robotic tennis trainer. In: 2012 4th IEEE RAS EMBS international conference on biomedical robotics and biomechatronics (BioRob); 2012. p. 355–60. https://doi.org/10.1109/BioRob.2012.6290262.

  30. Srimathveeravalli G, Thenkurussi K. Motor skill training assistance using haptic attributes. In: First joint eurohaptics conference and symposium on haptic interfaces for virtual environment and teleoperator systems. World haptics conference; 2005. p. 452–7. https://doi.org/10.1109/WHC.2005.96.

  31. Bluteau J, Coquillart S, Payan Y, Gentaz E. Haptic guidance improves the visuo-manual tracking of trajectories. PLoS ONE. 2008;3: e1775. https://doi.org/10.1371/journal.pone.0001775.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zondervan DK, Duarte JE, Rowe JB, Reinkensmeyer DJ. Time flies when you are in a groove: using entrainment to mechanical resonance to teach a desired movement distorts the perception of the movement’s timing. Exp Brain Res. 2014;232:1057–70. https://doi.org/10.1007/s00221-013-3819-3.

    Article  PubMed  Google Scholar 

  33. Wei K, Körding K. Relevance of error: what drives motor adaptation? J Neurophysiol. 2009;101:655–64. https://doi.org/10.1152/jn.90545.2008.

    Article  PubMed  Google Scholar 

  34. Shadmehr R, Smith MA, Krakauer JW. Error correction, sensory prediction, and adaptation in motor control. Annu Rev Neurosci. 2010;33:89–108. https://doi.org/10.1146/annurev-neuro-060909-153135.

    Article  CAS  PubMed  Google Scholar 

  35. Lee H, Choi S. Combining haptic guidance and haptic disturbance: an initial study of hybrid haptic assistance for virtual steering task. In: 2014 IEEE haptics symposium (HAPTICS); 2014. p. 159–65.https://doi.org/10.1109/HAPTICS.2014.6775449.

  36. Lee J, Choi S. Effects of haptic guidance and disturbance on motor learning: Potential advantage of haptic disturbance. In: 2010 IEEE haptics symposium; 2010. p. 335–42.https://doi.org/10.1109/HAPTIC.2010.5444635.

  37. Fisher ME, Huang FC, Klamroth-Marganska V, Riener R, Patton JL. Haptic error fields for robotic training. In: 2015 IEEE world haptics conference (WHC); 2015. p. 434–9. https://doi.org/10.1109/WHC.2015.7177750.

  38. Morris D, Tan H, Barbagli F, Chang T, Salisbury K. Haptic feedback enhances force skill learning. In: Proceedings of the second joint EuroHaptics conference and symposium on haptic interfaces for virtual environment and teleoperator systems. Washington, DC, USA: IEEE Computer Society; 2007. p. 21–6. https://doi.org/10.1109/WHC.2007.65.

  39. Rauter G, Sigrist R, Riener R, Wolf P. Learning of temporal and spatial movement aspects: a comparison of four types of haptic control and concurrent visual feedback. IEEE Trans Haptics. 2015;8:421–33. https://doi.org/10.1109/TOH.2015.2431686.

    Article  PubMed  Google Scholar 

  40. Wu HG, Miyamoto YR, Gonzalez Castro LN, Ölveczky BP, Smith MA. Temporal structure of motor variability is dynamically regulated and predicts motor learning ability. Nat Neurosci. 2014;17:312–21. https://doi.org/10.1038/nn.3616.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Cheng S, Sabes PN. Modeling sensorimotor learning with linear dynamical systems. Neural Comput. 2006;18:760–93. https://doi.org/10.1162/089976606775774651.

    Article  PubMed  PubMed Central  Google Scholar 

  42. van der Vliet R, Frens MA, de Vreede L, Jonker ZD, Ribbers GM, Selles RW, et al. Individual differences in motor noise and adaptation rate are optimally related. ENeuro 2018;5:ENEURO.0170-18.2018. https://doi.org/10.1523/ENEURO.0170-18.2018.

  43. Maggioni S, Reinert N, Lünenburger L, Melendez-Calderon A. An adaptive and hybrid end-point/joint impedance controller for lower limb exoskeletons. Front Robot AI. 2018;5:104. https://doi.org/10.3389/frobt.2018.00104.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Bernardoni F, Özen Ö, Buetler K, Marchal-Crespo L. Virtual reality environments and haptic strategies to enhance implicit learning and motivation in robot-assisted training. In: 2019 IEEE 16th international conference on rehabilitation robotics (ICORR); 2019. p. 760–5. https://doi.org/10.1109/ICORR.2019.8779420.

  45. Rauter G, Gerig N, Sigrist R, Riener R, Wolf P. When a robot teaches humans: automated feedback selection accelerates motor learning. Sci Robot 2019;4. https://doi.org/10.1126/scirobotics.aav1560.

  46. Palluel-Germain R, Bara F, Boisferon AH de, Hennion B, Gouagout P, Gentaz E. A visuo-haptic device - telemaque - increases kindergarten children’s handwriting acquisition. In: Second joint EuroHaptics conference and symposium on haptic interfaces for virtual environment and teleoperator systems (WHC’07); 2007. p. 72–7. https://doi.org/10.1109/WHC.2007.13.

  47. Marchal-Crespo L, van Raai M, Rauter G, Wolf P, Riener R. The effect of haptic guidance and visual feedback on learning a complex tennis task. Exp Brain Res. 2013;231:277–91. https://doi.org/10.1007/s00221-013-3690-2.

    Article  PubMed  Google Scholar 

  48. Schmidt RA, Lee TD. Motor control and learning: a behavioral emphasis. Champaign, IL, USA: Human Kinetics Publishers; 2005; 2010.

    Google Scholar 

  49. Shadmehr R, Holcomb HH. Neural correlates of motor memory consolidation. Science. 1997;277:821–5. https://doi.org/10.1126/science.277.5327.821.

    Article  CAS  PubMed  Google Scholar 

  50. McGaugh JL. Memory–a century of consolidation. Science. 2000;287:248–51. https://doi.org/10.1126/science.287.5451.248.

    Article  CAS  PubMed  Google Scholar 

  51. Heuer H, Lüttgen J. Robot assistance of motor learning: a neuro-cognitive perspective. Neurosci Biobehav Rev. 2015;56:222–40. https://doi.org/10.1016/j.neubiorev.2015.07.005.

    Article  PubMed  Google Scholar 

  52. Kitago T, Krakauer JW. Motor learning principles for neurorehabilitation. Handb Clin Neurol. 2013;110:93–103. https://doi.org/10.1016/B978-0-444-52901-5.00008-3.

    Article  PubMed  Google Scholar 

  53. Williams CK, Tremblay L, Carnahan H. It pays to go off-track: practicing with error-augmenting haptic feedback facilitates learning of a curve-tracing task. Front Psychol. 2016;7. https://doi.org/10.3389/fpsyg.2016.02010.

  54. Zhang Z, Sternad D. Back to reality: differences in learning strategy in a simplified virtual and a real throwing task. J Neurophysiol. 2021;125:43–62. https://doi.org/10.1152/jn.00197.2020.

    Article  PubMed  Google Scholar 

  55. Marchal-Crespo L, Tsangaridis P, Obwegeser D, Maggioni S, Riener R. Haptic error modulation outperforms visual error amplification when learning a modified gait pattern. Front Neurosci. 2019;13. https://doi.org/10.3389/fnins.2019.00061.

  56. Powell D, O’Malley MK. The task-dependent efficacy of shared-control haptic guidance paradigms. IEEE Trans Haptics. 2012;5:208–19. https://doi.org/10.1109/TOH.2012.40.

    Article  CAS  PubMed  Google Scholar 

  57. Milot M-H, Marchal-Crespo L, Green CS, Cramer SC, Reinkensmeyer DJ. Comparison of error-amplification and haptic-guidance training techniques for learning of a timing-based motor task by healthy individuals. Exp Brain Res. 2010;201:119–31. https://doi.org/10.1007/s00221-009-2014-z.

    Article  PubMed  Google Scholar 

  58. Sigrist R, Rauter G, Marchal-Crespo L, Riener R, Wolf P. Sonification and haptic feedback in addition to visual feedback enhances complex motor task learning. Exp Brain Res. 2015;233:909–25. https://doi.org/10.1007/s00221-014-4167-7.

    Article  PubMed  Google Scholar 

  59. Lee TD, Ishikura T, Kegel S, Gonzalez D, Passmore S. Head-putter coordination patterns in expert and less skilled golfers. J Mot Behav. 2008;40:267–72. https://doi.org/10.3200/JMBR.40.4.267-272.

    Article  PubMed  Google Scholar 

  60. Su ELM, Ganesh G, Yeong CF, Teo CL, Ang WT, Burdet E. Effect of grip force and training in unstable dynamics on micromanipulation accuracy. IEEE Trans Haptics. 2011;4:167–74. https://doi.org/10.1109/TOH.2011.33.

    Article  CAS  PubMed  Google Scholar 

  61. Feygin D, Keehner M, Tendick R. Haptic guidance: experimental evaluation of a haptic training method for a perceptual motor skill. In: 10th symposium on haptic interfaces for virtual environment and teleoperator systems. HAPTICS 2002. Proceedings; 2002. p. 40–7. https://doi.org/10.1109/HAPTIC.2002.998939.

  62. Fitts PM. Perceptual-motor skill learning. In: Melton AW, editor. Categories of human learning. Academic Press; 1964. p. 243–85. https://doi.org/10.1016/B978-1-4832-3145-7.50016-9.

  63. Penalver-Andres J, Buetler KA, Koenig T, Müri RM, Marchal-Crespo L. Providing task instructions during motor training enhances performance and modulates attentional brain networks. Front Neurosci. 2021;15. https://doi.org/10.3389/fnins.2021.755721

  64. Wulf G, Lewthwaite R. Optimizing performance through intrinsic motivation and attention for learning: the OPTIMAL theory of motor learning. Psychon Bull Rev. 2016;23:1382–414. https://doi.org/10.3758/s13423-015-0999-9.

    Article  PubMed  Google Scholar 

  65. Lohse KR, Boyd LA, Hodges NJ. Engaging environments enhance motor skill learning in a computer gaming task. J Mot Behav. 2016;48:172–82. https://doi.org/10.1080/00222895.2015.1068158.

    Article  PubMed  Google Scholar 

  66. Endo S, Fröhner J, Musić S, Hirche S, Beckerle P. Effect of external force on agency in physical human-machine interaction. Front Hum Neurosci. 2020;14:114. https://doi.org/10.3389/fnhum.2020.00114.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Darzi A, Wondra T, McCrea S, Novak D. Classification of multiple psychological dimensions in computer game players using physiology, performance, and personality characteristics. Front Neurosci. 2019;13:1278. https://doi.org/10.3389/fnins.2019.01278.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Guadagnoli MA, Lee TD. Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. J Mot Behav. 2004;36:212–24. https://doi.org/10.3200/JMBR.36.2.212-224.

    Article  PubMed  Google Scholar 

  69. Guthrie ER. Psychology of learning. Oxford, England: Harper; 1935.

    Google Scholar 

  70. Schmidt RA, Wrisberg CA. Motor learning and performance: a situation-based learning approach, 4th ed. Champaign, IL, US: Human Kinetics; 2008.

    Google Scholar 

  71. Fleishman EA, Quaintance MK, Broedling LA. Taxonomies of human performance: the description of human tasks. San Diego, CA, US: Academic; 1984.

    Google Scholar 

  72. Wulf G, Shea CH. Principles derived from the study of simple skills do not generalize to complex skill learning. Psychon Bull Rev. 2002;9:185–211. https://doi.org/10.3758/BF03196276.

    Article  PubMed  Google Scholar 

  73. Buchanan JJ, Park J-H, Ryu YU, Shea CH. Discrete and cyclical units of action in a mixed target pair aiming task. Exp Brain Res. 2003;150:473–89. https://doi.org/10.1007/s00221-003-1471-z.

    Article  PubMed  Google Scholar 

  74. Schaal S, Sternad D, Osu R, Kawato M. Rhythmic arm movement is not discrete. Nat Neurosci. 2004;7:1136–43. https://doi.org/10.1038/nn1322.

    Article  CAS  PubMed  Google Scholar 

  75. Marchal-Crespo, Rappo N., Riener R. The effectiveness of robotic training depends on motor task characteristics. Exp Brain Res. 2017. https://doi.org/10.1007/s00221-017-5099-9.

  76. Basalp E, Marchal-Crespo L, Rauter G, Riener R, Wolf P. Rowing simulator modulates water density to foster motor learning. Front Robot AI. 2019;6. https://doi.org/10.3389/frobt.2019.00074.

  77. Marchal-Crespo L, McHughen S, Cramer SC, Reinkensmeyer DJ. The effect of haptic guidance, aging, and initial skill level on motor learning of a steering task. Exp Brain Res. 2010;201:209–20. https://doi.org/10.1007/s00221-009-2026-8.

    Article  PubMed  Google Scholar 

  78. Chen X, Ragonesi C, Galloway JC, Agrawal SK. Training toddlers seated on mobile robots to drive indoors amidst obstacles. IEEE Trans Neural Syst Rehabil Eng. 2011;19:271–9. https://doi.org/10.1109/TNSRE.2011.2114370.

    Article  PubMed  Google Scholar 

  79. Bouchard AE, Corriveau H, Milot M-H. A single robotic session that guides or increases movement error in survivors post-chronic stroke: which intervention is best to boost the learning of a timing task? Disabil Rehabil. 2017;39:1607–14. https://doi.org/10.1080/09638288.2016.1205151.

    Article  PubMed  Google Scholar 

  80. Marchal-Crespo L, Baumann T, Imobersteg M, Maassen S, Riener R. Experimental evaluation of a mixed controller that amplifies spatial errors and reduces timing errors. Front Robot AI. 2017;4. https://doi.org/10.3389/frobt.2017.00019.

  81. Rossi C, Chau CW, Leech KA, Statton MA, Gonzalez AJ, Bastian AJ. The capacity to learn new motor and perceptual calibrations develops concurrently in childhood. Sci Rep. 2019;9:9322. https://doi.org/10.1038/s41598-019-45074-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Rozario SV, Housman S, Kovic M, Kenyon RV, Patton JL. Therapist-mediated post-stroke rehabilitation using haptic/graphic error augmentation. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:1151–6. https://doi.org/10.1109/IEMBS.2009.5333875.

    Article  PubMed  Google Scholar 

  83. Huang FC, Patton JL. Augmented dynamics and motor exploration as training for stroke. IEEE Trans Biomed Eng. 2013;60:838–44. https://doi.org/10.1109/TBME.2012.2192116.

    Article  PubMed  Google Scholar 

  84. Cesqui B, Aliboni S, Mazzoleni S, Carrozza MC, Posteraro F, Micera S. On the use of divergent force fields in robot-mediated neurorehabilitation. In: 2008 2nd IEEE RAS EMBS international conference on biomedical robotics and biomechatronics; 2008. p. 854–61. https://doi.org/10.1109/BIOROB.2008.4762927.

  85. Tropea P, Cesqui B, Monaco V, Aliboni S, Posteraro F, Micera S. Effects of the alternate combination of “error-enhancing” and “active assistive” robot-mediated treatments on stroke patients. IEEE J Transl Eng Health Med. 2013;1:2100109. https://doi.org/10.1109/JTEHM.2013.2271898.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Li Y, Huegel JC, Patoglu V, O’Malley MK. Progressive shared control for training in virtual environments. In: World haptics 2009 - third joint eurohaptics conference and symposium on haptic interfaces for virtual environment and teleoperator systems; 2009. p. 332–7. https://doi.org/10.1109/WHC.2009.4810873.

  87. Li Y, Carboni G, Gonzalez F, Campolo D, Burdet E. Differential game theory for versatile physical human–robot interaction. Nat Mach Intell. 2019;1:36–43. https://doi.org/10.1038/s42256-018-0010-3.

    Article  Google Scholar 

  88. Tamagnone I, Basteris A, Sanguineti V. Robot-assisted acquisition of a motor skill: evolution of performance and effort. In: 2012 4th IEEE RAS EMBS international conference on biomedical robotics and biomechatronics (BioRob); 2012. p. 1016–21. https://doi.org/10.1109/BioRob.2012.6290881.

  89. Xu G, Gao X, Pan L, Chen S, Wang Q, Zhu B, et al. Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation. Int J Adv Rob Syst. 2018;15:1729881418806433. https://doi.org/10.1177/1729881418806433.

    Article  Google Scholar 

  90. Giang C, Pirondini E, Kinany N, Pierella C, Panarese A, Coscia M, et al. Motor improvement estimation and task adaptation for personalized robot-aided therapy: a feasibility study. Biomed Eng Online. 2020;19:33. https://doi.org/10.1186/s12938-020-00779-y.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Menner M, Berntorp K, Zeilinger MN, Di Cairano S. Inverse learning for data-driven calibration of model-based statistical path planning. IEEE Trans Intell Veh. 2020:1–1. https://doi.org/10.1109/TIV.2020.3000323.

  92. Doran D, Schulz S, Besold TR. What does explainable AI really mean? a new conceptualization of perspectives; 2017. arXiv:171000794 [Cs].

  93. Garcez A d’Avila, Gori M, Lamb LC, Serafini L, Spranger M, Tran SN. Neural-symbolic computing: an effective methodology for principled integration of machine learning and reasoning; 2019. arXiv:190506088 [Cs].

  94. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1:206–15. https://doi.org/10.1038/s42256-019-0048-x.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Sternad D, Duarte M, Katsumata H, Schaal S. Bouncing a ball: tuning into dynamic stability. J Exp Psychol Hum Percept Perform. 2001;27:1163–84. https://doi.org/10.1037/0096-1523.27.5.1163.

  96. Danion F, Diamond JS, Flanagan JR. The role of haptic feedback when manipulating nonrigid objects. J Neurophysiol. 2012;107:433–41. https://doi.org/10.1152/jn.00738.2011.

    Article  PubMed  Google Scholar 

  97. Borich MR, Brodie SM, Gray WA, Ionta S, Boyd LA. Understanding the role of the primary somatosensory cortex: Opportunities for rehabilitation. Neuropsychologia. 2015;79:246–55. https://doi.org/10.1016/j.neuropsychologia.2015.07.007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Gassert R, Dietz V. Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective. J Neuroeng Rehabil. 2018;15:46. https://doi.org/10.1186/s12984-018-0383-x.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Özen Ö, Buetler KA, Marchal-Crespo L. Towards functional robotic training: motor learning of dynamic tasks is enhanced by haptic rendering but hampered by arm weight support. J Neuroeng Rehabil. 2022;19:19. https://doi.org/10.1186/s12984-022-00993-w.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Bertani R, Melegari C, De Cola MC, Bramanti A, Bramanti P, Calabrò RS. Effects of robot-assisted upper limb rehabilitation in stroke patients: a systematic review with meta-analysis. Neurol Sci. 2017;38:1561–9. https://doi.org/10.1007/s10072-017-2995-5.

    Article  PubMed  Google Scholar 

  101. Veerbeek JM, Langbroek-Amersfoort AC, van Wegen EEH, Meskers CGM, Kwakkel G. Effects of robot-assisted therapy for the upper limb after stroke. Neurorehabil Neural Repair. 2017;31:107–21. https://doi.org/10.1177/1545968316666957.

    Article  PubMed  Google Scholar 

  102. Handelzalts S, Ballardini G, Avraham C, Pagano M, Casadio M, Nisky I. Integrating tactile feedback technologies into home-based telerehabilitation: opportunities and challenges in light of COVID-19 pandemic. Front Neurorobot. 2021;15:4. https://doi.org/10.3389/fnbot.2021.617636.

    Article  Google Scholar 

  103. Pezent E, Fani S, Clark J, Bianchi M, O’Malley MK. Spatially separating haptic guidance from task dynamics through wearable devices. IEEE Trans Haptics. 2019;12:581–93. https://doi.org/10.1109/TOH.2019.2919281.

    Article  PubMed  Google Scholar 

  104. Shergill SS, Bays PM, Frith CD, Wolpert DM. Two eyes for an eye: the neuroscience of force escalation. Science. 2003;301:187–187. https://doi.org/10.1126/science.1085327.

    Article  CAS  PubMed  Google Scholar 

  105. Walsh LD, Taylor JL, Gandevia SC. Overestimation of force during matching of externally generated forces. J Physiol. 2011;589:547–57. https://doi.org/10.1113/jphysiol.2010.198689.

    Article  CAS  PubMed  Google Scholar 

  106. Onneweer B, Mugge W, Schouten AC. Force reproduction error depends on force level, whereas the position reproduction error does not. IEEE Trans Haptics. 2016;9:54–61. https://doi.org/10.1109/TOH.2015.2508799.

    Article  PubMed  Google Scholar 

  107. Mugge W, Schuurmans J, Schouten AC, van der Helm FCT. Sensory weighting of force and position feedback in human motor control tasks. J Neurosci. 2009;29:5476–82. https://doi.org/10.1523/JNEUROSCI.0116-09.2009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. van Beek FE, Tiest WMB, Kappers AML. Anisotropy in the haptic perception of force direction and magnitude. IEEE Trans Haptics. 2013;6:399–407. https://doi.org/10.1109/TOH.2013.37.

    Article  PubMed  Google Scholar 

  109. Toffin D, McIntyre J, Droulez J, Kemeny A, Berthoz A. Perception and reproduction of force direction in the horizontal plane. J Neurophysiol. 2003;90:3040–53. https://doi.org/10.1152/jn.00271.2003.

    Article  CAS  PubMed  Google Scholar 

  110. Abbink DA, Carlson T, Mulder M, de Winter JCF, Aminravan F, Gibo TL, et al. A topology of shared control systems—finding common ground in diversity. IEEE Trans Hum-Mach Syst. 2018;48:509–25. https://doi.org/10.1109/THMS.2018.2791570.

    Article  Google Scholar 

  111. de Mello Monteiro CB, Massetti T, da Silva TD, van der Kamp J, de Abreu LC, Leone C, et al. Transfer of motor learning from virtual to natural environments in individuals with cerebral palsy. Res Dev Disabil. 2014;35:2430–7. https://doi.org/10.1016/j.ridd.2014.06.006.

    Article  PubMed  Google Scholar 

  112. Wenk N, Penalver-Andres J, Palma R, Buetler KA, Muri R, Nef T, et al. Reaching in several realities: motor and cognitive benefits of different visualization technologies. IEEE Int Conf Rehabil Robot. 2019;2019:1037–42. https://doi.org/10.1109/ICORR.2019.8779366.

    Article  PubMed  Google Scholar 

  113. Wenk N, Penalver-Andres J, Buetler KA, Nef T, Müri RM, Marchal-Crespo L. Effect of immersive visualization technologies on cognitive load, motivation, usability, and embodiment. Virtual Real. 2021. https://doi.org/10.1007/s10055-021-00565-8.

    Article  PubMed  PubMed Central  Google Scholar 

  114. Brookes J, Mushtaq F, Jamieson E, Fath AJ, Bingham G, Culmer P, et al. Exploring disturbance as a force for good in motor learning. PLoS ONE. 2020;15:e0224055. https://doi.org/10.1371/journal.pone.0224055.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Marchal-Crespo L, López-Olóriz J, Jaeger L, Riener R. Optimizing learning of a locomotor task: amplifying errors as needed. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:5304–7. https://doi.org/10.1109/EMBC.2014.6944823.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank Dr. Peter Wolf and Dr. Ekin Basalp for their support during the literature research. This work was supported in part by the Swiss National Science Foundation (SNF) through the grant PP00P2163800, the Dutch Research Council (NWO) Talent Program VIDI TTW 2020, and AiTech, TU Delft’s initiative on Meaningful Human Control.

Author information

Authors and Affiliations

  1. Department of Cognitive Robotics, TU Delft/Faculty 3mE (Mechanical, Maritime and Materials Engineering), Mekelweg 2, 2628 CD, Delft, The Netherlands

    Niek Beckers & Laura Marchal-Crespo

  2. ARTORG Center for Biomedical Engineering Research, University of Bern, Freiburgstrasse 3, 3010, Bern, Switzerland

    Laura Marchal-Crespo

Authors
  1. Niek Beckers
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laura Marchal-Crespo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laura Marchal-Crespo .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Aerospace Engineering and Department of Anatomy and Neurobiology, University of California, Irvine, CA, USA

    David J. Reinkensmeyer

  2. Delft University of Technology, Delft, The Netherlands

    Laura Marchal-Crespo

  3. Spinal Cord Injury Center, University Hospital Balgrist, Zürich, Switzerland

    Volker Dietz

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Beckers, N., Marchal-Crespo, L. (2022). The Role of Haptic Interactions with Robots for Promoting Motor Learning. In: Reinkensmeyer, D.J., Marchal-Crespo, L., Dietz, V. (eds) Neurorehabilitation Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-08995-4_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08995-4_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08994-7

  • Online ISBN: 978-3-031-08995-4

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation