Skip to main content

Advertisement

Log in

Human-Robot Interaction in Rehabilitation and Assistance: a Review

  • Rehabilitation and Assistive Robotics (M Raison and S Achiche, Section Editors)
  • Published:
Current Robotics Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Research in assistive and rehabilitation robotics is a growing, promising, and challenging field emerged due to various social and medical needs such as aging populations, neuromuscular, and musculoskeletal disorders. Such robots can be used in various day-to-day scenarios or to support motor functionality, training, and rehabilitation. This paper reflects on the human-robot interaction perspective in rehabilitation and assistive robotics and reports on current issues and developments in the field.

Recent Findings

The survey on the literature reveals that new efforts are put on utilizing machine learning approaches alongside novel developments in sensing technology to adapt the systems with user routines in terms of activities for assistive systems and exercises for rehabilitation devices to fit each user’s need and maximize their effectiveness.

Summary

A review of recent research and development efforts on human-robot interaction in assistive and rehabilitation robotics is presented in this paper. First, different subdomains in assistive and rehabilitation robotic research are identified, and accordingly, a survey on the background and trends of such developments is provided.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Marchal-Crespo L, Reinkensmeyer DJ. Review of control strategies for robotic movement training after neurologic injury. J Neuroeng Rehab. 2009;6(1):20.

    Google Scholar 

  2. Krebs HI, Palazzolo JJ, Dipietro L, Ferraro M, Krol J, Rannekleiv K, et al. Rehabilitation robotics: performance-based progressive robot-assisted therapy. Auton Robot. 2003;15(1):7–20.

    Google Scholar 

  3. Leonardis D, Barsotti M, Loconsole C, Solazzi M, Troncossi M, Mazzotti C, et al. An EMG-controlled robotic hand exoskeleton for bilateral rehabilitation. IEEE Trans Haptics. 2015;8(2):140–51.

    Google Scholar 

  4. Lebrasseur A, Lettre J, Routhier F, Archambault PS, Campeau-Lecours A. Assistive robotic arm: evaluation of the performance of intelligent algorithms. Assist Technol. 2019:1–10 This paper provides comparative insights on how an intelligent algorithm should perfom when embedded in the control system of an assistive robotic manipulator.

  5. Windrich M, Grimmer M, Christ O, Rinderknecht S, Beckerle P. Active lower limb prosthetics: a systematic review of design issues and solutions. Biomed Eng Online. 2016;15(3):140.

    Google Scholar 

  6. Huysamen K, de Looze M, Bosch T, Ortiz J, Toxiri S, O'Sullivan LW. Assessment of an active industrial exoskeleton to aid dynamic lifting and lowering manual handling tasks. Appl Ergon. 2018;68:125–31.

    Google Scholar 

  7. Shi L, Yu Y, Xiao N, Gan D. Biologically inspired and rehabilitation robotics. Appl Bionics Biomech. 2019;2019:1–2.

    Google Scholar 

  8. Yan T, Cempini M, Oddo CM, Vitiello N. Review of assistive strategies in powered lower-limb orthoses and exoskeletons. Robot Auton Syst. 2015;64:120–36.

    Google Scholar 

  9. Beckerle P, Christ O, Wojtusch J, Schuy J, Wolff K, Rinderknecht S, et al., editors. Design and control of a robot for the assessment of psychological factors in prosthetic development. 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC): IEEE; 2012.

  10. Adams JA, editor. Human-robot interaction design: understanding user needs and requirements. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Los Angeles, CA: SAGE Publications Sage CA; 2005.

    Google Scholar 

  11. Goodrich MA, Schultz AC. Human-robot interaction: a survey. Found Trends Hum-Comput Interac. 2007;1(3):203–75.

    MATH  Google Scholar 

  12. Zheng Y, Zhong P, Liu K, Yang K, Yue Q. Human motion capture system based 3D reconstruction on rehabilitation assistance stability of lower limb exoskeleton robot climbing upstairs posture. IEEE Sensors J. 2019.

  13. Niku SB. Introduction to robotics: analysis, control, applications: John Wiley & Sons; 2020.

  14. Lynch KM. Park FC. Modern robotics: Cambridge University Press; 2017.

    Google Scholar 

  15. Luo S, Bimbo J, Dahiya R, Liu H. Robotic tactile perception of object properties: a review. Mechatronics. 2017;48:54–67.

    Google Scholar 

  16. Foster ME. Natural language generation for social robotics: opportunities and challenges. Philos Trans R Soc B. 2019;374(1771):20180027.

    Google Scholar 

  17. Pan L, Song A, Duan S, Yu Z. Patient-centered robot-aided passive neurorehabilitation exercise based on safety-motion decision-making mechanism. Biomed Res Int. 2017;2017:1–11.

    Google Scholar 

  18. Colombo R, editor. Robot assisted exercise: modelling the recovery process to personalise therapy. converging clinical and engineering research on neurorehabilitation III: Proceedings of the 4th International Conference on NeuroRehabilitation (ICNR2018), October 16–20, 2018, Pisa, Italy: Springer; 2018.

  19. Becker S, Bergamo F, Williams S, Disselhorst-Klug C. Comparison of muscular activity and movement performance in robot-assisted and freely performed exercises. IEEE Trans Neural Syst Rehab Eng. 2018;27(1):43–50.

    Google Scholar 

  20. Akdogan E, Aktan ME. Impedance control applications in therapeutic exercise robots. Control Systems Design of Bio-Robotics and Bio-mechatronics with Advanced Applications: Elsevier; 2020. p. 395–443. This paper explains a very common and essential HRI contro strategy which is impedance control, applied to important therapeutic robotic exercises.

  21. Michmizos KP, Krebs HI. Pediatric robotic rehabilitation: current knowledge and future trends in treating children with sensorimotor impairments. NeuroRehabilitation. 2017;41(1):69–76.

    Google Scholar 

  22. Campeau-Lecours A, Lamontagne H, Latour S, Fauteux P, Maheu V, Boucher F, et al. Kinova modular robot arms for service robotics applications. Rapid Automation: Concepts, Methodologies, Tools, and Applications. IGI Global. 2019:693–719.

  23. Kumar V, Hote YV, Jain S, editors. Review of exoskeleton: history, design and control. 2019 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE): IEEE; 2019.

  24. Abdi J, Al-Hindawi A, Ng T, Vizcaychipi MP. Scoping review on the use of socially assistive robot technology in elderly care. BMJ Open. 2018;8(2).

  25. Organization WH. Guidelines on the provision of manual wheelchairs in less resourced settings. 2008.

    Google Scholar 

  26. Ghorbel M, Pineau J, Gourdeau R, Javdani S, Srinivasa S. A decision-theoretic approach for the collaborative control of a smart wheelchair. Int J Soc Robot. 2018;10(1):131–45.

    Google Scholar 

  27. Schwesinger D, Shariati A, Montella C, Spletzer J. A smart wheelchair ecosystem for autonomous navigation in urban environments. Auton Robot. 2017;41(3):519–38.

    Google Scholar 

  28. Devigne L, Pasteau F, Babel M, Narayanan VK, Guegan S, Gallien P, editors. Design of a haptic guidance solution for assisted power wheelchair navigation. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC): IEEE; 2018.

  29. Chuy OY, Herrero J, Al-Selwadi A, Mooers A. Control and evaluation of a motorized attendant wheelchair with haptic interface. J Med Dev. 2019;13(1).

  30. MSI S, Nordin S, Ali AM, editors. Voice control intelligent wheelchair movement using CNNs. 2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS): IEEE; 2019.

  31. Rabhi Y, Mrabet M, Fnaiech F. Intelligent control wheelchair using a new visual joystick. J Healthcare Eng. 2018;2018:1–20.

    Google Scholar 

  32. Rabhi Y, Mrabet M, Fnaiech F. A facial expression controlled wheelchair for people with disabilities. Comput Methods Prog Biomed. 2018;165:89–105.

    Google Scholar 

  33. Rakasena E, Herdiman L, editors. Electric wheelchair with forward-reverse control using electromyography (EMG) control of arm muscle. Journal of Physics: Conference Series; 2020.

    Google Scholar 

  34. Kumar B, Paul Y, Jaswal RA, editors. Development of EMG controlled electric wheelchair using SVM and kNN classifier for SCI patients. International Conference on Advanced Informatics for Computing Research: Springer; 2019.

  35. Zgallai W, Brown JT, Ibrahim A, Mahmood F, Mohammad K, Khalfan M, et al., editors. Deep learning AI application to an EEG driven BCI smart wheelchair. 2019 Advances in Science and Engineering Technology International Conferences (ASET): IEEE; 2019. This paper is a very good and detailed example of using Deep Learning approaches to form an intelligent assistive control system using bio-signals.

  36. Coelho FJdOR. Multimodal interface for an intelligent wheelchair. 2019.

    Google Scholar 

  37. Wachaja A, Agarwal P, Zink M, Adame MR, Möller K, Burgard W. Navigating blind people with walking impairments using a smart walker. Auton Robot. 2017;41(3):555–73.

    Google Scholar 

  38. Alves J, Seabra E, Caetano I, Gonçalves J, Serra J, Martins M, et al., editors. Considerations and mechanical modifications on a smart walker. 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC): IEEE; 2016.

  39. Caetano I, Alves J, Gonçalves J, Martins M, Santos CP, editors. Development of a biofeedback approach using body tracking with active depth sensor in ASBGo smart walker. 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC): IEEE; 2016.

  40. Poirier S, Routhier F, Campeau-Lecours A, editors. Voice control interface prototype for assistive robots for people living with upper limb disabilities. 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR): IEEE; 2019.

  41. Sun L, Sa W, Chen H, Chen Y, editors. A novel human computer interface based on electrooculogram signal for smart assistive robots. 2018 IEEE International Conference on Mechatronics and Automation (ICMA): IEEE; 2018.

  42. Leroux M, Raison M, Adadja T, Achiche S, editors. Combination of eyetracking and computer vision for robotics control. 2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA): IEEE; 2015.

  43. Haseeb MA, Kyrarini M, Jiang S, Ristic-Durrant D, Gräser A, editors. Head gesture-based control for assistive robots. Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference; 2018.

    Google Scholar 

  44. Schabron B, Reust A, Desai J, Yihun Y, editors. Integration of forearm sEMG signals with IMU sensors for trajectory planning and control of assistive robotic arm. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): IEEE; 2019.

  45. Bousquet-Jette C, Achiche S, Beaini D, Cio YL-K, Leblond-Ménard C, Raison M. Fast scene analysis using vision and artificial intelligence for object prehension by an assistive robot. Eng Appl Artif Intell. 2017;63:33–44.

    Google Scholar 

  46. Chu F-J, Xu R, Zhang Z, Vela PA, Ghovanloo M, editors. The helping hand: an assistive manipulation framework using augmented reality and tongue-drive interfaces. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): IEEE; 2018.

  47. Sasaki M, Onishi K, Stefanov D, Kamata K, Nakayama A, Yoshikawa M, et al. Tongue interface based on surface EMG signals of suprahyoid muscles. Robomech J. 2016;3(1):9.

    Google Scholar 

  48. Cio Y-SL-K, Raison M, Ménard CL, Achiche S. Proof of concept of an assistive robotic arm control using artificial stereovision and eye-tracking. IEEE Trans Neural Syst Rehab Eng. 2019;27(12):2344–52.

    Google Scholar 

  49. Kæseler RL, Leerskov K, Struijk LA, Dremstrup K, Jochumsen M, editors. Designing a brain computer interface for control of an assistive robotic manipulator using steady state visually evoked potentials. 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR): IEEE; 2019.

  50. Zeng H, Shen Y, Hu X, Song A, Xu B, Li H, et al. Semi-autonomous robotic arm reaching with hybrid gaze–brain machine interface. Front Neurorobot. 2020;13:111.

    Google Scholar 

  51. Sharkawy A-N, Koustoumpardis PN, Aspragathos N. Human–robot collisions detection for safe human–robot interaction using one multi-input–output neural network. Soft Comput. 2019:1–33.

  52. Li Z, Liu J, Huang Z, Peng Y, Pu H, Ding L. Adaptive impedance control of human–robot cooperation using reinforcement learning. IEEE Trans Ind Electron. 2017;64(10):8013–22.

    Google Scholar 

  53. Sangiovanni B, Rendiniello A, Incremona GP, Ferrara A, Piastra M, editors. Deep reinforcement learning for collision avoidance of robotic manipulators. 2018 European Control Conference (ECC): IEEE; 2018.

  54. Liu Z, Ai Q, Liu Y, Zuo J, Zhang X, Meng W, et al., editors. An optimal motion planning method of 7-DOF robotic arm for upper limb movement assistance. 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM): IEEE; 2019.

  55. Guo W, Sheng X, Liu H, Zhu X. Toward an enhanced human–machine interface for upper-limb prosthesis control with combined EMG and NIRS signals. IEEE Trans Hum-Mach Syst. 2017;47(4):564–75 The research carried out in this paper can be a guide on how to implement and enhance bio-signal based human–machine interfaces.

  56. Pancholi S, Joshi AM. Portable EMG data acquisition module for upper limb prosthesis application. IEEE Sensors J. 2018;18(8):3436–43.

    Google Scholar 

  57. Zizoua C, Raison M, Boukhenous S, Attari M, Achiche S. Development of a bracelet with strain-gauge matrix for movement intention identification in traumatic amputees. IEEE Sensors J. 2017;17(8):2464–71.

    Google Scholar 

  58. Wijk U, Carlsson I. Forearm amputees' views of prosthesis use and sensory feedback. J Hand Ther. 2015;28(3):269–78.

    Google Scholar 

  59. Graczyk EL, Schiefer MA, Saal HP, Delhaye BP, Bensmaia SJ, Tyler DJ. The neural basis of perceived intensity in natural and artificial touch. Sci Transl Med. 2016;8(362):362ra142-362ra142.

    Google Scholar 

  60. Marasco PD, Hebert JS, Sensinger JW, Shell CE, Schofield JS, Thumser ZC, et al. Illusory movement perception improves motor control for prosthetic hands. Sci Transl Med. 2018;10(432).

  61. Valle G, Mazzoni A, Iberite F, D’Anna E, Strauss I, Granata G, et al. Biomimetic intraneural sensory feedback enhances sensation naturalness, tactile sensitivity, and manual dexterity in a bidirectional prosthesis. Neuron. 2018;100(1):37–45 e7.

    Google Scholar 

  62. Vu PP, Vaskov AK, Irwin ZT, Henning PT, Lueders DR, Laidlaw AT, et al. A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees. Sci Transl Med. 2020;12(533).

  63. Mastinu E, Doguet P, Botquin Y, Håkansson B, Ortiz-Catalan M. Embedded system for prosthetic control using implanted neuromuscular interfaces accessed via an osseointegrated implant. IEEE Trans Biomed Circ Syst. 2017;11(4):867–77.

    Google Scholar 

  64. Samuel OW, Zhou H, Li X, Wang H, Zhang H, Sangaiah AK, et al. Pattern recognition of electromyography signals based on novel time domain features for amputees' limb motion classification. Comput Electr Eng. 2018;67:646–55.

    Google Scholar 

  65. Samuel OW, Li X, Geng Y, Asogbon MG, Fang P, Huang Z, et al. Resolving the adverse impact of mobility on myoelectric pattern recognition in upper-limb multifunctional prostheses. Comput Biol Med. 2017;90:76–87.

    Google Scholar 

  66. Ameri A, Akhaee MA, Scheme E, Englehart K. Real-time, simultaneous myoelectric control using a convolutional neural network. PLoS One. 2018;13(9).

  67. Atzori M, Cognolato M, Müller H. Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands. Front Neurorobot. 2016;10:9.

    Google Scholar 

  68. Dantas H, Warren DJ, Wendelken SM, Davis TS, Clark GA, Mathews VJ. Deep learning movement intent decoders trained with dataset aggregation for prosthetic limb control. IEEE Trans Biomed Eng. 2019;66(11):3192–203.

    Google Scholar 

  69. Gaudet G, Raison M, Achiche S. Classification of upper limb phantom movements in transhumeral amputees using electromyographic and kinematic features. Eng Appl Artif Intell. 2018;68:153–64.

    Google Scholar 

  70. Van der Loos HM, Reinkensmeyer DJ, Guglielmelli E. Rehabilitation and health care robotics. Springer handbook of robotics. Springer; 2016. p. 1685–1728.

  71. Gull MA, Bai S, Bak T. A review on design of upper limb exoskeletons. Robotics. 2020;9(1):16 This paper provides important insights about designing upperlimb robotic prosthetics considering their interactions with the users, and describes multiple examples with a critical approach.

  72. Tucker MR, Olivier J, Pagel A, Bleuler H, Bouri M, Lambercy O, et al. Control strategies for active lower extremity prosthetics and orthotics: a review. J Neuroeng Rehab. 2015;12(1):1.

    Google Scholar 

  73. Gordleeva SY, Lobov SA, Grigorev NA, Savosenkov AO, Shamshin MO, Lukoyanov MV, et al. Real-time EEG–EMG human–machine interface-based control system for a lower-limb exoskeleton. IEEE Access. 2020.

  74. Dhindsa IS, Agarwal R, Ryait HS, editors. Joint angle prediction from Emg signals for lower limb exoskeleton. International Conference and Youth School on Information Technology and Nanotechnology (ITNT-2016); 2016; Samara.

    Google Scholar 

  75. Gui K, Liu H, Zhang D. A practical and adaptive method to achieve EMG-based torque estimation for a robotic exoskeleton. IEEE/ASME Trans Mechatron. 2019;24(2):483–94.

    Google Scholar 

  76. Beil J, Ehrenberger I, Scherer C, Mandery C, Asfour T, editors. Human motion classification based on multi-modal sensor data for lower limb exoskeletons. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): IEEE; 2018.

  77. Barron O, Raison M, Achiche S. Control of transhumeral prostheses based on electromyography pattern recognition: from amputees to deep learning. Powered Prostheses: Elsevier; 2020. p. 1–21.

  78. Trigili E, Grazi L, Crea S, Accogli A, Carpaneto J, Micera S, et al. Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks. J Neuroeng Rehab. 2019;16(1):45.

    Google Scholar 

  79. Chowdhury A, Raza H, Dutta A, Prasad G. EEG-EMG based hybrid brain computer interface for triggering hand exoskeleton for neuro-rehabilitation. Proceedings of the Advances in Robotics; 2017. p. 1–6.

    Google Scholar 

  80. Accogli A, Grazi L, Crea S, Panarese A, Carpaneto J, Vitiello N, et al. EMG-based detection of user’s intentions for human-machine shared control of an assistive upper-limb exoskeleton. Wearable Robotics: Challenges and Trends: Springer; 2017. p. 181–5.

  81. Irastorza-Landa N, Sarasola-Sanz A, Shiman F, López-Larraz E, Klein J, Valencia D, et al. EMG discrete classification towards a myoelectric control of a robotic exoskeleton in motor rehabilitation. Converging Clinical and Engineering Research on Neurorehabilitation II: Springer; 2017. p. 159–63.

  82. Hogan N. Impedance control: an approach to manipulation: part I—theory. 1985

    MATH  Google Scholar 

  83. Hogan N. Impedance control: an approach to manipulation: part II—implementation. 1985

    MATH  Google Scholar 

  84. Alqaudi B, Modares H, Ranatunga I, Tousif SM, Lewis FL, Popa DO. Model reference adaptive impedance control for physical human-robot interaction. Control Theory Technol. 2016;14(1):68–82.

    MathSciNet  MATH  Google Scholar 

  85. Li Z, Huang Z, He W, Su C-Y. Adaptive impedance control for an upper limb robotic exoskeleton using biological signals. IEEE Trans Ind Electron. 2016;64(2):1664–74.

    Google Scholar 

  86. Figueiredo J, Félix P, Santos CP, Moreno JC, editors. Towards human-knee orthosis interaction based on adaptive impedance control through stiffness adjustment. 2017 International Conference on Rehabilitation Robotics (ICORR): IEEE; 2017.

  87. Geoffroy P, Bordron O, Mansard N, Raison M, Stasse O, Bretl T, editors. A two-stage suboptimal approximation for variable compliance and torque control. 2014 European Control Conference (ECC): IEEE; 2014.

  88. Azimi V. Model-based robust and adaptive control of transfemoral prostheses: theory, simulation, and experiments: Georgia Institute of Technology; 2020.

  89. Torabi M, Sharifi M, Vossoughi G. Robust adaptive sliding mode admittance control of exoskeleton rehabilitation robots. Sci Iran Trans B Mech Eng. 2018;25(5):2628–42.

    Google Scholar 

  90. Burgar CG, Lum PS, Shor PC, Van der Loos HM. Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience. J Rehabil Res Dev. 2000;37(6):663–74.

    Google Scholar 

  91. Serpelloni M, Tiboni M, Lancini M, Pasinetti S, Vertuan A, Gobbo M, editors. Preliminary study of a robotic rehabilitation system driven by EMG for hand mirroring. 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA): IEEE; 2016.

  92. Sarasola-Sanz A, Irastorza-Landa N, López-Larraz E, Bibián C, Helmhold F, Broetz D, et al., editors. A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients. 2017 International Conference on Rehabilitation Robotics (ICORR): IEEE; 2017.

  93. Liu L, Chen X, Lu Z, Cao S, Wu D, Zhang X. Development of an EMG-ACC-based upper limb rehabilitation training system. IEEE Trans Neural Syst Rehab Eng. 2016;25(3):244–53.

    Google Scholar 

  94. Calabrò RS, Russo M, Naro A, De Luca R, Leo A, Tomasello P, et al. Robotic gait training in multiple sclerosis rehabilitation: can virtual reality make the difference? Findings from a randomized controlled trial. J Neurol Sci. 2017;377:25–30.

    Google Scholar 

  95. Archambault PS, Norouzi-Gheidari N, Kairy D, Levin MF, Milot M-H, Monte-Silva K, et al., editors. Upper extremity intervention for stroke combining virtual reality, robotics and electrical stimulation. 2019 International Conference on Virtual Rehabilitation (ICVR): IEEE; 2019.

  96. Berezny N, Dowlatshahi D, Ahmadi M, editors. Interaction control and haptic feedback for a lower-limb rehabilitation robot with virtual environments. Proceedings of the 6th International Conference of Control, Dynamic Systems, and Robotics; 2019.

    Google Scholar 

  97. Ocampo R, Tavakoli M, editors. Visual-haptic colocation in robotic rehabilitation exercises using a 2d augmented-reality display. 2019 International Symposium on Medical Robotics (ISMR): IEEE; 2019.

  98. Gui K, Liu H, Zhang D. Toward multimodal human–robot interaction to enhance active participation of users in gait rehabilitation. IEEE Trans Neural Syst Rehab Eng. 2017;25(11):2054–66.

    Google Scholar 

  99. Jamwal PK, Hussain S, Ghayesh MH, Rogozina SV. Impedance control of an intrinsically compliant parallel ankle rehabilitation robot. IEEE Trans Ind Electron. 2016;63(6):3638–47.

    Google Scholar 

  100. Song A, Pan L, Xu G, Li H. Adaptive motion control of arm rehabilitation robot based on impedance identification. Robotica. 2015;33(9):1795–812.

    Google Scholar 

  101. Cabitza F, Locoro A, Banfi G. Machine learning in orthopedics: a literature review. Front Bioeng Biotechnol. 2018;6:75.

    Google Scholar 

  102. Fong J, Ocampo R, Gross DP, Tavakoli M. Intelligent robotics incorporating machine learning algorithms for improving functional capacity evaluation and occupational rehabilitation. J Occup Rehabil. 2020; This study shows how a machine learning algorithm in conjungtion with a robotic terpist can work to evaluate and improve the therapeutic ionntervention.

  103. Dolatabadi E, Taati B, Mihailidis A. An automated classification of pathological gait using unobtrusive sensing technology. IEEE Trans Neural Syst Rehab Eng. 2017;25(12):2336–46.

    Google Scholar 

  104. Cui C, Bian G-B, Hou Z-G, Zhao J, Su G, Zhou H, et al. Simultaneous recognition and assessment of post-stroke hemiparetic gait by fusing kinematic, kinetic, and electrophysiological data. IEEE Trans Neural Syst Rehab Eng. 2018;26(4):856–64.

    Google Scholar 

  105. Badesa FJ, Morales R, Garcia-Aracil N, Sabater JM, Casals A, Zollo L. Auto-adaptive robot-aided therapy using machine learning techniques. Comput Methods Prog Biomed. 2014;116(2):123–30.

    Google Scholar 

  106. Barzilay O, Wolf A. Adaptive rehabilitation games. J Electromyogr Kinesiol. 2013;23(1):182–9.

    Google Scholar 

  107. Cai S, Li G, Su E, Wei X, Huang S, Ma K, et al. Real-time detection of compensatory patterns in patients with stroke to reduce compensation during robotic rehabilitation therapy. IEEE J Biomed Health Inform. 2020.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abolfazl Mohebbi.

Ethics declarations

Conflict of Interest

The author declares that he has no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article belongs to the Topical Collection on Rehabilitation and Assistive Robotics

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohebbi, A. Human-Robot Interaction in Rehabilitation and Assistance: a Review. Curr Robot Rep 1, 131–144 (2020). https://doi.org/10.1007/s43154-020-00015-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s43154-020-00015-4

Keywords

Navigation