Skip to main content

Human–Robot Interaction for Rehabilitation Robotics

  • Chapter
  • First Online:
Digitalization in Healthcare

Part of the book series: Future of Business and Finance ((FBF))

Abstract

Research on rehabilitation robotics has gained significant attention over the last decades due to unmet medical needs for people with neurological and musculoskeletal disorders. In this chapter, we focus on the current states and emerging trends of human–robot interaction (HRI) for rehabilitation robotics by giving an overview of two representative examples: upper-limb prosthesis and therapeutic robot for stroke rehabilitation. For prostheses, we discuss the development of HRI in forward prothesis control and sensory feedback. In terms of HRI in therapeutic robots for stroke rehabilitation, human intention detection, therapy feedback, and rehabilitation performance assessment are introduced. Finally, we conclude each example with remaining challenges and future directions.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 99.99
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

  • Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y., & Zhao, X. (2019). A comprehensive review of EEG-based brain–computer interface paradigms. Journal of Neural Engineering, 16(1), 011001.

    Article  Google Scholar 

  • Aboseria, M., Clemente, F., Engels, L. F., & Cipriani, C. (2018). Discrete vibro-tactile feedback prevents object slippage in hand prostheses more intuitively than other modalities. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(8), 1577–1584.

    Article  Google Scholar 

  • Acosta, A. M., Dewald, H. A., & Dewald, J. P. (2011). Pilot study to test effectiveness of video game on reaching performance in stroke. Journal of Rehabilitation Research and Development, 48(4), 431.

    Article  Google Scholar 

  • Alankus, G., & Kelleher, C. (2012). Reducing compensatory motions in video games for stroke rehabilitation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2049–2058).

    Google Scholar 

  • Antfolk, C., Cipriani, C., Carrozza, M. C., Balkenius, C., Björkman, A., Lundborg, G., et al. (2013a). Transfer of tactile input from an artificial hand to the forearm: Experiments in amputees and able-bodied volunteers. Disability and Rehabilitation: Assistive Technology, 8(3), 249–254.

    Google Scholar 

  • Antfolk, C., D’alonzo, M., Rosén, B., Lundborg, G., Sebelius, F., & Cipriani, C. (2013b). Sensory feedback in upper limb prosthetics. Expert Review of Medical Devices, 10(1), 45–54.

    Article  Google Scholar 

  • Archambault, P. S., Norouzi-Gheidari, N., Kairy, D., Levin, M. F., Milot, M.-H., Monte-Silva, K., et al. (2019). Upper extremity intervention for stroke combining virtual reality, robotics and electrical stimulation. In 2019 International Conference on Virtual Rehabilitation (ICVR) (pp. 1–7). Piscataway: IEEE.

    Google Scholar 

  • Atallah, L., Lo, B., King, R., & Yang, G.-Z. (2011). Sensor positioning for activity recognition using wearable accelerometers. IEEE Transactions on Biomedical Circuits and Systems, 5(4), 320–329.

    Article  Google Scholar 

  • Beckerle, P., Salvietti, G., Unal, R., Prattichizzo, D., Rossi, S., Castellini, C., et al. (2017). A human–robot interaction perspective on assistive and rehabilitation robotics. Frontiers in Neurorobotics, 11, 24.

    Article  Google Scholar 

  • Berger, A., Horst, F., Müller, S., Steinberg, F., & Doppelmayr, M. (2019). Current state and future prospects of EEG and fNIRS in robot-assisted gait rehabilitation: a brief review. Frontiers in Human Neuroscience, 13, 172.

    Article  Google Scholar 

  • Bergmeister, K. D., Vujaklija, I., Muceli, S., Sturma, A., Hruby, L. A., Prahm, C., et al. (2017). Broadband prosthetic interfaces: Combining nerve transfers and implantable multichannel EMG technology to decode spinal motor neuron activity. Frontiers in Neuroscience, 11, 421.

    Article  Google Scholar 

  • Bernhardt, M., Frey, M., Colombo, G., & Riener, R. (2005). Hybrid force-position control yields cooperative behaviour of the rehabilitation robot Lokomat. In 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005 (pp. 536–539). Piscataway: IEEE.

    Google Scholar 

  • Bhattacharyya, S., Konar, A., & Tibarewala, D. (2014). Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose. Medical & Biological Engineering & Computing, 52(12), 1007–1017.

    Article  Google Scholar 

  • Broeren, J., Rydmark, M., & Sunnerhagen, K. S. (2004). Virtual reality and haptics as a training device for movement rehabilitation after stroke: a single-case study. Archives of Physical Medicine and Rehabilitation, 85(8), 1247–1250.

    Article  Google Scholar 

  • Cai, S., Chen, Y., Huang, S., Wu, Y., Zheng, H., Li, X., et al. (2019). SVM-based classification of sEMG signals for upper-limb self-rehabilitation training. Frontiers in Neurorobotics, 13, 31.

    Article  Google Scholar 

  • Chang, W. H., & Kim, Y.-H. (2013). Robot-assisted therapy in stroke rehabilitation. Journal of Stroke, 15(3), 174.

    Article  Google Scholar 

  • Chen, C., Wang, Z., Li, W., Chen, H., Mei, Z., Yuan, W., et al. (2018). Novel flexible material-based unobtrusive and wearable body sensor networks for vital sign monitoring. IEEE Sensors Journal, 19(19), 8502–8513.

    Article  Google Scholar 

  • Cheng, N., Phua, K. S., Lai, H. S., Tam, P. K., Tang, K. Y., Cheng, K. K., et al. (2020). Brain-computer interface-based soft robotic glove rehabilitation for stroke. IEEE Transactions on Biomedical Engineering, 67, 3339–3351.

    Article  Google Scholar 

  • Cianchetti, M., Laschi, C., Menciassi, A., & Dario, P. (2018). Biomedical applications of soft robotics. Nature Reviews Materials, 3(6), 143–153.

    Article  Google Scholar 

  • Cognolato, M., Gijsberts, A., Gregori, V., Saetta, G., Giacomino, K., Hager, A.-G. M., et al. (2020). Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics. Scientific Data, 7(1), 1–15.

    Google Scholar 

  • Collins, K. L., Guterstam, A., Cronin, J., Olson, J. D., Ehrsson, H. H., & Ojemann, J. G. (2017). Ownership of an artificial limb induced by electrical brain stimulation. Proceedings of the National Academy of Sciences, 114(1), 166–171.

    Article  Google Scholar 

  • Côté-Allard, U., Campbell, E., Phinyomark, A., Laviolette, F., Gosselin, B., & Scheme, E. (2020). Interpreting deep learning features for myoelectric control: A comparison with handcrafted features. Frontiers in Bioengineering and Biotechnology, 8, 158.

    Article  Google Scholar 

  • de Oliveira, A. C., Warburton, K., Sulzer, J. S., & Deshpande, A. D. (2019). Effort estimation in robot-aided training with a neural network. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 563–569). Piscataway: IEEE.

    Chapter  Google Scholar 

  • Dellon, B., & Matsuoka, Y. (2007). Prosthetics, exoskeletons, and rehabilitation [grand challenges of robotics]. IEEE Robotics & Automation Magazine, 14(1), 30–34.

    Article  Google Scholar 

  • Dipietro, L., Ferraro, M., Palazzolo, J. J., Krebs, H. I., Volpe, B. T., & Hogan, N. (2005). Customized interactive robotic treatment for stroke: EMG-triggered therapy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(3), 325–334.

    Article  Google Scholar 

  • Doğan-Aslan, M., Nakipoğlu-Yüzer, G. F., Doğan, A., Karabay, İ., & Özgirgin, N. (2012). The effect of electromyographic biofeedback treatment in improving upper extremity functioning of patients with hemiplegic stroke. Journal of Stroke and Cerebrovascular Diseases, 21(3), 187–192.

    Article  Google Scholar 

  • Dovat, L., Lambercy, O., Gassert, R., Maeder, T., Milner, T., Leong, T. C., et al. (2008). Handcare: A cable-actuated rehabilitation system to train hand function after stroke. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16(6), 582–591.

    Article  Google Scholar 

  • Edelman, B. J., Baxter, B., & He, B. (2015). EEG source imaging enhances the decoding of complex right-hand motor imagery tasks. IEEE Transactions on Biomedical Engineering, 63(1), 4–14.

    Article  Google Scholar 

  • Fang, Y., Hettiarachchi, N., Zhou, D., & Liu, H. (2015). Multi-modal sensing techniques for interfacing hand prostheses: A review. IEEE Sensors Journal, 15(11), 6065–6076.

    Article  Google Scholar 

  • Farina, D., & Amsüss, S. (2016). Reflections on the present and future of upper limb prostheses. Expert Review of Medical Devices, 13(4), 321–324.

    Article  Google Scholar 

  • Farris, R. J., Quintero, H. A., & Goldfarb, M. (2011). Preliminary evaluation of a powered lower limb orthosis to aid walking in paraplegic individuals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(6), 652–659.

    Article  Google Scholar 

  • Fern’ndez-Baena, A., Susín, A., & Lligadas, X. (2012). Biomechanical validation of upper-body and lower-body joint movements of Kinect motion capture data for rehabilitation treatments. In 2012 Fourth International Conference on Intelligent Networking and Collaborative Systems (pp. 656–661). Piscataway: IEEE.

    Chapter  Google Scholar 

  • Foong, R., Ang, K. K., Quek, C., Guan, C., Phua, K. S., Kuah, C. W. K., et al. (2019). Assessment of the efficacy of EEG-based MI-BCI with visual feedback and EEG correlates of mental fatigue for upper-limb stroke rehabilitation. IEEE Transactions on Biomedical Engineering, 67(3), 786–795.

    Article  Google Scholar 

  • Frolov, A. A., Mokienko, O., Lyukmanov, R., Biryukova, E., Kotov, S., Turbina, L., et al. (2017). Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: A randomized controlled multicenter trial. Frontiers in Neuroscience, 11, 400.

    Article  Google Scholar 

  • Gandolfi, M., Formaggio, E., Geroin, C., Storti, S. F., Boscolo Galazzo, I., Bortolami, M., et al. (2018). Quantification of upper limb motor recovery and EEG power changes after robot-assisted bilateral arm training in chronic stroke patients: A prospective pilot study. Neural Plasticity, 2018, 8105480.

    Article  Google Scholar 

  • Geethanjali, P. (2016). Myoelectric control of prosthetic hands: State-of-the-art review. Medical Devices (Auckland, NZ), 9, 247.

    Google Scholar 

  • Giggins, O. M., Persson, U. M., & Caulfield, B. (2013). Biofeedback in rehabilitation. Journal of Neuroengineering and Rehabilitation, 10(1), 60.

    Article  Google Scholar 

  • Goldfarb, M., Lawson, B. E., & Shultz, A. H. (2013). Realizing the promise of robotic leg prostheses. Science Translational Medicine, 5(210), 210ps15–210ps15.

    Google Scholar 

  • Guo, W., Sheng, X., Liu, H., & Zhu, X. (2017). Toward an enhanced human–machine interface for upper-limb prosthesis control with combined EMG and NIRS signals. IEEE Transactions on Human-Machine Systems, 47(4), 564–575.

    Article  Google Scholar 

  • Hamzeheinejad, N., Straka, S., Gall, D., Weilbach, F., & Latoschik, M. E. (2018). Immersive robot-assisted virtual reality therapy for neurologically-caused gait impairments. In 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 565–566). Piscataway: IEEE.

    Chapter  Google Scholar 

  • Hayhurst, J. (2018). How augmented reality and virtual reality is being used to support people living with dementia—design challenges and future directions. In Augmented Reality and Virtual Reality (pp. 295–305). Berlin: Springer.

    Chapter  Google Scholar 

  • Ho, N., Tong, K., Hu, X., Fung, K., Wei, X., Rong, W., et al. (2011). An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: Task training system for stroke rehabilitation. In 2011 IEEE International Conference on Rehabilitation Robotics (pp. 1–5). Piscataway: IEEE.

    Google Scholar 

  • Holden, M. K. (2005). Virtual environments for motor rehabilitation. Cyberpsychology & Behavior, 8(3), 187–211.

    Article  Google Scholar 

  • Horki, P., Solis-Escalante, T., Neuper, C., & Müller-Putz, G. (2011). Combined motor imagery and SSVEP based BCI control of a 2 DOF artificial upper limb. Medical & Biological Engineering & Computing, 49(5), 567–577.

    Article  Google Scholar 

  • Hu, X., Tong, K., Song, R., Zheng, X., Lui, K., Leung, W., et al. (2009). Quantitative evaluation of motor functional recovery process in chronic stroke patients during robot-assisted wrist training. Journal of Electromyography and Kinesiology, 19(4), 639–650.

    Article  Google Scholar 

  • Kapelner, T., Vujaklija, I., Jiang, N., Negro, F., Aszmann, O. C., Principe, J., et al. (2019). Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses. Journal of Neuroengineering and Rehabilitation, 16(1), 47.

    Article  Google Scholar 

  • Keller, U., Schölch, S., Albisser, U., Rudhe, C., Curt, A., Riener, R., et al. (2015). Robot-assisted arm assessments in spinal cord injured patients: A consideration of concept study. PloS One, 10(5), e0126948.

    Article  Google Scholar 

  • Khan, S. M., Khan, A. A., & Farooq, O. (2019). Selection of features and classifiers for EMG-EEG-based upper limb assistive devices—a review. IEEE Reviews in Biomedical Engineering, 13, 248–260.

    Article  Google Scholar 

  • Kim, D., Kang, B. B., Kim, K. B., Choi, H., Ha, J., Cho, K.-J., et al. (2019). Eyes are faster than hands: A soft wearable robot learns user intention from the egocentric view. Sci Robot, 4(26), eaav2949.

    Google Scholar 

  • Kim, J.-H., Bießmann, F., & Lee, S.-W. (2014). Decoding three-dimensional trajectory of executed and imagined arm movements from electroencephalogram signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(5), 867–876.

    Article  Google Scholar 

  • Kumar, N., & Michmizos, K. P. (2020). Deep learning of movement intent and reaction time for EEG-informed adaptation of rehabilitation robots. Preprint arXiv:2002.08354.

    Google Scholar 

  • Kung, P.-C., Lin, C.-C. K., & Ju, M.-S. (2010). Neuro-rehabilitation robot-assisted assessments of synergy patterns of forearm, elbow and shoulder joints in chronic stroke patients. Clinical Biomechanics, 25(7), 647–654.

    Article  Google Scholar 

  • Li, C., Xu, J., Zhu, Y., Kuang, S., Qu, W., & Sun, L. (2020). Detecting self-paced walking intention based on fNIRS technology for the development of BCI. Medical & Biological Engineering & Computing, 58, 1–9.

    Article  Google Scholar 

  • Li, S., Zhang, X., & Webb, J. D. (2017). 3-D-Gaze-based robotic grasping through mimicking human visuomotor function for people with motion impairments. IEEE Transactions on Biomedical Engineering, 64(12), 2824–2835.

    Article  Google Scholar 

  • Li, Y., Zhang, Q., Zeng, N., Chen, J., & Zhang, Q. (2019). Discrete hand motion intention decoding based on transient myoelectric signals. IEEE Access, 7, 81630–81639.

    Article  Google Scholar 

  • Lo, A. C., Guarino, P. D., Richards, L. G., Haselkorn, J. K., Wittenberg, G. F., Federman, D. G., et al. (2010). Robot-assisted therapy for long-term upper-limb impairment after stroke. New England Journal of Medicine, 362(19), 1772–1783.

    Article  Google Scholar 

  • Lundborg, G., Rosén, B., & Lindberg, S. (1999). Hearing as substitution for sensation: A new principle for artificial sensibility. The Journal of Hand Surgery, 24(2), 219–224.

    Article  Google Scholar 

  • Lunenburger, L., Colombo, G., Riener, R., & Dietz, V. (2004). Biofeedback in gait training with the robotic orthosis Lokomat. In The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (vol. 2, pp. 4888–4891). Piscataway: IEEE.

    Google Scholar 

  • Marasco, P. D., Kim, K., Colgate, J. E., Peshkin, M. A., & Kuiken, T. A. (2011). Robotic touch shifts perception of embodiment to a prosthesis in targeted reinnervation amputees. Brain, 134(3), 747–758.

    Article  Google Scholar 

  • Markovic, M., Schweisfurth, M. A., Engels, L. F., Farina, D., & Dosen, S. (2018). Myocontrol is closed-loop control: Incidental feedback is sufficient for scaling the prosthesis force in routine grasping. Journal of Neuroengineering and Rehabilitation, 15(1), 1–11.

    Article  Google Scholar 

  • McMullen, D. P., Hotson, G., Katyal, K. D., Wester, B. A., Fifer, M. S., McGee, T. G., et al. (2013). Demonstration of a semi-autonomous hybrid brain–machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(4), 784–796.

    Article  Google Scholar 

  • Mubin, O., Alnajjar, F., Jishtu, N., Alsinglawi, B., & Al Mahmud, A. (2019). Exoskeletons with virtual reality, augmented reality, and gamification for stroke patients’ rehabilitation: Systematic review. JMIR Rehabilitation and Assistive Technologies, 6(2), e12010.

    Article  Google Scholar 

  • Muller-Putz, G. R., & Pfurtscheller, G. (2007). Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Transactions on Biomedical Engineering, 55(1), 361–364.

    Article  Google Scholar 

  • Naseer, N., & Hong, K.-S. (2015). fNIRS-based brain-computer interfaces: A review. Frontiers in Human Neuroscience, 9, 3.

    Google Scholar 

  • Niu, C. M., Luo, Q., Chou, C.-h., Liu, J., Hao, M., & Lan, N. (2021). Neuromorphic model of reflex for realtime human-like compliant control of prosthetic hand. Annals of Biomedical Engineering 49, 673–688.

    Google Scholar 

  • Nordin, N., Xie, S. Q., & Wünsche, B. (2014). Assessment of movement quality in robot-assisted upper limb rehabilitation after stroke: A review. Journal of Neuroengineering and Rehabilitation, 11(1), 137.

    Article  Google Scholar 

  • Novak, D., & Riener, R. (2013). Enhancing patient freedom in rehabilitation robotics using gaze-based intention detection. In 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR) (pp. 1–6). Piscataway: IEEE.

    Google Scholar 

  • Oddo, C. M., Raspopovic, S., Artoni, F., Mazzoni, A., Spigler, G., Petrini, F., et al. (2016). Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans. Elife, 5, e09148.

    Article  Google Scholar 

  • Osborn, L. E., Dragomir, A., Betthauser, J. L., Hunt, C. L., Nguyen, H. H., Kaliki, R. R., et al. (2018). Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain. Science Robotics, 3(19).

    Google Scholar 

  • Ottobock (2020). C-leg 4. https://www.thelondonprosthetics.com/prosthetic-solutions/lower-limb/microprocessor-knees/c-leg-4. Online Accessed October 18, 2020.

  • Park, W., Kwon, G. H., Kim, D.-H., Kim, Y.-H., Kim, S.-P., & Kim, L. (2014). Assessment of cognitive engagement in stroke patients from single-trial EEG during motor rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(3), 351–362.

    Article  Google Scholar 

  • Parr, J. V. V., Vine, S. J., Wilson, M. R., Harrison, N. R., & Wood, G. (2019). Visual attention, EEG alpha power and T7-Fz connectivity are implicated in prosthetic hand control and can be optimized through gaze training. Journal of Neuroengineering and Rehabilitation, 16(1), 1–20.

    Article  Google Scholar 

  • Polygerinos, P., Wang, Z., Galloway, K. C., Wood, R. J., & Walsh, C. J. (2015). Soft robotic glove for combined assistance and at-home rehabilitation. Robotics and Autonomous Systems, 73, 135–143.

    Article  Google Scholar 

  • Rea, M., Rana, M., Lugato, N., Terekhin, P., Gizzi, L., Brötz, D., et al. (2014). Lower limb movement preparation in chronic stroke: A pilot study toward an fNIRS-BCI for gait rehabilitation. Neurorehabilitation and Neural Repair, 28(6), 564–575.

    Article  Google Scholar 

  • Ren, J.-L., Chien, Y.-H., Chia, E.-Y., Fu, L.-C., & Lai, J.-S. (2019). Deep learning based motion prediction for exoskeleton robot control in upper limb rehabilitation. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 5076–5082). Piscataway: IEEE.

    Chapter  Google Scholar 

  • Ribeiro, J., Mota, F., Cavalcante, T., Nogueira, I., Gondim, V., Albuquerque, V., et al. (2019). Analysis of man-machine interfaces in upper-limb prosthesis: A review. Robotics, 8(1), 16.

    Article  Google Scholar 

  • Ruhunage, I., Perera, C. J., Nisal, K., Subodha, J., & Lalitharatne, T. D. (2017). EMG signal controlled transhumerai prosthetic with EEG-SSVEP based approach for hand open/close. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 3169–3174). Piscataway: IEEE.

    Chapter  Google Scholar 

  • Sakurada, T., Kawase, T., Takano, K., Komatsu, T., & Kansaku, K. (2013). A BMI-based occupational therapy assist suit: Asynchronous control by SSVEP. Frontiers in Neuroscience, 7, 172.

    Article  Google Scholar 

  • Samuel, O. W., Asogbon, M. G., Geng, Y., Al-Timemy, A. H., Pirbhulal, S., Ji, N., et al. (2019). Intelligent EMG pattern recognition control method for upper-limb multifunctional prostheses: Advances, current challenges, and future prospects. IEEE Access, 7, 10150–10165.

    Article  Google Scholar 

  • Schiefer, M., Tan, D., Sidek, S. M., & Tyler, D. J. (2015). Sensory feedback by peripheral nerve stimulation improves task performance in individuals with upper limb loss using a myoelectric prosthesis. Journal of Neural Engineering, 13(1), 016001.

    Article  Google Scholar 

  • Sikdar, S., Rangwala, H., Eastlake, E. B., Hunt, I. A., Nelson, A. J., Devanathan, J., et al. (2013). Novel method for predicting dexterous individual finger movements by imaging muscle activity using a wearable ultrasonic system. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(1), 69–76.

    Article  Google Scholar 

  • Smith, L. H., Kuiken, T. A., & Hargrove, L. J. (2014). Real-time simultaneous and proportional myoelectric control using intramuscular EMG. Journal of Neural Engineering, 11(6), 066013.

    Article  Google Scholar 

  • Stachaczyk, M., Atashzar, S. F., Farina, D. (2020). Adaptive spatial filtering of high-density EMG for reducing the influence of noise and artefacts in myoelectric control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28, 1511–1517.

    Article  Google Scholar 

  • Stanton, R., Ada, L., Dean, C. M., & Preston, E. (2017). Biofeedback improves performance in lower limb activities more than usual therapy in people following stroke: A systematic review. Journal of Physiotherapy, 63(1), 11–16.

    Article  Google Scholar 

  • Stein, J., Narendran, K., McBean, J., Krebs, K., & Hughes, R. (2007). Electromyography-controlled exoskeletal upper-limb-powered orthosis for exercise training after stroke. American Journal of Physical Medicine & Rehabilitation, 86(4), 255–261.

    Article  Google Scholar 

  • Stoller, O., Waser, M., Stammler, L., & Schuster, C. (2012). Evaluation of robot-assisted gait training using integrated biofeedback in neurologic disorders. Gait & Posture, 35(4), 595–600.

    Article  Google Scholar 

  • Taati, B., Wang, R., Huq, R., Snoek, J., & Mihailidis, A. (2012). Vision-based posture assessment to detect and categorize compensation during robotic rehabilitation therapy. In 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) (pp. 1607–1613). Piscataway: IEEE.

    Chapter  Google Scholar 

  • Tam, W.-k., Wu, T., Zhao, Q., Keefer, E., & Yang, Z. (2019). Human motor decoding from neural signals: A review. BMC Biomedical Engineering, 1(1), 22.

    Google Scholar 

  • Tamburella, F., Moreno, J. C., Valenzuela, D. S. H., Pisotta, I., Iosa, M., Cincotti, F., et al. (2019). Influences of the biofeedback content on robotic post-stroke gait rehabilitation: Electromyographic vs joint torque biofeedback. Journal of Neuroengineering and Rehabilitation, 16(1), 95.

    Article  Google Scholar 

  • Trujillo, P., Mastropietro, A., Scano, A., Chiavenna, A., Mrakic-Sposta, S., Caimmi, M., et al. (2017). Quantitative EEG for predicting upper limb motor recovery in chronic stroke robot-assisted rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(7), 1058–1067.

    Article  Google Scholar 

  • United Nations, D. o. E. . S. A. (2019). World population ageing, 2019. Herndon: United Nations

    Google Scholar 

  • Varghese, R. J., Lo, B. P. L., & Yang, G.-Z. (2020). Design and prototyping of a bio-inspired kinematic sensing suit for the shoulder joint: Precursor to a multi-DoF shoulder exosuit. IEEE Robotics and Automation Letters, 5(2), 540–547.

    Article  Google Scholar 

  • Veerbeek, J. M., Langbroek-Amersfoort, A. C., Van Wegen, E. E., Meskers, C. G., & Kwakkel, G. (2017). Effects of robot-assisted therapy for the upper limb after stroke: A systematic review and meta-analysis. Neurorehabilitation and Neural Repair, 31(2), 107–121.

    Article  Google Scholar 

  • Vovk, A., Patel, A., & Chan, D. (2019). Augmented reality for early Alzheimer’s disease diagnosis. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–60).

    Google Scholar 

  • Wagner, J., Solis-Escalante, T., Scherer, R., Neuper, C., & Müller-Putz, G. (2014). It’s how you get there: Walking down a virtual alley activates premotor and parietal areas. Frontiers in Human Neuroscience, 8, 93.

    Article  Google Scholar 

  • Walsh, C. (2018). Human-in-the-loop development of soft wearable robots. Nature Reviews Materials, 3(6), 78–80.

    Article  Google Scholar 

  • Wang, J., Fei, Y., & Chen, W. (2020). Integration, sensing, and control of a modular soft-rigid pneumatic lower limb exoskeleton. Soft Robotics, 7(2), 140–154.

    Article  Google Scholar 

  • Wang, Y., & Chen, W. (2011). Hybrid map-based navigation for intelligent wheelchair. In 2011 IEEE International Conference on Robotics and Automation (pp. 637–642). Piscataway: IEEE.

    Chapter  Google Scholar 

  • Wilke, M. A., Niethammer, C., Meyer, B., Farina, D., & Dosen, S. (2019). Psychometric characterization of incidental feedback sources during grasping with a hand prosthesis. Journal of NeuroEngineering and Rehabilitation, 16(1), 1–13.

    Article  Google Scholar 

  • Xu, H., Zhang, D., Huegel, J. C., Xu, W., & Zhu, X. (2015). Effects of different tactile feedback on myoelectric closed-loop control for grasping based on electrotactile stimulation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(8), 827–836.

    Article  Google Scholar 

  • Yanagisawa, T., Hirata, M., Saitoh, Y., Kishima, H., Matsushita, K., Goto, T., et al. (2012). Electrocorticographic control of a prosthetic arm in paralyzed patients. Annals of Neurology, 71(3), 353–361.

    Article  Google Scholar 

  • Ye, W., Li, Z., Yang, C., Chen, F., & Su, C.-Y. (2017). Motion detection enhanced control of an upper limb exoskeleton robot for rehabilitation training. International Journal of Humanoid Robotics, 14(01), 1650031.

    Article  Google Scholar 

  • Young, A. J., & Ferris, D. P. (2016). State of the art and future directions for lower limb robotic exoskeletons. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(2), 171–182.

    Article  Google Scholar 

  • Zhai, X., Jelfs, B., Chan, R. H., & Tin, C. (2017). Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on convolutional neural network. Frontiers in Neuroscience, 11, 379.

    Article  Google Scholar 

  • Zhang, J., Wang, B., Zhang, C., Xiao, Y., & Wang, M. Y. (2019). An EEG/EMG/EOG-based multimodal human-machine interface to real-time control of a soft robot hand. Frontiers in Neurorobotics, 13, 7.

    Article  Google Scholar 

  • Zhong, B., Huang, H., & Lobaton, E. (2020). Reliable vision-based grasping target recognition for upper limb prostheses. IEEE Transactions on Cybernetics, 1–13.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guang-Zhong Yang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 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

Guo, Y., Gu, X., Yang, GZ. (2021). Human–Robot Interaction for Rehabilitation Robotics. In: Glauner, P., Plugmann, P., Lerzynski, G. (eds) Digitalization in Healthcare. Future of Business and Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-65896-0_23

Download citation

Publish with us

Policies and ethics