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.
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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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Chang, W. H., & Kim, Y.-H. (2013). Robot-assisted therapy in stroke rehabilitation. Journal of Stroke, 15(3), 174.
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.
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.
Cianchetti, M., Laschi, C., Menciassi, A., & Dario, P. (2018). Biomedical applications of soft robotics. Nature Reviews Materials, 3(6), 143–153.
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.
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.
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.
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.
Dellon, B., & Matsuoka, Y. (2007). Prosthetics, exoskeletons, and rehabilitation [grand challenges of robotics]. IEEE Robotics & Automation Magazine, 14(1), 30–34.
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.
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.
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.
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.
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.
Farina, D., & Amsüss, S. (2016). Reflections on the present and future of upper limb prostheses. Expert Review of Medical Devices, 13(4), 321–324.
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.
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.
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.
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.
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.
Geethanjali, P. (2016). Myoelectric control of prosthetic hands: State-of-the-art review. Medical Devices (Auckland, NZ), 9, 247.
Giggins, O. M., Persson, U. M., & Caulfield, B. (2013). Biofeedback in rehabilitation. Journal of Neuroengineering and Rehabilitation, 10(1), 60.
Goldfarb, M., Lawson, B. E., & Shultz, A. H. (2013). Realizing the promise of robotic leg prostheses. Science Translational Medicine, 5(210), 210ps15–210ps15.
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.
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.
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.
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.
Holden, M. K. (2005). Virtual environments for motor rehabilitation. Cyberpsychology & Behavior, 8(3), 187–211.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Naseer, N., & Hong, K.-S. (2015). fNIRS-based brain-computer interfaces: A review. Frontiers in Human Neuroscience, 9, 3.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
United Nations, D. o. E. . S. A. (2019). World population ageing, 2019. Herndon: United Nations
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.
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.
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).
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.
Walsh, C. (2018). Human-in-the-loop development of soft wearable robots. Nature Reviews Materials, 3(6), 78–80.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zhong, B., Huang, H., & Lobaton, E. (2020). Reliable vision-based grasping target recognition for upper limb prostheses. IEEE Transactions on Cybernetics, 1–13.
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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
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