Posts Tagged rehabilitation robot

[Abstract + References] Design of Finger Exoskeleton Rehabilitation Robot Using the Flexible Joint and the MYO Armband


High-risk diseases such as stroke can do great harm to human hands. Hand rehabilitation for stroke patients is a complex and necessary task. To achieve this goal, this paper introduces a hand exoskeleton equipment with flexible joints and EMG-base motion prediction. Experiment of the equipment includes kinematics analysis, EMG signal detection by MYO armband and motion prediction base on BP neural network. The result shows that the device can not only assists patient bending or extending fingers, but also perform six kinds of rehabilitation exercises with 92% accuracy for target motion recognition.


  1. 1.
    Ates, S., Haarman, C.J.W., Stienen, A.H.A.: SCRIPT passive orthosis: design of interactive hand and wrist exoskeleton for rehabilitation at home after stroke. Auton. Robots 41(3), 711–723 (2017)CrossRefGoogle Scholar
  2. 2.
    Wolf, S.L., Blanton, S., Baer, H., et al.: Repetitive task practice: a critical review of constraint-induced movement therapy in stroke. Neurologist 8(6), 325–338 (2002)Google Scholar
  3. 3.
    Diez, J.A., Catalan, J.M., Lledo, L.D., et al.: Multimodal robotic system for upper-limb rehabilitation in physical environment. Adv. Mech. Eng. 8(9), 8/9/1687814016670282 (2016)CrossRefGoogle Scholar
  4. 4.
    Sarac, M., Solazzi, M., Sotgiu, E., et al.: Design and kinematic optimization of a novel underactuated robotic hand exoskeleton. Meccanica 52, 749–761 (2017)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Hansen, C., Gosselin, F., Ben Mansour, K., et al.: Design-validation of a hand exoskeleton using musculoskeletal modeling. Appl. Ergon. 68, 283–288 (2018)CrossRefGoogle Scholar
  6. 6.
    Kim, S.J., Kim, Y., Lee, H., Ghasemlou, P., Kim, J.: Development of an MR-compatible hand exoskeleton that is capable of providing interactive robotic rehabilitation during fMRI imaging. Med. Biol. Eng. Comput. 56, 261–272 (2018)CrossRefGoogle Scholar
  7. 7.
    Dicicco, M., Lucas, L., Matsuoka, Y.: Comparison of control strategies for an EMG controlled orthotic exoskeleton for the hand. In: Proceedings of IEEE International Conference on Robotics and Automation, New Orleans, LA, USA, pp. 1622–1627. IEEE (2004)Google Scholar
  8. 8.
    Bouzit, M., Burdea, G., Popescu, G., Boian, R.: The Rutgers Master II new design force feedback glove. IEEE/ASME Trans. Mechatron 7, 256–263 (2002)CrossRefGoogle Scholar
  9. 9.
    Yisheng, T.M.: Robotic Glove for Hand Rehabilitation [OL] (2019). Accessed 27 Mar 2019
  10. 10.
    Adamovich, S., Merians, A., Boian, R., Tremaine, M., et al.: A virtual reality-based exercise system for hand rehabilitation post stroke. Teleoperators Virtual Environ. 14(2), 161–174 (2005)CrossRefGoogle Scholar
  11. 11.
    Wang, J., Li, J., Zhang, Y., Wang, S.: Design of an exoskeleton for index finger rehabilitation. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 5957–5960. IEEE (2009)Google Scholar
  12. 12.
    Conti, R., et al.: Kinematic synthesis and testing of a new portable hand exoskeleton. Meccanica 52, 2873–2897 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Randazzo, L., Iturrate, I., Perdikis, S., et al.: mano: A wearable hand exoskeleton for activities of daily living and neurorehabilitation. IEEE Robot. Autom. Lett. 3(1), 500–507 (2018)CrossRefGoogle Scholar
  14. 14.
    Bataller, A., Cabrera, J.A., Clavijo, M., Castillo, J.J.: Evolutionary synthesis of mechanisms applied to the design of an exoskeleton for finger rehabilitation. Mech. Mach. Theory 105, 31–43 (2016)CrossRefGoogle Scholar
  15. 15.
    Park, Y., Jo, I., Lee, J., et al.: A dual-cable hand exoskeleton system for virtual reality. Mechatronics 49, 177–186 (2018)CrossRefGoogle Scholar
  16. 16.
    Tadano, K., Akai, M., Kadota, K., Kawashima, K.: Development of grip amplified glove using bi-articular mechanism with pneumatic artificial rubber muscle. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2363–2368 (2010)Google Scholar
  17. 17.
    Biggar, S., Yao, W.: Design and evaluation of a soft and wearable robotic glove for hand rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 24(10), 1 (2016)CrossRefGoogle Scholar
  18. 18.
    Hu, X., Tong, K., Wei, X., Rong, W., Susanto, E., Ho, S.: The effects of post-stroke upper-limb training with an electromyography (EMG)-driven hand robot. J. Electromyogr. Kinesiol. 23(5), 1065–1074 (2013)CrossRefGoogle Scholar
  19. 19.
    Jones, C.L., Wang, F., Morrison, R., et al.: Design and development of the cable actuated finger exoskeleton for hand rehabilitation following stroke. IEEE/ASME Trans. Mechatron. 19(1), 131–140 (2014)CrossRefGoogle Scholar

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[Abstract + References] Preliminary Design of Soft Exo-Suit for Arm Rehabilitation – Conference paper


Every year, millions of people experience a stroke but only a few of them fully recover. Recovery requires a working staff, which is time consuming and inefficient. Therefore, over the past few years rehabilitation robots like Exoskeletons have been used in the recuperation process for patients. In this paper we have designed an Exosuit which takes into considerations of the rigid Exo-Skeleton and its limitations for patients suffering from loss of function of the arm. This paper concentrates on enabling a stroke affected person to perform flexion-extension at elbow joint. Validation of the developed model on general population is still needed.


  1. 1.
    Mathers, C., Fat, D.M., Boerma, J.T., World Health Organization: The global burden of disease: 2004 update. World Health Organization (2008)Google Scholar
  2. 2.
    McPhee, S.J., Hammer, G.D.: Nervous system disorders. Pathophysiol. Dis. Introd. Clin. Med. 59, 177–180 (2010)Google Scholar
  3. 3.
    Committee on Nervous System Disorders in Developing Countries the Board on Global Health and the Institute of Medicine. Neurological, Psychiatric, and Develop-Mental Disorders. National Academies Press, Washington, DC (2001)Google Scholar
  4. 4.
    Zhang, Y., Arakalian, V.: Design of a passive robotic ExoSuit for carrying heavy loads. In: Proceedings of the IEEE-RAS, 18th Annual International Conference on Humanoid Robots, Lyon, France (2018)Google Scholar
  5. 5.
    Gross, R., et al.: Modulation of lower limb muscle activity induced by curved walking in typically developing children. Gait Posture 50, 34–41 (2016)CrossRefGoogle Scholar
  6. 6.
    Viteckova, S., Kutilek, P., Jirina, M.: Wearable lower limb robotics: a review. Biocybern. Biomed. Eng. 33(2), 96–105 (2013)CrossRefGoogle Scholar
  7. 7.
    Rupala, B.S., Singla, A., Virk, G.S.: Lower limb exoskeletons: a brief review. In: Proceedings of the Conference on Mechanical Engineering and Technology COMET, Varanasi, Utter Pradesh, pp. 18–24 (2016)Google Scholar
  8. 8.
    Collo, A., Bonnet, V., Venture, G.: A quasi-passive lower limb exoskeleton for partial body weight support. In: Proceedings of the 6th IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), UTown, Singapore, pp. 643–648 (2016)Google Scholar
  9. 9.
    Stewart, A.M., Pretty, C.G., Adams, M., Chen, X.: Review of upper limb hybrid exoskeletons. IFAC 50(1), 15169–15178 (2017)Google Scholar
  10. 10.
    Serea, F., Poboroniuc, M., Hartopanu, S., Olaru, R.: Exoskeleton for upper arm rehabilitation for disabled patients. In: International Conference and Exposition on Electrical and Power Engineering, (EPE 2014), pp. 153–157 (2014)Google Scholar
  11. 11.
    Perry, J.C., Rosen, J., Burns, S.: Upper-limb powered exoskeleton design. IEEE/ASM Trans. Mechatron. 12(4), 408–417 (2007)CrossRefGoogle Scholar
  12. 12.
    Li, B., Yuan, B., Chen, J., Zuo, Y., Yang, Y.: Mechanical design and human-machine coupling dynamic analysis of a lower extremity exoskeleton. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds.) ICIRA 2017. LNCS (LNAI), vol. 10462, pp. 593–604. Springer, Cham (2017). Scholar
  13. 13.
    Jarrasé, N.: Contributions à l’explotation d’exosquelettes actifs pour la rééducation neuromotrice. Ph.D. thesis of Pierre et Marie Curie University (UPMC) (2010)Google Scholar
  14. 14.
    Gunn, M., Shank, T.M., Epps, M., Hossain, J., Rahman, T.: User evaluation of a dynamic arm orthosis for people with neuromuscular disorders. IEEE Trans. Neural Syst. Rehabil. Eng. 24(12), 1277–1283 (2016)CrossRefGoogle Scholar
  15. 15.
    Seth, D., Chablat, D., Bennis, F., Sakka, S., Jubeau, M., Nordez, A.: New dynamic muscle fatigue model to limit musculo-skeletal disorder. In: Virtual Reality International Conference 2016, Article no. 26 (2016)Google Scholar
  16. 16.
    Seth, D., Chablat, D., Sakka, S., Bennis, F.: Experimental validation of a new dynamic muscle fatigue model. In: Duffy, V.G.G. (ed.) DHM 2016. LNCS, vol. 9745, pp. 54–65. Springer, Cham (2016). Scholar
  17. 17.
    Seth, D., Chablat, D., Bennis, F., Sakka, S., Jubeau, M., Nordez, A.: Validation of a new dynamic muscle fatigue model and DMET analysis. Int. J. Virtual Real. 2016(16), 2016 (2016)Google Scholar
  18. 18.
    Talaty, M., Esquenazi, A., Briceno, J.E.: Differentiating ability in users of the ReWalk(TM) powered exoskeleton: an analysis of walking kinematics. In: Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR), Seattle, USA, pp. 1–5 (2013).
  19. 19.
    Aoustin, Y.: Walking gait of a biped with a wearable walking assist device. Int. J. of Humanoid Robotics 12(2), 1 550 018-1–11 550 018-20 (2015). Scholar
  20. 20.
    Ktistakis, I.P., Bourbakis, N.G.: A survey on robotic wheelchairs mounted with robotic arms. In: National Aerospace and Electronics Conference (NAECON), pp. 258–262 (2015)Google Scholar
  21. 21.
    Aoustin, Y., Formalskii, A.: Walking of biped with passive exoskeleton: evaluation of energy consumption. Multibody Syst. Dyn. 43, 71–96 (2017). Scholar
  22. 22.
    Park, W., Jeong, W., Kwon, G., Kim, Y.H., Kim, L.: A rehabilitation device to improve the hand grasp function of stroke patients using a patient-driven approach. In: IEEE International Conference on Rehabilitation Robotics, Seattle Washington, USA (2013)Google Scholar
  23. 23.
    Akhmadeev, K., Rampone, E., Yu, T., Aoustin, Y., Le Carpentier, E.: A testing system for a real-time gesture classification using surface EMG. In: Proceedings of the 20th IFAC World Congress, Toulouse France (2017)Google Scholar
  24. 24.
    Schwartz, C., Lempereur, M., Burdin, V., Jacq, J.J., Rémy-Néris, O.: Shoulder motion analysis using simultaneous skin shape registration. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, France (2007)Google Scholar
  25. 25.
    National Stroke Association Brochure (2017)Google Scholar
  26. 26.
    Nef, T., Guidali, M., Riener, R.: ARMin III – arm therapy exoskeleton with an ergonomic shoulder actuation. Appl. Bionics Biomech. 6(2), 127–142 (2009)CrossRefGoogle Scholar
  27. 27.
    Krebs, H.I., Hogan, N., Volpe, B.T., Aisen, M.L., Edelstein, L., Diels, C.: Overview of clinical trials with MITMANUS: a robot-aided neuro-rehabilitation facility. Technol. Health Care 7(6), 419–423 (1999)Google Scholar
  28. 28.
    Ali, H.: Bionic exoskeleton: history, development and the future. IOSR J. Mechan. Civ. Eng. 58–62 (2014)Google Scholar
  29. 29.
    Banala, S.K., Agrawal, S.K., Scholz, J.P.: Active leg exoskeleton (ALEX) for gait rehabilitation of motor-impaired patients. In: IEEE 2007 Rehabilitation Robotics, pp. 401–407 (2007)Google Scholar
  30. 30.
    Fitle, K.D., Pehlivan, A.U., O’Malley, M.K.: A robotic exoskeleton for re-habilitation and assessment of the upper limb following incomplete spinal cord in-jury. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4960–4966 (2015)Google Scholar
  31. 31.
  32. 32.
  33. 33.
    Plagenhoef, S., et al.: Anatomical data for analyzing human motion (1983)Google Scholar

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[Abstract + References] A compact wrist rehabilitation robot with accurate force/stiffness control and misalignment adaptation


Robots have been demonstrated to assist the rehabilitation of patients with upper or lower limb disabilities. To make exoskeleton robots more friendly and accessible to patients, they need to be lightweight and compact without major performance tradeoffs. Existing upper-limb exoskeleton robots focus on the assistance of the coarse-motion of the upper arm while the fine-motion rehabilitation of the forearm is often ignored. This paper presents a wrist robot with three degrees-of-freedom. Using a geared bearing, slider crank mechanisms, and a spherical mechanism, this robot can provide the complete motion assistance for the forearm. The optimized robot dimensions allow large torque and rotation output while the motors are placed parallel to the forearm. Thus lightweight, compactness, and better inertia properties can be achieved. Linear and rotary series elastic actuators (SEAs) with high torque-to-weight ratios are proposed to accurately measure and control the interaction force and impedance between the robot and the wrist. The resulting 1.5-kg robot can be used alone or easily in combination with other robots to provide various robot-aided upper limb rehabilitation.


  1. Andrews, J.G., Youm, Y.: A biomechanical investigation of wrist kinematics. J. Biomech. 12(1), 83–93 (1979)CrossRefGoogle Scholar
  2. Bian, H., et al.: Mechanical design of EFW Exo II: a hybrid exoskeleton for elbow-forearm-wrist rehabilitation.” In: 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 689–694. IEEE (2017)Google Scholar
  3. Brooks, T.L.: Telerobotic response requirements. In: IEEE International Conference on Systems, man and cybernetics, 1990. Conference Proceedings. IEEE (1990)Google Scholar
  4. Buongiorno, D., et al.: WRES: a novel 3DoF wrist ExoSkeleton with tendon-driven differential transmission for neuro-rehabilitation and teleoperation. IEEE Robot. Autom. Lett. (2018)Google Scholar
  5. Chandler, R. F., et al. Investigation of inertial properties of the human body. No. AMRL-TR-74-137. Air Force Aerospace Medical Research Lab Wright-Patterson AFB OH, 1975Google Scholar
  6. Choi, W., et al.: Low stiffness design and hysteresis compensation torque control of SEA for active exercise rehabilitation robots. Autonom. Robots 41(5), 1221–1242 (2017)CrossRefGoogle Scholar
  7. Dong, W., et al.: Soft human–machine interfaces: design, sensing and stimulation. Int. J. Intell. Robot. Appl. 1–26 (2018)Google Scholar
  8. French, J.A. et al.: System characterization of MAHI Exo-II: a robotic exoskeleton for upper extremity rehabilitation. In: Proceedings of the ASME Dynamic Systems and Controls Conference, October (2014)Google Scholar
  9. Hope, James, McDaid, Andrew: Development of wearable wrist and forearm exoskeleton with shape memory alloy actuators. J. Intell. Rob. Syst. 3(3), 2152–2159 (2017)Google Scholar
  10. Hsieh, H.-C., et al.: Design of a parallel actuated exoskeleton for adaptive and safe robotic shoulder rehabilitation. IEEE/ASME Trans. Mechatron. 22(5), 2034–2045 (2017)CrossRefGoogle Scholar
  11. Hu, X.L., et al.: A comparison between electromyography-driven robot and passive motion device on wrist rehabilitation for chronic stroke. Neurorehabilit. Neural Repair 23(8), 837–846 (2009)CrossRefGoogle Scholar
  12. Khokhar, Zeeshan O., Xiao, Zhen G., Menon, Carlo: Surface EMG pattern recognition for real-time control of a wrist exoskeleton. Biomed. Eng. Online 9(1), 41 (2010)CrossRefGoogle Scholar
  13. Kim, Bongsu, Deshpande, Ashish D.: An upper-body rehabilitation exoskeleton Harmony with an anatomical shoulder mechanism: design, modeling, control, and performance evaluation. Int. J. Robot. Res. 36(4), 414–435 (2017)CrossRefGoogle Scholar
  14. Krebs, H.I., et al.: Robot-aided neurorehabilitation: a robot for wrist rehabilitation. IEEE Trans. Neural Syst. Rehabilit. Eng. 15(3), 327–335 (2007)CrossRefGoogle Scholar
  15. Lambelet, C., et al.: The eWrist—a wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation. In: 2017 International Conference on Rehabilitation Robotics (ICORR), IEEE (2017)Google Scholar
  16. Lee, Y.-F., et al.: A Humanoid robotic wrist with two-dimensional series elastic actuation for accurate force/torque interaction. IEEE/ASME Trans. Mechatron. 21(3), 1315–1325 (2016)CrossRefGoogle Scholar
  17. Lee, J., et al.: A robust control method of multi-DOF power-assistant robots for unknown external perturbation using sEMG signals. In: Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on. IEEE (2015)Google Scholar
  18. Lin, K.-Y., et al.: High-performance series elastic stepper motors for interaction force control. In: 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), IEEE (2017)Google Scholar
  19. Mehling, J.S.: Impedance control approaches for series elastic actuators. Diss. Rice University (2015)Google Scholar
  20. Meng, W., et al.: Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics 31, 132–145 (2015)CrossRefGoogle Scholar
  21. Oh, Sehoon, Kong, Kyoungchul: High-precision robust force control of a series elastic actuator. IEEE/ASME Trans. Mechatron. 22(1), 71–80 (2017)CrossRefGoogle Scholar
  22. Omarkulov, N., et al.: Preliminary mechanical design of NU-Wrist: A 3-DOF self-aligning Wrist rehabilitation robot. In: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE, Singapore (2016).
  23. Patil, G., et al.: Momentum-based trajectory planning for lower-limb exoskeletons supporting sit-to-stand transitions. Int. J. Intell. Robot. Appl. 2(2), 180–192 (2018)CrossRefGoogle Scholar
  24. Pehlivan, A.U., et al.: Design and validation of the RiceWrist-S exoskeleton for robotic rehabilitation after incomplete spinal cord injury. Robotica 32(8), 1415–1431 (2014)CrossRefGoogle Scholar
  25. Perry, J.C., et al.: Upper-limb powered exoskeleton design. IEEE/ASME Trans. Mechatron. 12(4), 408–417 (2007)CrossRefGoogle Scholar
  26. Pezent, E., et al.: Design and characterization of the openwrist: a robotic wrist exoskeleton for coordinated hand-wrist rehabilitation. In: 2017 International Conference on Rehabilitation Robotics (ICORR), IEEE (2017)Google Scholar
  27. Pu, S.-W., Sung-Yu, T., Jen-Yuan, C.: Design and development of the wearable hand exoskeleton system for rehabilitation of hand impaired patients. In: 2014 IEEE International Conference onAutomation Science and Engineering (CASE). IEEE (2014)Google Scholar
  28. Saadatzi, M., David, C. L., Ozkan, C.: Comparison of human-robot interaction torque estimation methods in a wrist rehabilitation exoskeleton. J. Intell. Robot. Syst. 1–17 (2018)Google Scholar
  29. Squeri, V., et al.: Wrist rehabilitation in chronic stroke patients by means of adaptive, progressive robot-aided therapy. IEEE Trans. Neural Syst. Rehabilit. Eng. 22(2), 312–325 (2014)CrossRefGoogle Scholar
  30. Su, Y.-Y, et al.: Design of a lightweight forearm exoskeleton for fine-motion rehabilitation. In: 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE (2018)Google Scholar
  31. Vitiello, N., et al.: NEUROExos: A powered elbow exoskeleton for physical rehabilitation. IEEE Trans. Robot. 29(1), 220–235 (2013)CrossRefGoogle Scholar
  32. Wu, K.-Y. et al.: Series elastic actuation of an elbow rehabilitation exoskeleton with axis misalignment adaptation. In: 2017 International Conference on Rehabilitation Robotics (ICORR), IEEE (2017)Google Scholar
  33. Xu, D., et al.: Development of a Reconfigurable Wrist Rehabilitation Device with an Adaptive Forearm Holder. In: 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE (2018)Google Scholar
  34. Yin, K., et al.: Fuzzy iterative learning control strategy for powered ankle prosthesis. Int. J. Intell. Robot. Appl. 2(1), 122–131 (2018)CrossRefGoogle Scholar
  35. Yu, H., et al.: Human–robot interaction control of rehabilitation robots with series elastic actuators. IEEE Trans. Robot. 25(2), 95–106 (2015)Google Scholar
  36. Zhang, T., He H.H.: Lower-back robotic exoskeleton. IEEE Robot. Autom. Mag. (2018)Google Scholar

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[Abstract] A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation


Previous studies on robotic rehabilitation have shown that subjects’ active participation and effort involved in rehabilitation training can promote the performance of therapies. In order to improve the voluntary effort of participants during the rehabilitation training, assist-as-needed (AAN) control strategies regulating the robotic assistance according to subjects’ performance and conditions have been developed. Unfortunately, the heterogeneity of patients’ motor function capability in task space is not taken into account during the implementation of these controllers. In this paper, a new scheme called greedy AAN (GAAN) controller is designed for the upper limb rehabilitation training of neurologically impaired subjects. The proposed GAAN control paradigm includes a baseline controller and a Gaussian RBF network that is utilized to model the functional capability of subjects and to provide corresponding a task challenge for them. In order to avoid subjects’ slacking and encourage their active engagement, the weight vectors of RBF networks evaluating subjects’ impairment level are updated based on a greedy strategy that makes the networks progressively learn the maximum forces over time provided by subjects. Simultaneously, a challenge level modification algorithm is employed to adjust the task challenge according to the task performance of subjects. Experiments on 12 subjects with neurological impairment are conducted to validate the performance and feasibility of the GAAN controller. The results show that the proposed GAAN controller has significant potential to promote the subjects’ voluntary engagement during training exercises.

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[ARTICLE] Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation – Full Text

In the therapist-centered rehabilitation program, the experienced therapists can observe emotional changes of stroke patients and make corresponding decisions on their intervention strategies. Likewise, robotic-assisted stroke rehabilitation systems will be more appreciated if they can also perceive emotional states of the stroke patients and enhance their engagements by exploring emotion-based dynamic difficulty adjustments. Nevertheless, few research have addressed this issue. A two-phase pilot study with anxiety as the target emotion state was conducted in this article. In phase I, the motor performances and the physiological responses to the stroke subject’s anxiety with high, medium, and low intensities were statistically analyzed, and anxiety models with three intensities were offline developed using support vector machine–based classifiers. In phase II, anxiety-based closed-loop robot-aided training task adaptation and its impacts on patient–robot interaction engagements were explored. As a comparison, a performance-based robotic behavior adaptation was also implemented. Experimental results with 12 recruited stroke patients conducted on the Barrett WAMTM manipulator verified that the rehabilitation robot can implicitly recognize the anxiety intensities of the stroke survivors and the anxiety-based real-time robotic behavior adaptation shows more engagements in the human–robot interactions.

Task-oriented repetitive rehabilitation training is becoming the state-of-the-art therapy approach for poststroke patients. These therapy tasks are traditionally implemented by physical therapists. In recent years, there is an increasing interest in using robotic devices to help providing motor rehabilitation therapy.1Compared with the therapist-centered therapy, robot-assisted stroke rehabilitation can not only provide a variety of highly repetitive movements and training protocols for stroke patients, but also offer objective measurements of stroke patients’ functional improvements.

Stroke patients’ active engagements in rehabilitation training have been shown to be a very positive factor to the success of rehabilitation.2 Early rehabilitation robots are able to provide active assistance to stroke patients, but do not take into account individual properties, spontaneous intentions, or voluntary efforts of that particular person. These problems were addressed by integrating the patients into the sensorimotor control loop. By recognizing the patients’ active motor abilities or movement intentions, the human-in-the-loop rehabilitation robotic systems are able to optimize participation and support the patients only as little as needed.3 However, stroke patients’ active involvements in the existing rehabilitation robotic systems are mostly considered from biomechanical and bioelectrical viewpoints, where the patients’ active force/position signals4,5 or electrical activities of the brain and the muscles6,7 were recorded.

In the therapist-centered program, the therapists who work with the stroke patients can not only perceive the patients’ active motor involvements, but also continuously monitor the patients’ emotion changes in order to make appropriate decisions on their intervention strategies. The stroke patients are particularly vulnerable to anxiety and frustration, which requires to plan tasks at an appropriate level of difficulty. Likewise, robotic-assisted stroke rehabilitation training systems will be more appreciated if they can perceive the stroke patients’ emotion changes and make emotion-based dynamic difficulty adjustment. Offering insights into the patients’ emotional changes and adapting emotion-based behavioral interventions are known as another critical factor to successful stroke rehabilitation.8 Nonetheless, very few research on robot-assisted stroke rehabilitation are specifically addressed how to automatically recognize and respond to the emotion changes of the stroke survivors. One possible reason is that there are some difficulties in perceiving the stroke patient’s emotion states.

There are several modalities such as facial expression,9 vocal intonation,10 body gesture,11 and physiology12 that can be utilized to recognize the emotion states of individuals in human–robot/computer interaction. Nevertheless, the patients with chronic stroke are often characterized by dull facial expression, severe aphasia, and limb motor dysfunction. These vulnerabilities place limits on observational, conversational, and limb methodologies to recognize the stroke survivors’ emotional states. Physiology-based measurements are far more robust against these difficulties because they are noninvasive and further the psycho-physiological signals can be continuously available without the stroke patient’s active cooperation. Besides, evidences show that the transition from one emotion state to another is accompanied by dynamic shifts in indicators of autonomous and central nervous system activity.13,14

In this article, anxiety, which can be easily evoked by training tasks with different difficulties in clinical rehabilitation therapy, was chosen as the target emotion state. The primary focuses of the current research were firstly to offline evaluate anxiety with high, medium, and low intensities and then to carry out real-time anxiety-based robot-aided rehabilitation training task adaptation.

The block diagram of the anxiety detection and subsequent anxiety-based robot-aided training task adaptation system are shown in Figure 1. It consists of two consecutive phases: offline anxiety modeling (phase I) and online anxiety-based robot-aided training task adaptation (phase II). In phase I, the features, from the physiological and the motor performances recordings of the stroke subjects under anxiety with high, medium, and low intensities, were firstly extracted and then subjected to analysis of variance (ANOVA)-based statistical analyses to obtain the features with significant differences among three anxiety intensities. Anxiety with three intensities was further offline evaluated using support vector machine (SVM)–based anxiety classifier, in which the features with significant differences were adopted as inputs while the self-reported questionnaires as outputs. In phase II, robot-aided rehabilitation training tasks were online adapted to the recognized intensities of anxiety from the stroke subjects. Further, to demonstrate the effect of introducing anxiety of stroke patients into robot-assisted stroke rehabilitation, the impacts of anxiety-based robot-aided behavior adaptation on the stroke patient’s engagements were explored using the performance-based robot-aided training task adaptation as a comparison. The details on the enrolled subjects are given in the “Subjects” section while the experimental system setup is depicted in the “Experimental setup” section. “Phase I: Offline anxiety modeling” section demonstrates the offline modeling of the anxiety with high, medium, and low intensities (phase I). The online anxiety-based robot-aided rehabilitation training task adaptation and its impacts on the stroke patient’s engagements are shown in the “Phase II: Online anxiety-based robot-aided rehabilitation training task adaptation” section (phase II).


Figure 1. Overview of the anxiety-based robot-aided training task adaptation system. SVM: support vector machine; EMG: electromyogram; ECG: electrocardiogram; SC: skin conductance; ANOVA: analysis of variance.


The stroke patients, with upper extremity motor impairments and similar Brunnstrom Recovery Scale (BRS) evaluation scores, were recruited as representative of hemiparesis participants. Participants were excluded from the study if they had severe neurological disorder, senile dementia, or cognitive intact.15 Twelve stroke participants (mean age: 53.6 years, 7 males, 5 females, mean stroke time: 12.6 months, 6 right-sided hemiplegia, 6 left-sided hemiplegia, 10 stage-4 and 2 stage-3 BRS scores of upper extremity, and 9 stage-4 and 3 stage-3 BRS scores of hand) were recruited, and all were received motor rehabilitation therapy at the Rehabilitation Medicine Center of the Nanjing Tongren Hospital of China. Before the tasks began, ethical approval was obtained from the Medical Ethics Committee of the Nanjing Tongren Hospital of China, and all subjects were informed about the procedure and that they would be video-recorded and photo-taken during the experiment. All subjects gave written informed consent concerning the use of their video footage and questionnaire data for further analysis. Of the 12 stroke participants, one stroke subject (upper extremity and hand BRS scores were both stage-3) was not able to complete phase I experiments, and the rest also took part in phase II closed-loop experiments.


Continue —> Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation – Guozheng Xu, Xiang Gao, Lizheng Pan, Sheng Chen, Qiang Wang, Bo Zhu, Jinfei Li, 2018

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[WEB SITE] PaRRo Portable Arm Robot Designed for Rehab

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University of Michigan researchers have designed a low-cost, portable arm rehabilitation robot, which they suggest can be used at home and facilitate motor recovery in patients with cerebral palsy, stroke, or spinal cord injury.

The development of the rehab robot, named PaRRo, is described in a study published in the journal IEEE Transactions on Biomedical Engineering.

PaRRo was designed to provide task-specific training, according to the researchers, in a news story from Cerebral Palsy News Today.

It features an effector at the end of a robotic arm, which is engineered to be maneuvered by the patient. The effector is connected to a system of brakes that offer resistance to the arm’s movement, training muscle strength and improving arm resistance.

The amount of resistance can be controlled by each patient, meaning that the arm exercise intensities can be adapted to each patient’s motor skills.

However, the news story continues, the rehab robot is passive, which means it does not have any computer control, nor does it actively operate by taking over from the user.

In their research, the team performed simulations to calculate the robot’s resistive force and workspace. They then constructed a prototype based on these results, which was tested in a healthy male volunteer with no neurological or orthopedic impairments.

Nine surface electrodes were placed in different muscles and recorded the muscle activity via electromyography.

Both the force generated by the robot and the force produced by the user matched those predicted by the simulations when the device was moved across different directions.

Electromyography results also revealed the robot was capable of generating resistive forces adjustable to the subject’s motor abilities, the news story explains.

“These results indicate that PaRRo is a feasible low-cost approach to provide functional resistance training to the muscles of the upper-extremity,” according to the researchers, in the study.

“The proposed robotic device could provide a technological breakthrough that will make rehabilitation robots accessible for small outpatient rehabilitation centers and in-home therapy,” they add.

[Source: Cerebral Palsy News Today]


via PaRRo Portable Arm Robot Designed for Rehab – Rehab Managment

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[ARTICLE] Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies – Full Text



The application of rehabilitation robots has grown during the last decade. While meta-analyses have shown beneficial effects of robotic interventions for some patient groups, the evidence is less in others. We established the Advanced Robotic Therapy Integrated Centers (ARTIC) network with the goal of advancing the science and clinical practice of rehabilitation robotics. The investigators hope to exploit variations in practice to learn about current clinical application and outcomes. The aim of this paper is to introduce the ARTIC network to the clinical and research community, present the initial data set and its characteristics and compare the outcome data collected so far with data from prior studies.


ARTIC is a pragmatic observational study of clinical care. The database includes patients with various neurological and gait deficits who used the driven gait orthosis Lokomat® as part of their treatment. Patient characteristics, diagnosis-specific information, and indicators of impairment severity are collected. Core clinical assessments include the 10-Meter Walk Test and the Goal Attainment Scaling. Data from each Lokomat® training session are automatically collected.


At time of analysis, the database contained data collected from 595 patients (cerebral palsy: n = 208; stroke: n = 129; spinal cord injury: n = 93; traumatic brain injury: n = 39; and various other diagnoses: n = 126). At onset, average walking speeds were slow. The training intensity increased from the first to the final therapy session and most patients achieved their goals.


The characteristics of the patients matched epidemiological data for the target populations. When patient characteristics differed from epidemiological data, this was mainly due to the selection criteria used to assess eligibility for Lokomat® training. While patients included in randomized controlled interventional trials have to fulfill many inclusion and exclusion criteria, the only selection criteria applying to patients in the ARTIC database are those required for use of the Lokomat®. We suggest that the ARTIC network offers an opportunity to investigate the clinical application and effectiveness of rehabilitation technologies for various diagnoses. Due to the standardization of assessments and the use of a common technology, this network could serve as a basis for researchers interested in specific interventional studies expanding beyond the Lokomat®.


The number of technological devices that therapists can utilize to treat people with neurological impairments has grown substantially during the last decade. Alongside this growth in clinical use, research involving robotic therapy has grown rapidly. A search in Pubmed with the terms “robot” OR “robotic*” AND “rehabilitation” revealed 2225 hits (March 2017) with research markedly increasing after 2010. Despite this increase in research activity and clinical use, the effectiveness of robot-assisted interventions in neurorehabilitation is still in debate. While in some patient populations, for example adults with stroke, meta-analyses have shown that robotic interventions for the lower and upper extremity can be beneficial [12], current evidence is much less convincing in other patient groups, such as spinal cord injury (SCI), traumatic brain injury (TBI), multiple sclerosis (MS) and cerebral palsy (CP).

When comparing the effectiveness of robot-assisted gait training (RAGT) to conventional interventions of similar dosage in adult patients after SCI, it appears that neither intervention is superior [34]. In other populations, such as MS, a small number of pilot studies have been conducted, and a review [5] concluded that the evidence for the effectiveness remained inconclusive. In adult patients with TBI, to our knowledge, there is only one randomized controlled trial that investigated the effectiveness of RAGT [6]. While RAGT improved gait symmetry compared to manually assisted body-weight supported treadmill training, improvements in other gait parameters were not different between the interventions. In children with CP, the body of evidence is similarly small, as only two randomized trials were found [78]. To the authors’ knowledge, there are no randomized controlled trials in children with other diagnoses. Studies comparing effectiveness between different patient groups are lacking.

One important factor leading to the lack of conclusive research is the relatively small number of available centers and participating patients and consequently the small statistical power of attempted studies. Multicenter collaborations are needed to achieve adequate number of participants. Several of the limitations in the evidence of the application of RAGT arise from patient selection criteria and use of different, poorly described and/or low-dosed training protocols. For example, when systematically reviewing the literature in children, we found no paper describing a training protocol on how to apply a robot for rehabilitation of gait [9]. Most of the systematic reviews mentioned that it is extremely difficult to pool results from studies due to the large variability in treatment duration and frequency, contents of the training and inclusion criteria of the patients. For children with CP, an expert team was created to formulate goals, inclusion criteria, training parameters and recommendations on including RAGT in the clinical setting, to assist therapists who train children with CP with the Lokomat® (Hocoma AG, Volketswil, Switzerland) [9]. Such information could be used as a first step in defining training protocols, but this information is missing for most other patient groups.

While randomized controlled trials are usually considered the “gold standard” in building solid evidence in the field of medicine, it is often difficult for rehabilitation specialists working in the clinical environment to interpret the findings with respect to the population of patients they treat on a daily basis. Randomized controlled trials require a specialized team, a controlled setting and a strict selection of patients according to well defined inclusion and exclusion criteria. These criteria often select individuals most likely to benefit based on specific parameters and lack of co-morbidities. These narrow criteria may impact the ecological validity, as results only apply to a minority of patients. This was recently investigated by Dörenkamp et al. [10] who reported that the majority of patients in primary care (40% at the age of 50 years and at least two-thirds of the octogenarian population [11]) simultaneously suffered from multiple medical problems. Further, improvements in function might be less comparable to results described in randomized controlled trials and the treatment regimens used may not be applicable to patients with multiple comorbidities.

To overcome these issues, we established the Advanced Robotic Therapy Integrated Centers (ARTIC) network to collect data from patients using RAGT in a wide variety of clinical settings. ARTIC hopes to develop guidelines for usage as well as to answer scientific questions concerning the use of RAGT. While the ARTIC network includes a general patient population, other research networks focus on a specific disorder or diagnostic group (see, for example [1213]). ARTIC focuses on a common technological intervention – currently the driven gait orthosis Lokomat® – and aims to gather evidence for the efficient and effective use of robotic therapy. Variation in practice among ARTIC members together with collection of common data and outcome measurements will enable the group to draw strong, generalizable conclusions. Further goals include establishing standardized treatment protocols and increasing medical and governmental acceptance of robotic therapy. The aims of this paper are to introduce the ARTIC network to the clinical and research community, present initial data on the characteristics of included patients and compare these to those known from existing epidemiological data and interventional studies.[…]


Continue —> Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1 Lokomat® system (of different generations) with (a) adult leg orthoses and (b) pediatric leg orthoses. Patients walk on a treadmill belt, are weight supported, and the exoskeleton device guides the legs through a physiological walking pattern

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[ARTICLE] The Optimal Speed for Cortical Activation of Passive Wrist Movements Performed by a Rehabilitation Robot: A Functional NIRS Study – Full Text

Objectives: To advance development of rehabilitation robots, the conditions to induce appropriate brain activation during rehabilitation performed by robots should be optimized, based on the concept of brain plasticity. In this study, we examined differences in cortical activation according to the speed of passive wrist movements performed by a rehabilitation robot.

Methods: Twenty three normal subjects participated in this study. Passive movements of the right wrist were performed by the wrist rehabilitation robot at three different speeds: 0.25 Hz; slow, 0.5 Hz; moderate and 0.75 Hz; fast. We used functional near-infrared spectroscopy to measure the brain activity accompanying the passive movements performed by a robot. The relative changes in oxy-hemoglobin (HbO) were measured in two regions of interest (ROI): the primary sensory-motor cortex (SM1) and premotor area (PMA).

Results: In the left SM1 the HbO value was significantly higher at 0.5 Hz, compared with movements performed at 0.25 Hz and 0.75 Hz (p < 0.05), while no significant differences were observed in the left PMA (p > 0.05). In the group analysis, the left SM1 was activated during passive movements at three speeds (uncorrected p < 0.05) and the greatest activation in the SM1 was observed at 0.5 Hz.

Conclusions: In conclusion, the contralateral SM1 showed the greatest activation by a moderate speed (0.5 Hz) rather than slow (0.25 Hz) and fast (0.75 Hz) speed. Our results suggest an ideal speed for execution of the wrist rehabilitation robot. Therefore, our results might provide useful data for more effective and empirically-based robot rehabilitation therapy.


A number of rehabilitation robots have been developed in the past two decades to aid functional recovery of impaired limbs in patients with brain injury (Volpe et al., 2000Hesse et al., 2005Kahn et al., 2006Lum et al., 2006Masiero et al., 2007Nef et al., 2007Coote et al., 2008Housman et al., 2009Chang et al., 2014). In the field of rehabilitation, high intensive, task-oriented and repetitive execution of movements is effective for functional recovery of impaired upper limbs following brain injury (Bütefisch et al., 1995Kwakkel et al., 2004Schaechter, 2004Levin et al., 2008Murphy and Corbett, 2009Oujamaa et al., 2009). Rehabilitation robots can easily and precisely provide these labor-intensive rehabilitative treatments, and the effect of rehabilitation robots on functional recovery in patients with brain injury has been demonstrated in many studies (Volpe et al., 2000Hesse et al., 2005Lum et al., 2006Masiero et al., 2007Coote et al., 2008Norouzi-Gheidari et al., 2012). Compared to conventional therapy (CT) provided by a therapist, the effectiveness of robot assisted therapy (RT) is questionable (Masiero et al., 2011Norouzi-Gheidari et al., 2012). There is no difference between RT and intensive CT of the same duration/intensity condition, and extra sessions of RT in addition to CT bring better motor recovery of the shoulder and elbow (not for hand and wrist) compared with CT (Norouzi-Gheidari et al., 2012). To make the best use of robot for upper limb rehabilitation, increased efficacy of robotic rehabilitation is necessary. However, research on the optimal conditions to maximize the rehabilitative effect during treatment with a rehabilitation robot has been limited (Reinkensmeyer et al., 2007).

Brain plasticity, the ability of our brain system to reorganize its structure and function, is the basic mechanism underlying functional recovery in patients with brain injury (Schaechter, 2004Murphy and Corbett, 2009). The underlying principle of rehabilitation in terms of brain plasticity is based on the modulation of cortical activation induced by the manipulation of external stimuli (Kaplan, 1988). Little is known about the cortical effects resulting from rehabilitation robot treatment (Li et al., 2013Chang et al., 2014Jang et al., 2015).

Functional neuroimaging techniques, including functional MRI (fMRI), Positron Emission Tomography (PET) and functional Near Infrared Spectroscopy (fNIRS) provide important information about the activation of the brain by external stimuli (Frahm et al., 1993Willer et al., 1993Miyai et al., 2001Fujii and Nakada, 2003Perrey, 2008Kim et al., 2011Leff et al., 2011Gagnon et al., 2012). Of these, fNIRS provides a non-invasive method for measurement of the hemodynamic responses associated with activation of the cerebral cortex based on the intrinsic optical absorption of blood (Arenth et al., 2007Irani et al., 2007Perrey, 2008Ye et al., 2009Leff et al., 2011). Compared with other functional neuroimaging techniques, fNIRS has a unique advantage of less sensitivity to motion artifact and metallic material. Therefore, fNIRS appears suitable for the study of brain response during treatment with rehabilitation robots (Perrey, 2008Mihara et al., 2010Leff et al., 2011Li et al., 2013Chang et al., 2014).

In this study, we hypothesized that there exists optimal conditions for robotic rehabilitation to enhance the rehabilitative effect. The speed of movement performed by rehabilitation robot could be a unique aspect of robot rehabilitation, because varied speed can be provided consistently only with the robot. To confirm our hypothesis, using fNIRS, we examined the optimal speed of passive wrist movements performed by a rehabilitation robot that induces cortical activation through proprioceptive input by passive movements (Radovanovic et al., 2002Francis et al., 2009Lee et al., 2012). As a part of upper limb, the wrist enhances the usefulness of the hand by allowing it to take different orientations with respect to the elbow (van der Lee, 2001). If there exists an optimal speed that offers the greatest cortical activation, it could be applicable for robotic rehabilitation and research for other optimal conditions such as duration.

Subjects and Methods


Healthy right-handed subjects (15 males, 8 females; mean age 26.5, range 21–30) with no history of neurological, psychiatric, or physical illness were recruited for this study. Handedness was evaluated using the Edinburg Handedness Inventory (Oldfield, 1971). All subjects were fully informed about the purpose of the research and provided written, informed consent prior to participation in this study. The study protocol was approved by the Institutional Review Board of the Daegu Gyeongbuk Institute of Science and Technology (DGIST). Data from two subjects were excluded because the subjects did not follow the required instructions during the data collection.



Regarding flexion and extension only, the human wrist can be simplified as a one degree of freedom (DOF) kinematic model with one revolute joint (Zatsiorsky, 2002). As mentioned above, the wrist rehabilitation robot was designed and manufactured as a simplified kinematic model of the wrist. The robot used for wrist rehabilitation has three parts: hand, wrist joint and forearm, and provides passive movement of flexion and extension (Figure 1). It has a gear driven mechanism using a single motor. The actuation system for the wrist part is composed of DC, a brushless motor with encoder (EC-i 40, Maxon motor), harmonic drive (CSF-11-50, Sam-ik THK, gear ratio 50:1), and force-torque sensor (Mini 45, ATI). In house developed software was used to control the robot. For the real-time control, Linux Fedora 11 and the Real Time Application Interface for Linux (RTAI) Ver 3.8 systems were mounted. Real-time sensing control was achieved using an encoder and Sensoray s626 board, in which time delay control (TDC) was used for precise position control. The robot showed a position error of 0.1°–1° during the experiment.

Enter Figure 1. (A) The wrist rehabilitation robot. Lateral view of the wrist rehabilitation robot, the hand part (dotted line), wrist part (solid line) and forearm part (dashed line). (B) A front view of robot and subjects with the trunk strap and near infrared spectroscopy (NIRS) optodes. (C) Wrist flexion of the robot. (D) Wrist extension of the robot.a caption

 When using the robot for wrist rehabilitation, the hand and forearm must be fixed to the robot in order to perform the passive wrist movement. First, the subjects placed their forearm on the armrest made of foam covered with a soft cloth. They were instructed to place their hand on the support bar under the hand part of the robot before fixing all fingers to the finger holder with velcro straps. The robot performs the passive wrist exercise using a rotary motion of a gear driven by a motor and realizes a full range of motion (ROM) from 80° (flexion) to 75° (extension) when the degree of neutral wrist position is 0°, with the wrist in a flat position, with velocity of the wrist motions up to 2 Hz.[…]


Continue —> Frontiers | The Optimal Speed for Cortical Activation of Passive Wrist Movements Performed by a Rehabilitation Robot: A Functional NIRS Study | Frontiers in Human Neuroscience

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[ARTICLE] A novel approach for robot-assisted upper-limb rehabilitation – Full Text

This study presented a novel control approach for rehabilitation robotic system using the hybrid system theory and the subject’s bio-damping and bio-stiffness parameters. Resistance training was selected as a paradigm. The proposed control architecture incorporated the physical therapist’s behavior intervention, the stroke survivor’s muscle strength changes, and the robotic device’s motor control into a unified framework. The main focuses of this research were to (i) automatically monitor the subject’s muscle strength changes using the online identified bio-damping/stiffness parameters; (ii) make decisions on the modification of the desired resistive force so as to coincide with the subject’s muscle strength changes; and (iii) generate accommodating plans when the safety-related issues such as spasticity and the abnormal robotic working states happen during the execution of training tasks. A Barrett WAM compliant manipulator-based resistance training system and two experiments including four scenarios were developed to verify the proposed approach. Experimental results with healthy subjects showed that the hybrid system–based control architecture could administrate the subject’s muscle strength changes and the robotic device’s interventions in an automated and safe manner.

Patients suffering from strokes need to receive intensive rehabilitation training to improve range of motion, movement coordination, and muscle strength. These therapy programs are traditionally conducted by physical therapists. However, the efficacy of therapist-centered rehabilitation therapy often relies on the therapist’s subjective clinical experience. In recent years, there is an increasing interest in using robotic devices to help providing rehabilitation training because these devices can provide a variety of highly repetitive movements and training protocols for stroke patients.1

Human–robot interactive control approaches, that specify how rehabilitation robotic devices adaptively interact with stroke survivors in an automated and safe manner, are one of the major challenges in developing robot-assisted training system. The control strategies explored in some early rehabilitation robotic devices, such as the proportional integral derivative controller for MIME,2 the impedance controller for MIT-Manus,3 and the admittance controller for GENTLE/s,4are mainly concentrated on providing constant assistance while not integrating stroke patients’ feedback into the control loop. It is a common hypothesis in the field of robot-assisted rehabilitation that the control approaches, that can close the loop via the patients and further adapt robotic devices’ assistance to the stroke patients’ progress, will be more efficient.5 This issue is typically addressed using assist-as-needed6,7 or user-cooperative control strategies.8,9 In the study by Hussain et al.,6 an adaptive seamless assist-as-needed control scheme is developed for the robotic gait training, which learns in real time the disability level of human subjects based on the trajectory tracking errors and adapts the robotic assistance accordingly. Riener et al.8 presents a patient-cooperative strategy for robotic gait training, and results with healthy subjects show that subjects train more actively and only get support as much as needed. Nonetheless, the potential issue of these methods is that they focus on low-level robotic motor controllers. The training tasks update, the safety-related issues (e.g. spasticity and twitch) monitoring, the robotic working states (e.g. joint torque, voltage, workload, and end-effector velocity) detection, the therapeutic progress assessment, and the decision-making behaviors are all administered by physical therapists.

In the last few years, hierarchical supervisory control strategies have been developed. These approaches incorporate the training tasks update and the physical therapist’s behavior decision into the low-level robotic motor controller. Denève et al.10 merge a high-level sequential controller into a robotic-assisted upper limb rehabilitation system, by which three different low-level control laws for passive, active, and resistance modes can be switched. In the study by Varol et al.,11 a three-level hierarchical supervisory control architecture is proposed, which consists of the lowest level robotic joint torque controller, the middle-level torque references generator, and the high-level intent recognizer. Fuzzy-based hierarchical supervisory control strategies are also presented in our previous studies.12,13 Especially in the study,13 a high-level safety supervisory controller is designed to monitor spasticity-related issues. Great improvements have been made in the current hierarchical supervisory control architecture for incorporating therapists’ behavior-decision experiences. Unfortunately, these supervisory control methods are statically designed concerning some predefined situations, and the absence of dynamic mechanism makes them incapable of dealing with the extended/unexpected events and the complex training tasks coordination. Besides, few rehabilitation robotic control system design takes the safety-related issues into consideration.

In this article, we present a hybrid system–based control architecture using resistance training as a paradigm, which can incorporate the physical therapist’s behavior intervention, the stroke survivor’s muscle strength progress, and the robotic device’s motor control into a unified framework. In fact, over the years, the hybrid systems framework has been effectively used in many fields to model and analyze their performances, such as the power systems,14 the communication networks,15 and the coordinated control of assistive robotic devices for complex tasks.16

The primary focus of this article is to (i) automatically monitor the impaired limb’s muscle strength progress using the online identified bio-impedance changes; (ii) make decisions on the modification of the desired resistive force so as to coincide with the impaired limb’s muscle strength changes; and (iii) be aware of robotic working state/safety-related issues during the execution of training task and to generate accommodating plans when such events happen. The remainder of this article is organized as follows: “Methods” section presents the experimental setup and protocols, bio-impedance parameters identification, and controller development. “Results” section details the results of the proposed control strategy. Some discussions and conclusions are given in sections “Discussions” and “Conclusions”.


Experimental setup

The rehabilitation robotic system for upper limb muscle strength training used in the trials, shown in Figure 1(a), consisted of a Barrett WAM™ manipulator, a three dimensional (3-D) force sensor, and an external PC offered by Barrett Technology.17 The standard WAM™ is a four degree of freedom highly dexterous, back-drivable manipulator. Its human-like kinematics and high back drivability enable inherent force-control, haptic interaction, and rehabilitation application. In order to record the force interaction between the impaired limb and the WAM end-effector, a 3-D force sensor was designed and attached to the end-effector. Figure 1(b) and (c) shows the mechanical structure, appearance, and strain gauges distribution of the 3-D force sensor. Force data measured from the sensor must be transformed from the WAM tool frame into its world frame. The graphical user interface developed using Linux/GDK technology, shown in Figure 1(d), was used to display the actual training trajectory when the patient moved his arm in the XOZ vertical plane (O-XYZ coordinates shown in Figure 1(a)), where no reference trajectories were predefined except for several via points. The external PC, running with the Ubuntu Linux system and the Xenomai real-time module, was responsible for executing the control loop and sending high-level commands to the WAM-aided rehabilitation training system. Real-time communication between the external PC and motor Pucks™ was conducted via a high-speed controller area network bus.


Figure 1. The Barrett WAM™ rehabilitation robotic system for upper limb muscle strength training and its attachments. (a) The Barrett WAM™ rehabilitation robotic system, (b) mechanical structure and appearance of the 3-D force sensor, (c) distribution of 16 strain gauges on the cross beam, and (d) the graphical user interface for muscle training in XOZ plane.


Continue —>  A novel approach for robot-assisted upper-limb rehabilitationInternational Journal of Advanced Robotic Systems – Guozheng Xu, Xiang Gao, Sheng Chen, Qiang Wang, Bo Zhu, Jinfei Li, 2017

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[Abstract] Recent Advances on Lower Limb Exoskeleton Rehabilitation Robot


Background: Lower limb exoskeleton rehabilitation robot is a bionic robot, which is the product of the combination of medical technology and robot technology, simulating human walking movement. It can be mainly used for rehabilitation training of patients with lower limb dysfunction.

Objective: To provide an overview of recent lower limb exoskeleton rehabilitation robot and introduce their respective characteristics and development.

Method: A recent lower limb exoskeleton rehabilitation robot is divided into passive drive, pneumatic drive, hydraulic drive and motor drive. This paper reviews various representative patents related to lower limb exoskeleton rehabilitation robot. The structural characteristics and applications of the typical lower limb exoskeleton rehabilitation robots are introduced.

Results: The differences between different types of lower limb exoskeleton rehabilitation robots are compared and analyzed, and the structural characteristics are concluded. The main problems in its development are analyzed, the development trend is foreseen, and the current and future research of the patents on lower limb exoskeleton rehabilitation robot is discussed.

Conclusion: There are a lot of patents and articles about the exoskeleton rehabilitation robots, however, if these problems can be solved, such as small size, light weight and high power output are solved at the same time, the consistency with human body will be advanced, with the combination of traditional rehabilitation medicine. It will be possible to maximize the rehabilitation of the lower limbs.

Source: Recent Advances on Lower Limb Exoskeleton Rehabilitation Robot: Ingenta Connect

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