Posts Tagged Wrist rehabilitation

[Abstract + References] Improving Motivation in Wrist Rehabilitation Therapies – Conference paper


Rehabilitation encompasses a wide variety of activities aimed at reducing the impact of injuries and disabilities by applying different exercises. Frequently, such exercises are carried out at home as a repetition of the same movements or tasks to achieve both motor learning and the necessary cortical changes. Although this increases the patients’ available time for rehabilitation, it may also have some unpleasant side effects. That occurs because carrying out repetitive exercises in a more isolated environment may result in a boring activity that leads patients to give up their rehabilitation. Therefore, patients’ motivation should be considered an essential feature while designing rehabilitation exercises. In this paper, we present how we have faced this need by exploiting novel technology to guide patients in their rehabilitation process. It includes a game crafted to make recovery funny and useful, at the same time. The game and the use we made of the specific hardware follow the recommendations and good practices provided by medical experts.


  1. 1.
    Aguiar, L.F., Bo, A.P.L.: Hand gestures recognition using electromyography for bilateral upper limb rehabilitation. In: 2017 IEEE Life Sciences Conference (LSC), pp. 63–66. IEEE (2017)Google Scholar
  2. 2.
    Amirabdollahian, F., Walters, M.L.: Application of support vector machines in detecting hand grasp gestures using a commercially off the shelf wireless myoelectric armband. In: 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 111–115 (2017)Google Scholar
  3. 3.
    Batista, T.V.V., Machado, L.S., Valenca, A.M.G.: Surface electromyography for game-based hand motor rehabilitation. In: 2016 XVIII Symposium on Virtual and Augmented Reality (SVR), pp. 140–144. IEEE (2016)Google Scholar
  4. 4.
    Bevilacqua, V., Brunetti, A., Trigiante, G., Trotta, G.F., Fiorentino, M., Manghisi, V., Uva, A.E.: Design and Development of a Forearm Rehabilitation System Based on an Augmented Reality Serious Game. Presented at the (2016)Google Scholar
  5. 5.
    Bütefisch, C., Hummelsheim, H., Denzler, P., Mauritz, K.H.: Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand. J. Neurol. Sci. 130(1), 59–68 (1995)CrossRefGoogle Scholar
  6. 6.
    Charles, S.K., Krebs, H.I., Volpe, B.T., Lynch, D., Hogan, N.: Wrist rehabilitation following stroke: initial clinical results. In: Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics, pp. 13–16. IEEE (2005)Google Scholar
  7. 7.
    Cialdini, R.B.: Influence: The Psychology of Persuation. Morrow, New York (1993)Google Scholar
  8. 8.
    Cram, J.R., Steger, J.C.: EMG scanning in the diagnosis of chronic pain. Biofeedback Self Regul. 8(2), 229–241 (1983)CrossRefGoogle Scholar
  9. 9.
    Deterding, S., Sicart, M., Nacke, L., O’Hara, K., Dixon, D.: Gamification using game-design elements in non-gaming contexts. In: 2011 Annual Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA 2011), pp. 24–25. ACM Press, Vancouver (2011)Google Scholar
  10. 10.
    Dromerick, A.W., Edwards, D.F., Hahn, M.: Does the application of constraint-induced movement therapy during acute rehabilitation reduce arm impairment after ischemic stroke? Stroke 31(12), 2984–2988 (2000)CrossRefGoogle Scholar
  11. 11.
    Esfahlani, S.S., Thompson, T., Parsa, A.D., Brown, I., Cirstea, S.: ReHabgame: a non-immersive virtual reality rehabilitation system with applications in neuroscience. Heliyon 4(2), e00526 (2018)CrossRefGoogle Scholar
  12. 12.
    He, S., Yang, C., Wang, M., Cheng, L., Hu, Z.: Hand gesture recognition using MYO armband. Chinese Automation Congress (CAC), 2017, pp. 4850–4855 (2017)Google Scholar
  13. 13.
    Holden, M.K.: Virtual environments for motor rehabilitation: review. CyberPsychology Behav. 8(3), 187–211 (2005)CrossRefGoogle Scholar
  14. 14.
    Horger, M.M.: The reliability of goniometric measurements of active and passive wrist motions. Am. J. Occup. Ther. 44(4), 342–348 (1990)CrossRefGoogle Scholar
  15. 15.
    Kingston, B.: Understanding Joints: A Practical Guide to Their Structure and Function. Nelson Thornes (2000)Google Scholar
  16. 16.
    Langan, J., Subryan, H., Nwogu, I., Cavuoto, L.: Reported use of technology in stroke rehabilitation by physical and occupational therapists. Disabil. Rehabil. Assist. Technol. 13(7), 1–7 (2017)Google Scholar
  17. 17.
    Leap Motion Inc: Leap Motion.
  18. 18.
    Van der Lee, J.H., Wagenaar, R.C., Lankhorst, G.J., Vogelaar, T.W., Devillé, W.L., Bouter, L.M.: Forced use of the upper extremity in chronic stroke patients: results from a single-blind randomized clinical trial. Stroke 30(11), 2369–2375 (1999)CrossRefGoogle Scholar
  19. 19.
    López-Jaquero, V., Montero, F., Teruel, M.A.: Influence awareness: considering motivation in computer-assisted rehabilitation. J. Ambient Intell. Humaniz. Comput. 10(6), 2018–2197 (2017)Google Scholar
  20. 20.
    Mendez, I., Hansen, B.W., Grabow, C.M., Smedegaard, E.J.L., Skogberg, N.B., Uth, X.J., Bruhn, A., Geng, B., Kamavuako, E.N.: Evaluation of the Myo armband for the classification of hand motions. In: 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 1211–1214 (2017)Google Scholar
  21. 21.
    World Health Organization: International Classification of Functioning, Disability and Health: ICF. World Health Organization (2001)Google Scholar
  22. 22.
    Ortiz-Catalan, M., Nijenhuis, S., Ambrosch, K., Bovend’Eerdt, T., Koenig, S., Lange, B.: Virtual reality. In: Emerging Therapies in Neurorehabilitation, pp. 249–265. Springer (2014)Google Scholar
  23. 23.
    Rechy-Ramirez, E.J., Marin-Hernandez, A., Rios-Figueroa, H.V.: A human-computer interface for wrist rehabilitation: a pilot study using commercial sensors to detect wrist movements. Vis. Comput., 1–15 (2017)Google Scholar
  24. 24.
    Sathiyanarayanan, M., Rajan, S.: MYO Armband for physiotherapy healthcare: A case study using gesture recognition application. In: 2016 8th International Conference on Communication Systems and Networks (COMSNETS), pp. 1–6 (2016)Google Scholar
  25. 25.
    Skirven, T.M., Osterman, A.L., Fedorczyk, J.M., Amadio, P.C.: Rehabilitation of the Hand and Upper Extremity. Mosby (2011)Google Scholar
  26. 26.
    Slutsky, D.J., Herman, M.: Rehabilitation of distal radius fractures: a biomechanical guide. Hand Clin. 21(3), 455–468 (2005)CrossRefGoogle Scholar
  27. 27.
    Tabor, A., Bateman, S., Scheme, E., Flatla, D.R., Gerling, K.: Designing game-based myoelectric prosthesis training. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems – CHI 2017, pp. 1352–1363. ACM Press, New York (2017)Google Scholar
  28. 28.
    Teruel, M.A., Navarro, E., González, P., López-Jaquero, V., Montero, F.: Applying thematic analysis to define an awareness interpretation for collaborative computer games. Inf. Softw. Technol. 74, 17–44 (2016)CrossRefGoogle Scholar
  29. 29.
    Thalmic Labs Inc.: Myo Gesture Control ArmbandGoogle Scholar
  30. 30.
    Vines, A.: Helping your wrist to recover after a fracture. Oxford University Hospitals NHS Trust (2015)Google Scholar
  31. 31.
    Wolf, S.L., Winstein, C.J., Miller, J.P., Taub, E., Uswatte, G., Morris, D., Giuliani, C., Light, K.E., Nichols-Larsen, D.: EXCITE investigators, for the: effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. J. Am. Med. Assoc. 296(17), 2095–2104 (2006)CrossRefGoogle Scholar
  32. 32.
    Zhou, H., Hu, H.: Human motion tracking for rehabilitation—a survey. Biomed. Signal Process. Control 3(1), 1–18 (2008)CrossRefGoogle Scholar

via Improving Motivation in Wrist Rehabilitation Therapies | SpringerLink

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[Abstract] Attention-controlled assistive wrist rehabilitation using a low-cost EEG Sensor


It is essential to make sure patients be actively involved in motor training using robot-assisted rehabilitation to achieve better rehabilitation outcomes. This paper introduces an attention-controlled wrist rehabilitation method using a low-cost EEG sensor. Active rehabilitation training is realized using a threshold of the attention level measured by the low-cost EEG sensor as a switch for a flexible wrist exoskeleton assisting wrist ?exion/extension and radial/ulnar deviation. We present a prototype implementation of this active training method and provide a preliminary evaluation. The feasibility of the attention-based control was proven with the overall actuation success rate of 95%. The experimental results also proved that the visual guidance was helpful for the users to concentrate on the wrist rehabilitation training; two types of visual guidance, namely looking at the hand motion shown on a video and looking at the user’s own hand, had no significant performance difference; a general threshold of a certain group of users can be utilized in the wrist robot control rather than a customized threshold to simplify the procedure.

via Attention-controlled assistive wrist rehabilitation using a low-cost EEG Sensor – IEEE Journals & Magazine

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Control of a exoskeleton with different sensors using a microcontroller and Matlab: This project will be used the exoskeleton for wrist rehabilitation and evaluation designed in the RoboticsLab. This device is actuated with SMA (Shape Memory Alloys) wires and it has two DOF. The objectives of the work will be: to integrate position and pressure sensors into the exoskeleton; to use the information of these sensors to control in position and / or strength the exoskeleton in repetitive tasks for the flexion-extension movement of the wrist; collect data on the execution of the task that could be used by the doctor to evaluate the patient’s progression.

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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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[Abstract] A pilot study on the optimal speeds for passive wrist movements by a rehabilitation robot of stroke patients: A functional NIRS study  


The optimal conditions inducing proper brain activation during performance of rehabilitation robots should be examined to enhance the efficiency of robot rehabilitation based on the concept of brain plasticity. In this study, we attempted to investigate differences in cortical activation according to the speeds of passive wrist movements performed by a rehabilitation robot for stroke patients. 9 stroke patients with right hemiparesis participated in this study. Passive movements of the affected wrist were performed by the rehabilitation robot at three different speeds: 0.25 Hz; slow, 0.5Hz; moderate and 0.75 Hz; fast. We used functional near-infrared spectroscopy to measure the brain activity during the passive movements performed by a robot. Group-average activation map and the relative changes in oxy-hemoglobin (ΔOxyHb) in two regions of interest: the primary sensory-motor cortex (SM1); premotor area (PMA) and region of all channels were measured. In the result of group-averaged activation map, the contralateral SM1, PMA and somatosensory association cortex (SAC) showed the greatest significant activation according to the movements at 0.75 Hz, while there is no significantly activated area at 0.5 Hz. Regarding ΔOxyHb, no significant diiference was observed among three speeds regardless of region. In conclusion, the contralateral SM1, PMA and SAC showed the greatest activation by a fast speed (0.75 Hz) rather than slow (0.25 Hz) and moderate (0. 5 Hz) speed. Our results suggest an optimal speed for execution of the wrist rehabilitation robot. Therefore, we believe that our findings might point to several promising applications for future research regarding useful and empirically-based robot rehabilitation therapy.

Source: A pilot study on the optimal speeds for passive wrist movements by a rehabilitation robot of stroke patients: A functional NIRS study – IEEE Xplore Document

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[ARTICLE] A Fuzzy-based Adaptive Rehabilitation Framework for Home-based Wrist Training – Full Text PDF


Computer-based rehabilitation systems have emerged as promising assistive tools for effective training and diagnosis and gained popularity in clinical settings.

For many patients, home-based rehabilitation can be really beneficial in their therapy journeys since it can eliminate the obstacles encountered by many of them in clinics, such as travel distance and cost. However, an effective home-training system requires a good adaptation mechanism that conforms to both the patient’s abilities and the therapist’s performance requirements.

This paper introduces a web-enabled wrist rehabilitation framework that adopts the fuzzy logic approach to provide adaptive tasks for the patient while taking into account the therapist training guidance.

We also assess the effectiveness of the framework while coping with different training parameters by simulating a number of performance scenarios and experimenting with normal subjects. Simulation results, as well as experimental analysis, demonstrated the ability of the proposed framework to adapt to patient’s performance and therapist’s feedback.

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[ARTICLE] Motion control of a novel robotic wrist exoskeleton via pneumatic muscle actuators


In this article, the motion control problem of a robotic EXOskeletal WRIST (EXOWRIST) prototype is considered. This novel robotic appliance’s motion is achieved via pneumatic muscle actuators, a pneumatic form of actuation possessing crucial attributes for the development of an exoskeleton that is safe, reliable, portable and low-cost. The EXOWRIST’s properties are presented in detail and compared to the recent wrist exoskeleton technology, while its two degrees-of-freedom movement capabilities (extension-flexion, ulnar-radial deviation) are experimentally evaluated on a healthy human volunteer via an advanced nonlinear PID-based control algorithm.

Source: IEEE Xplore Abstract (Abstract) – Motion control of a novel robotic wrist exoskeleton via pneumatic muscle actuator


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[ARTICLE] Development of wrist rehabilitation robot and interface system


The authors have developed a practical wrist rehabilitation robot for hemiplegic patients. It consists of a mechanical rotation unit, sensor, grip, and computer system. A myoelectric sensor is used to monitor the extensor carpi radialis longus/brevis muscle and flexor carpi radialis muscle activity during training. The training robot can provoke training through myoelectric sensors, a biological signal detector and processor in advance, so that patients can undergo effective training of extention and flexion in an excited condition. In addition, both-wrist system has been developed for mirror effect training, which is the most effective function of the system, so that autonomous training using both wrists is possible. Furthermore, a user-friendly screen interface with easily recognizable touch panels has been developed to give effective training for patients. The developed robot is small size and easy to carry. The developed aspiring interface system is effective to motivate the training of patients. The effectiveness of the robot system has been verified in hospital trails.

Source: Development of wrist rehabilitation robot and interface system – IOS Press

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