Wrist-Based Haptic Rehabilitation Device for Stroke using Body-Grounded Haptic Concept: A Review

Stroke is a major cause of long-term disability, and impaired wrist and hand function can greatly limit independence in daily activities. Conventional rehabilitation remains important, but it is often constrained by therapist availability, limited training intensity, and reduced patient motivation during repetitive exercises. Haptic and robotic-assisted rehabilitation devices have been introduced to support repetitive, measurable, and feedback-driven therapy. However, many existing systems are bulky, costly, or designed mainly for clinical and laboratory settings, limiting their wider use in home-based or low-resource rehabilitation settings. This paper reviews selected wrist-based haptic and rehabilitation devices and proposes the development of a compact body-grounded haptic device, named H³-WristKit, for post-stroke wrist rehabilitation. The proposed device focuses on wrist flexion-extension movement and is designed to deliver adaptive assistive and resistive feedback using impedance control. Existing interfaces, including Hapkit, H³Kit, pneumatic wrist rehabilitation robots, low-cost wrist robots, and RiceWrist-S, were compared in terms of design, portability, advantages, and limitations. The review shows a clear gap between high-performance grounded systems and portable educational haptic devices. The proposed H³-WristKit aims to address this gap by combining a lightweight body-grounded structure, low-cost fabrication, and rehabilitation-oriented control. Future work should include prototype fabrication, control validation, safety testing, usability evaluation, and preliminary testing with post-stroke users to assess practical feasibility, patient adherence, and user satisfaction. This work provides an early design direction for a more accessible wrist rehabilitation device that may support continuous upper-limb therapy after stroke.

body-grounded haptics, haptic feedback, rehabilitation robotics, stroke rehabilitation, wrist rehabilitation

1. L. Pu, L. Wang, R. Zhang, T. Zhao, Y. Jiang, and L. Han, “Projected global trends in ischemic stroke incidence, deaths and disability-adjusted life years from 2020 to 2030,” Stroke, vol. 54, no. 5, pp. 1330–1339, 2023, doi: 10.1161/STROKEAHA.122.040073. [Google Scholar] [Crossref]

2. Centers for Disease Control and Prevention, “About stroke,” May 22, 2024. [Online]. Available: https://www.cdc.gov/stroke/about/index.html [Google Scholar] [Crossref]

3. J. M. Veerbeek, E. E. H. van Wegen, J. van der Heijden, and G. Kwakkel, “Robot-assisted therapy for the upper limb after stroke: A systematic review and meta-analysis,” Gait & Posture, vol. 39, no. 2, pp. 682–687, 2014, doi: 10.1016/j.gaitpost.2013.09.018. [Google Scholar] [Crossref]

4. D. J. Reinkensmeyer and J. Patton, “Robotics, motor learning, and neurologic recovery,” Annual Review of Biomedical Engineering, vol. 11, pp. 249–277, 2009, doi: 10.1146/annurev-bioeng-060908-150139. [Google Scholar] [Crossref]

5. V. S. Huang and J. W. Krakauer, “Robotic neurorehabilitation: A computational motor learning perspective,” Journal of NeuroEngineering and Rehabilitation, vol. 6, article 5, 2009, doi: 10.1186/1743-0003-6-5. [Google Scholar] [Crossref]

6. J. Roh et al., “Assessment of upper-limb function in patients with chronic stroke using kinematic measures during robot-assisted therapy,” Journal of NeuroEngineering and Rehabilitation, vol. 10, article 109, 2013, doi: 10.1186/1743-0003-10-109. [Google Scholar] [Crossref]

7. J. A. Kleim and T. A. Jones, “Principles of experience-dependent neural plasticity: Implications for rehabilitation after brain damage,” Journal of Speech, Language, and Hearing Research, vol. 51, no. 1, pp. S225–S239, 2008, doi: 10.1044/1092-4388(2008/018). [Google Scholar] [Crossref]

8. C. J. Winstein et al., “Guidelines for adult stroke rehabilitation and recovery: A guideline for healthcare professionals from the American Heart Association/American Stroke Association,” Stroke, vol. 47, no. 6, pp. e98–e169, 2016, doi: 10.1161/STR.0000000000000098. [Google Scholar] [Crossref]

9. K. S. Tan and N. Venketasubramanian, “Stroke burden in Malaysia,” Cerebrovascular Diseases Extra, vol. 12, no. 2, pp. 58–62, 2022. [Google Scholar] [Crossref]

10. Flint Rehab, “10 helpful hand exercises for stroke patients of all ability levels,” Dec. 13, 2022. [Online]. Available: https://www.flintrehab.com/hand-exercises-for-stroke-patients/ [Google Scholar] [Crossref]

11. B. T. Volpe et al., “Intensive sensorimotor arm training mediated by therapist or robot improves hemiparesis in patients with chronic stroke,” Neurorehabilitation and Neural Repair, vol. 22, no. 3, pp. 305–310, 2008, doi: 10.1177/1545968307313489. [Google Scholar] [Crossref]

12. B. T. Volpe, H. I. Krebs, N. Hogan, L. Edelstein, C. Diels, and M. Aisen, “A novel approach to stroke rehabilitation: Robot-aided sensorimotor training,” Neurology, vol. 54, no. 10, pp. 1938–1944, 2000, doi: 10.1212/WNL.54.10.1938. [Google Scholar] [Crossref]

13. S. C. Cramer, “Treatments to promote neural repair after stroke,” Journal of Clinical Investigation, vol. 128, no. 1, pp. 77–86, 2018, doi: 10.1172/JCI94464. [Google Scholar] [Crossref]

14. J. E. Harris, J. J. Eng, W. C. Miller, and A. S. Dawson, “A self-administered Graded Repetitive Arm Supplementary Program improves arm function during inpatient stroke rehabilitation: A multi-center randomized controlled trial,” Stroke, vol. 40, no. 6, pp. 2123–2128, 2009, doi: 10.1161/STROKEAHA.108.544585. [Google Scholar] [Crossref]

15. Y. Li, J. Chen, H. Chen, R. Huang, and X. Ma, “A greedy assist-as-needed controller for end-effector upper limb rehabilitation robot based on 3-DOF potential field constraints,” Frontiers in Neurorobotics, vol. 17, 2023, doi: 10.3389/fnbot.2023.1118182. [Google Scholar] [Crossref]

16. S. Kwok et al., “A systematic review of neurorehabilitation robotics: A practical guide for clinicians,” Brain Injury, vol. 31, no. 4–5, pp. 527–534, 2017, doi: 10.1080/02699052.2017.1302834. [Google Scholar] [Crossref]

17. D. O. Kleindorfer et al., “2021 guideline for the prevention of stroke in patients with stroke and transient ischemic attack,” Stroke, vol. 52, no. 7, pp. e364–e467, 2021, doi: 10.1161/STR.0000000000000375. [Google Scholar] [Crossref]

18. S. Pezeshki, M. Poursina, A. Khosravi, D. Al-Jumeily, and S. Lughmani, “A comprehensive review of control challenges and methods in end-effector upper-limb rehabilitation robots,” Sensors, vol. 20, no. 22, article 6432, 2020, doi: 10.3390/s20226432. [Google Scholar] [Crossref]

19. K. Boardsworth et al., “Upper limb robotic rehabilitation following stroke: A systematic review and meta-analysis investigating efficacy and the influence of device features and program parameters,” Journal of NeuroEngineering and Rehabilitation, vol. 22, article 164, 2025, doi: 10.1186/s12984-025-01662-4. [Google Scholar] [Crossref]

20. MAHI Lab, “Human motion assessment for rehabilitation,” Rice University. [Online]. Available: https://mahilab.rice.edu/research/human-motion-assessment-rehabilitation [Google Scholar] [Crossref]

21. A. Agarwal and A. D. Deshpande, “An improved adaptive robotic assistance methodology for upper-limb rehabilitation,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3624–3631, 2018, doi: 10.1109/LRA.2018.2851748. [Google Scholar] [Crossref]

22. N. J. Cowan et al., “Control, analysis, and implementation of haptic systems,” IEEE Control Systems Magazine, vol. 29, no. 3, pp. 70–81, 2009, doi: 10.1109/MCS.2009.932918. [Google Scholar] [Crossref]

23. M. O. Martinez et al., “Evolution and analysis of Hapkit: An open-source haptic device for educational applications,” IEEE Transactions on Haptics, vol. 13, no. 2, pp. 354–367, 2020. [Google Scholar] [Crossref]

24. S. Rehmat and H. Culbertson, “H³Kit: Hand-held haptic kit for STEM education,” in 2019 IEEE World Haptics Conference, 2019, pp. 401–406. [Google Scholar] [Crossref]

25. Stanford University, “Build one,” Hapkit. [Online]. Available: https://hapkit.stanford.edu/build.html [Google Scholar] [Crossref]

26. R. Bloom, Z. Huang, K. Lavarias, M. Ren, and T. K. Morimoto, “H³Kit: Hand-held haptic kit for STEM education,” in Proceedings of the IEEE Haptics Symposium, 2024. [Google Scholar] [Crossref]

27. UCSD Morimoto Lab, “H3Kit,” GitHub. [Online]. Available: https://github.com/UCSDMorimotoLab/H3Kit [Google Scholar] [Crossref]

28. M. B. Näf, K. Junius, M. Rossini, C. Rodriguez-Guerrero, B. Vanderborght, and D. Lefeber, “Misalignment compensation for full human-exoskeleton kinematic compatibility: State of the art and evaluation,” Applied Mechanics Reviews, vol. 70, no. 5, article 050802, 2018. [Google Scholar] [Crossref]

29. H. Al-Fahaam, S. Davis, and S. Nefti-Meziani, “Mechanical design, fabrication, kinematics and dynamics modeling, multiple impedance control of a wrist rehabilitation robot,” 2016. [Details to verify] [Google Scholar] [Crossref]

30. S. Bu, “The 2-DOF wrist rehabilitation robot,” 2018. [Details to verify] [Google Scholar] [Crossref]

31. E. T. Wolbrecht, J. Leavitt, D. J. Reinkensmeyer, and J. E. Bobrow, “Control of a pneumatic rehabilitation robot for optimizing plastic recovery after stroke,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 16, no. 3, pp. 294–304, 2008. [Google Scholar] [Crossref]

32. L. A. Jones and H. Z. Tan, “Application of psychophysical techniques to haptic research,” IEEE Transactions on Haptics, vol. 6, no. 3, pp. 268–284, 2013, doi: 10.1109/TOH.2012.12. [Google Scholar] [Crossref]

33. F. A. Mussa-Ivaldi and D. J. Reinkensmeyer, “Physical human-robot interaction in rehabilitation and assistive robotics,” in Biomedical Engineering Handbook, CRC Press, 2008. [Google Scholar] [Crossref]

34. Ambitious Impact, “What are DALYs and are they a good metric?” Jan. 23, 2014. [Online]. Available: https://www.ambitiousimpact.com/post/what-are-dalys-and-are-they-a-good-metric [Google Scholar] [Crossref]

35. L. Masia, M. Casadio, P. Giannoni, G. Sandini, and P. Morasso, “Performance adaptive training control strategy for a 2-DOF wrist exoskeleton,” Medical & Biological Engineering & Computing, vol. 47, no. 2, pp. 155–166, 2009. [Journal/details to verify] [Google Scholar] [Crossref]

36. W. J. Powers et al., “Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines,” Stroke, vol. 50, no. 12, pp. e344–e418, 2019, doi: 10.1161/STR.0000000000000211. [Google Scholar] [Crossref]

37. J. Bernhardt et al., “Agreed definitions and a shared vision for new standards in stroke recovery research: The Stroke Recovery and Rehabilitation Roundtable publication strategy,” International Journal of Stroke, vol. 12, no. 5, pp. 444–450, 2017, doi: 10.1177/1747493017711815. [Google Scholar] [Crossref]

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