Journal of Bionic Engineering

, Volume 15, Issue 5, pp 783–794 | Cite as

Development of a Hand Exoskeleton System for Quantitative Analysis of Hand Functions

  • Jeongsoo Lee
  • Minhyuk Lee
  • Joonbum BaeEmail author


This paper proposes a hand exoskeleton system for evaluating hand functions. To evaluate hand functions, the hand exoskeleton system must be able to pull each finger joint, measure the finger joint angle and exerted force on the finger simultaneously. The proposed device uses serially connected 4-bar linkage structures, which have two embedded actuators with encoders and two loadcells per finger, to move each phalanx independently and measure the finger joint angles. A modular design was used for the exoskeleton, to facilitate the removal of unnecessary modules in different experiments and improve convenience. Silicon was used on the surface of the worn part to reduce the skin irritation that results from prolonged usage. This part was also designed to be compatible with various finger thicknesses. Using the proposed hand exoskeleton system, finger independence, multi-finger synergy, and finger joint stiffness were determined in five healthy subjects. The finger movement and force data collected in the experiments were used for analyzing three hand functions based on the physical and physiological phenomena.


evaluation of hand function hand rehabilitation wearable system exoskeleton 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This study was supported by Translational Research Center for Rehabilitation Robots (Grant No. NRCTR-EX16004), Korea National Rehabilitation Center, Ministry of Health & Welfare, Korea.


  1. [1].
    The Internet Stroke Center, Stroke Statistics, [2018-03],
  2. [2].
    Go A S, Mozaffarian D, Roger V L, Benjamin E J, Berry J D, Blaha M J, Fullerton H J. Heart disease and stroke statistics— 2014 update: A report from the American Heart Association. Circulation, 2014, 129, e28–e292.CrossRefGoogle Scholar
  3. [3].
    O'Driscoll S W, Giori N J. Continuous passive motion (CPM): Theory and principles of clinical application. Journal of Rehabilitation Research and Development, 2000, 37, 179.Google Scholar
  4. [4].
    Pand A D, Johnson G R, Price C I M, Curless R H, Barnes M P, Rodgers H. A review of the properties and limitations of the Ashworth and modified Ashworth Scales as measures of spasticity. Clinical Rehabilitation, 1999, 13, 373–383.CrossRefGoogle Scholar
  5. [5].
    Mehrholz J, Wagner K, Meissner D, Grundmann K, Zange C, Koch R, Pohl M. Reliability of the modified tardieu scale and the modified ashworth scale in adult patients with severe brain injury: A comparison study. Clinical Rehabilitation, 2005, 19, 751–759.CrossRefGoogle Scholar
  6. [6].
    Zatsiorsky V M, Li Z M, Latash M L. Enslaving effects in multi-finger force production. Experimental Brain Research, 2000, 131, 187–195.CrossRefGoogle Scholar
  7. [7].
    Campolo D, Widjaja F, Xu H, Ang W T, Burdet E. Analysis of accuracy in pointing with redundant hand-held tools: A geometric approach to the uncontrolled manifold method. PLOS Computational Biology, 2013, 9, e1002978.MathSciNetCrossRefGoogle Scholar
  8. [8].
    Verrel J. Distributional properties and variance-stabilizing transformations for measures of uncontrolled manifold effects. Journal of Neuroscience Methods, 2010, 191, 166–170.CrossRefGoogle Scholar
  9. [9].
    Latash M L, Anson J G. Synergies in health and disease: Relations to adaptive changes in motor coordination. Physical Therapy, 2006, 86, 1151–1160.Google Scholar
  10. [10].
    Scholz J P, Kang N, Patterson D, Latash M L. Uncontrolled manifold analysis of single trials during multi-finger force production by persons with and without Down syndrome. Experimental Brain Research, 2003, 153, 45–58.CrossRefGoogle Scholar
  11. [11].
    Li Z M, Davis G, Gustafson N P, Goitz R J. A robot-assisted study of intrinsic muscle regulation on proximal interphalangeal joint stiffness by varying metacarpophalangeal joint position. Journal of Orthopaedic Research, 2006, 24, 407–415.CrossRefGoogle Scholar
  12. [12].
    Littler J W. The finger extensor mechanism. Surgical Clinics of North America, 1967, 47, 415–432.CrossRefGoogle Scholar
  13. [13].
    Esteki A, Mansour J M. An experimentally based nonlinear viscoelastic model of joint passive moment. Journal of Biomechanics, 1996, 29, 443–450.CrossRefGoogle Scholar
  14. [14].
    Dionysian E, Kabo J M, Dorey F J, Meals R A. Proximal interphalangeal joint stiffness: Measurement and analysis. Journal of Hand Surgery, 2005, 30, 573–579.CrossRefGoogle Scholar
  15. [15].
    Wright V, Johns R J. Quantitative and qualitative analysis of joint stiffness in normal subjects and in patients with connective tissue diseases. Annals of the Rheumatic Diseases, 1961, 20, 36.CrossRefGoogle Scholar
  16. [16].
    Troncossi M, Mozaffari-Foumashi M, Parenti-Castelli V. An original classification of rehabilitation hand exoskeletons. Journal of Robotics and Mechanical Engineering Research, 2016, 1, 17–29.CrossRefGoogle Scholar
  17. [17].
    Polygerinos P, Wang Z, Galloway K C, Wood R J, Walsh C J. Soft robotic glove for combined assistance and at-home rehabilitation. Robotics and Autonomous Systems, 2015, 73, 135–143.CrossRefGoogle Scholar
  18. [18].
    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. Medical & Biological Engineering & Computing, 2018, 56, 261–272.CrossRefGoogle Scholar
  19. [19].
    Conti R, Meli E, Ridolfi A, Bianchi M, Governi L, Volpe Y, Allotta B. Kinematic synthesis and testing of a new portable hand exoskeleton. Meccanica, 2017, 52, 2873–2897.MathSciNetCrossRefGoogle Scholar
  20. [20].
    Randazzo L, Iturrate I, Perdikis S, Millán J D R. Mano: A wearable hand exoskeleton for activities of daily living and neurorehabilitation. IEEE Robotics and Automation Letters, 2018, 3, 500–507.CrossRefGoogle Scholar
  21. [21].
    Cempini M, Cortese M, Vitiello N. A Powered finger-thumb wearable hand exoskeleton with self-aligning joint axes. IEEE/ASME Transactions on Mechatronics, 2015, 20, 705–716.CrossRefGoogle Scholar
  22. [22].
    Ho N S K, Tong K Y, Hu X L, Fung K L, Wei X J, Rong W, Susanto E A. An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: Task training system for stroke rehabilitation. Proceedings of the 12th IEEE International Conference on Rehabilitation Robotics (ICORR), Zurich, Switzerland, 2011, 1–5.Google Scholar
  23. [23].
    Kim S, Lee J, Bae J. Analysis of finger muscular forces using a wearable hand exoskeleton system. Journal of Bionic Engineering, 2017, 14, 680–691.CrossRefGoogle Scholar
  24. [24].
  25. [25].
    Boian R, Sharma A, Han C, Merians A, Burdea G, Adamovich S, Recce M, Tremaine M, Poizner H. Virtual realitybased post-stroke hand rehabilitation. Proceedings of Medicine Meets Virtual Reality Conference, Newport Beach, USA, 2002, 64–70.Google Scholar
  26. [26].
    Raghavan P, Petra E, Krakauer J W, Gordon A M. Patterns of impairment in digit independence after subcortical stroke. Journal of Neurophysiology, 2006, 95, 369–378.CrossRefGoogle Scholar
  27. [27].
    Lang C E, Schieber M H. Reduced muscle selectivity during individuated finger movements in humans after damage to the motor cortex or corticospinal tract. Journal of Neurophysiology, 2004, 91, 1722–1733.CrossRefGoogle Scholar
  28. [28].
    Suarez-Escobar M, Gallego-Sanchez J A, Rendon-Velez E. Mechanisms for linkage-driven underactuated hand exoskeletons: Conceptual design including anatomical and mechanical specifications. International Journal on Interactive Design and Manufacturing (IJIDeM), 2017, 11, 55–75.CrossRefGoogle Scholar
  29. [29].
    Rijpkema H, Girard M. Computer animation of knowledgebased human grasping. ACM Siggraph Computer Graphics, 1991, 25, 339–348.CrossRefGoogle Scholar
  30. [30].
    Sizekorea. Korean Normal Anthropometric Data, [2015-03],
  31. [31].
    Maxon Motor, [2018-03],
  32. [32].
    Kim S, Bae J. Force-mode control of rotary series elastic actuators in a lower extremity exoskeleton using model- inverse time delay control. IEEE/ASME Transactions on Mechatronics, 2017, 22, 1392–1400.CrossRefGoogle Scholar
  33. [33].
    National Instrument, [2018-03],
  34. [34].
    OptiTrack, Prime 13, [2018-03],
  35. [35].
    Kang N, Shinohara M, Zatsiorsky V M, Latash M L. Learning multi-finger synergies: An uncontrolled manifold analysis. Experimental Brain Research, 2004, 157, 336–350.CrossRefGoogle Scholar
  36. [36].
    Shim J K, Hsu J, Karol S, Hurley B F. Strength training increases training-specific multifinger coordination in humans. Motor Control, 2008, 12, 311–329.CrossRefGoogle Scholar
  37. [37].
    Olafsdottir H, Zatsiorsky V M, Latash M L. Is the thumb a fifth finger? A study of digit interaction during force production tasks. Experimental Brain Research, 2005, 160, 203–213.CrossRefGoogle Scholar

Copyright information

© Jilin University 2018

Authors and Affiliations

  1. 1.Bio-Robotics and Control (BiRC) Laboratory, Department of Mechanical EngineeringUNISTUlsanRepublic of Korea

Personalised recommendations