IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v15y2019i4p1550147719845548.html
   My bibliography  Save this article

Wrist joint proprioceptive acuity assessment using inertial and magnetic measurement systems

Author

Listed:
  • Lin Li
  • ShuWang Li
  • YanXia Li

Abstract

Human wrist proprioception is particularly important due to its role in manual dexterity and associated tasks of daily living. Most studies have focused on testing single degree of freedom joints or were only capable of displacing or moving a joint in a single plane, such as flexion/extension of the wrist. The purpose of this study was to examine the effects of both direction and angular level on the accuracy of human wrist position reproduction error. Sixty subjects (all males) without a history of wrist pathology were recruited from a university campus. Subjects performed a position reproduction task in eight directions at three angular levels. The results showed that wrist position reproduction error depends on direction and angular level. Comparable reliability for the intra-observer measurements for wrist range of motion and joint position sense. The orientation of the joint position sense production ellipse is similar to the orientation of the range of motion ellipse, indicating that subjects generated the most accurate in directions where it is easy to generate more range of motion and the lowest accurate in directions where it is not easy to generate more range of motion. Joint position sense decreased in accuracy as the joint angle increased. The position reproduction error depends on the angular level, and subjects overshoot the target angle for the low angular levels (25% range of motion) and undershoot the target angle for high angular levels (75% range of motion). Mapping the human wrist joint position sense ellipse contributes to our understanding of the comprehensive proprioceptive function of the wrist. This technique offers the opportunity to assess all of the directions of proprioceptive function, which in turn may aid in improving therapeutic approaches.

Suggested Citation

  • Lin Li & ShuWang Li & YanXia Li, 2019. "Wrist joint proprioceptive acuity assessment using inertial and magnetic measurement systems," International Journal of Distributed Sensor Networks, , vol. 15(4), pages 15501477198, April.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:4:p:1550147719845548
    DOI: 10.1177/1550147719845548
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147719845548
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147719845548?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Christopher M. Harris & Daniel M. Wolpert, 1998. "Signal-dependent noise determines motor planning," Nature, Nature, vol. 394(6695), pages 780-784, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shogo Yonekura & Yasuo Kuniyoshi, 2017. "Bodily motion fluctuation improves reaching success rate in a neurophysical agent via geometric-stochastic resonance," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-16, December.
    2. Shih-Wei Wu & Maria F Dal Martello & Laurence T Maloney, 2009. "Sub-Optimal Allocation of Time in Sequential Movements," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-13, December.
    3. Max Berniker & Megan K O’Brien & Konrad P Kording & Alaa A Ahmed, 2013. "An Examination of the Generalizability of Motor Costs," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-11, January.
    4. Lionel Rigoux & Emmanuel Guigon, 2012. "A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-13, October.
    5. Yanhao Ren & Qiang Luo & Wenlian Lu, 2023. "Synchronization Analysis of Linearly Coupled Systems with Signal-Dependent Noises," Mathematics, MDPI, vol. 11(10), pages 1-15, May.
    6. Seth W. Egger & Stephen G. Lisberger, 2022. "Neural structure of a sensory decoder for motor control," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    7. Ashesh Vasalya & Gowrishankar Ganesh & Abderrahmane Kheddar, 2018. "More than just co-workers: Presence of humanoid robot co-worker influences human performance," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-19, November.
    8. Josh Merel & Donald M Pianto & John P Cunningham & Liam Paninski, 2015. "Encoder-Decoder Optimization for Brain-Computer Interfaces," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-25, June.
    9. Wei Zhang & Sasha Reschechtko & Barry Hahn & Cynthia Benson & Elias Youssef, 2019. "Force-stabilizing synergies can be retained by coordinating sensory-blocked and sensory-intact digits," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-17, December.
    10. Julian J Tramper & Bart van den Broek & Wim Wiegerinck & Hilbert J Kappen & Stan Gielen, 2012. "Time-Integrated Position Error Accounts for Sensorimotor Behavior in Time-Constrained Tasks," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-10, March.
    11. Konrad P Körding & Izumi Fukunaga & Ian S Howard & James N Ingram & Daniel M Wolpert, 2004. "A Neuroeconomics Approach to Inferring Utility Functions in Sensorimotor Control," PLOS Biology, Public Library of Science, vol. 2(10), pages 1-1, September.
    12. Vassilios N Christopoulos & Paul R Schrater, 2009. "Grasping Objects with Environmentally Induced Position Uncertainty," PLOS Computational Biology, Public Library of Science, vol. 5(10), pages 1-11, October.
    13. Bastien Berret & Frédéric Jean, 2020. "Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-28, February.
    14. H H L M Goossens & A J van Opstal, 2012. "Optimal Control of Saccades by Spatial-Temporal Activity Patterns in the Monkey Superior Colliculus," PLOS Computational Biology, Public Library of Science, vol. 8(5), pages 1-18, May.
    15. Michael Sherback & Francisco J Valero-Cuevas & Raffaello D'Andrea, 2010. "Slower Visuomotor Corrections with Unchanged Latency are Consistent with Optimal Adaptation to Increased Endogenous Noise in the Elderly," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-13, March.
    16. Bastien Berret & Adrien Conessa & Nicolas Schweighofer & Etienne Burdet, 2021. "Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-24, June.
    17. Nadav S Bar & Sigurd Skogestad & Jose M Marçal & Nachum Ulanovsky & Yossi Yovel, 2015. "A Sensory-Motor Control Model of Animal Flight Explains Why Bats Fly Differently in Light Versus Dark," PLOS Biology, Public Library of Science, vol. 13(1), pages 1-18, January.
    18. Paolo Tommasino & Antonella Maselli & Domenico Campolo & Francesco Lacquaniti & Andrea d’Avella, 2021. "A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-32, June.
    19. Joshua G A Cashaback & Heather R McGregor & Ayman Mohatarem & Paul L Gribble, 2017. "Dissociating error-based and reinforcement-based loss functions during sensorimotor learning," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-28, July.
    20. Jonathan B Dingwell & Joby John & Joseph P Cusumano, 2010. "Do Humans Optimally Exploit Redundancy to Control Step Variability in Walking?," PLOS Computational Biology, Public Library of Science, vol. 6(7), pages 1-15, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:15:y:2019:i:4:p:1550147719845548. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.