Dynamic Functional Assessment of Hand Motion Using an Animation Glove: The Effect of Stenosing Tenosynovitis

moCap for Orthopedic DiagnosisFunctional assessment of hand motion is important in surgical patients, as well as those seeking evaluation in the work place. We hypothesized that baseline function in simple tasks could be captured using an animation glove, and would reproducibly reveal parameters relevant to hand function. Methods Ten subjects were assessed on tasks involving hand function at varied speeds. Tasks included forceful making of a fist, gradual making of a fist, quickly grasping a baseball and slowly grasping the baseball. Range of motion (ROM) data was recorded using the Cyberglove II (Cyberglove Systems LLC, San Jose, CA). Results Subjects completed tasks efficiently using either hand. Peak extension velocity of the index finger MCP joint was 989.82 deg/sec, with PIP joint peak velocity 953.06 deg/sec and DIP joint peak velocity 574.21 deg/sec. Middle finger velocities followed a similar trend as in the index finger. In the ring finger, however, the more distal joints reached higher peak velocities relative to the MCP joint. Peak velocity occurred over 71% of the flexion arc and 46% of the extension arc. Certain baseball grasp trials revealed a response to the initiation of contact, in which deceleration occurred prior to fully grasping the ball.  Conclusions Basic functional tasks of the hand can be dynamically assessed using motion capture gloves. In the index and middle fingers, the more proximal MCP joint reached higher velocities than the DIP joint. The converse was true of the ring finger. Peak flexion and extension velocity occurs over a range. A “somatosensory response” is seen with object grasp, characterized as deceleration over a narrow ROM prior to full grasp.  Dynamic functional assessment provides knowledge of the patient’s ability to use the limb in space. A glove equipped with motion capture technology offers a means of quantitative measurement of active hand/finger motion.
Collaborators
  • Michael Schreck, University of Rochester Medical Center
  • Meghan Kelly, University of Rochester Medical Center
  • Sarah Lander, University of Rochester Medical Center
  • Anjan Kaushik, University of Rochester Medical Center
  • Heather Smith, University of Rochester Medical Center
  • Scott Bell, University of Rochester Medical Center
  • Vishwanath Raman, Department of Computer Science, Rochester Institute of Technology
  • Mark Ollies, College of Applied Science and Technology, Rochester Institute of Technology
  • John Elfar, University of Rochester Medical Center
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    Publications
  • Schreck, M. J., M. Kelly, S. Lander, A. Kaushik, H. Smith, S. Bell, V. Raman, D. Olles, J. Geigel, M. Olles, and J. C. Elfar (2017). Dynamic Functional Assessment of Hand Motion Using an Animation Glove: The Effect of Stenosing Tenosynovitis. HAND. PMID: 28984481, 1558944717729218. eprint: https://doi.org/10.1177/1558944717729218.
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