Measurement of 3-D Workspace of Thumb Tip with RGB-D Sensor for Quantitative Rehabilitation

  • Tadashi Matsuo
  • Nobutaka Shimada
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 45)


The three dimensional workspace of the thumb tip, where a thumb tip can reach, is closely related to functions that can be performed by the thumb. However, on the hand and finger rehabilitation, one dimensional range of motion for each joint is manually measured for evaluating the current state of the hand. It requires a therapist to measure the ranges. In addition, it is difficult to evaluate the three dimensional workspace from the one dimensional ranges. We propose a method to automatically estimate three dimensional position of the thumb tip with a contactless depth sensor. To evaluate the relative position to the palm, we also propose a method to estimate three dimensional configuration of the palm. With experiments, we show the effectiveness of the proposed method.


Depth Image Convolutional Neural Network Local Shape Standard Position Candidate Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by JSPS KAKENHI Grant Number 24500224, 15H02764 and MEXT-Supported Program for the Strategic Research Foundation at Private Universities, S1311039, 2013–2016.


  1. 1.
    Breiman, L.: Technical note: Some properties of splitting criteria. Mach. Learn. 24(1), 41–47 (1996).
  2. 2.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001).
  3. 3.
    Health, J.L., Organization, W.: External injury: hand rehabilitation., Accessed 29 May 2015
  4. 4.
    Kuo, L.C., Chiu, H.Y., Chang, C.W., Hsu, H.Y., Sun, Y.N.: Functional workspace for precision manipulation between thumb and fingers in normal hands. J. Electromyogr. Kinesiol. 19(5), 829–839 (2009).
  5. 5.
    Leap Motion, I.: Leap motion controller. Accessed 29 May 2015
  6. 6.
    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)Google Scholar
  7. 7.
    Marzke, M.W.: Precision grips, hand morphology, and tools. Am. J. Phys. Anthr. 102(1), 91–110 (1997)CrossRefGoogle Scholar
  8. 8.
    Microsoft: Kinect for windows. Accessed 29 May 2015
  9. 9.
    Yonemoto, K., Ishigami, S., Toru, K.: Display and measurement of movable range of joints. The Japanese Journal of Rehabilitation Medicine 32(4), 207–217 (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Ritsumeikan UniversityShigaJapan

Personalised recommendations