Time-based Simplified Denavit-Heartenberg Translation (TS-DH) for Capturing Finger Kinematic Data

  • Authors

    • Mohd Amir Idzham Iberahim
    • Syadiah Nor Wan Shamsuddin
    • Mokhairi Makhtar
    • Mohd Nordin Abdul Rahman
    • Nordin Simbak
    2018-08-17
    https://doi.org/10.14419/ijet.v7i3.28.20958
  • Denavit-Heartenberg (DH) Parameter, Virtual Fine Motor, Forward Kinematic, Stroke Rehabilitation.

  • Abstract

    Virtual finger model is commonly used in many applications for stroke fine motor rehabilitation especially in Virtual Reality (VR) applications. Capturing movement data for fingers is one of the important phases in any virtual fine motor rehabilitation process. Manual observation provides inconsistent evaluation given by different therapists for different rehabilitation sessions. Although the process of capturing data is performed, without time series of captured data, the data will not have a significant impact in producing consistent and meaningful evaluation. Furthermore, the consistency of the assessment of rehabilitation sessions will deliver more robust rehabilitation progress analysis. This data is very useful when paired with time information which can be analyzed to produce optimal evaluation. This paper proposes Time-based Simplified Denavit-Heartenberg Translation (TS-DH) consisting of forward kinematic with simplified DH parameter for capturing coordinate of end of each bone from virtual finger model paired with timeframe data. The DH model is enhanced by implementing 2 additional rules in assigning joint parameter. The data will be recorded with timeframe of every finger movement. As a conclusion, TS-DH model can be used in any virtual finger environment accurately.

     

     

  • References

    1. [1] E. Carmeli, S. Peleg, G. Bartur, E. Elbo, and J.-J. Vatine, “HandTutorTM enhanced hand rehabilitation after stroke - a pilot study,†Physiother. Res. Int., vol. 16, no. 4, pp. 191–200, Dec. 2011.

      [2] A. Scano et al., “Kinect One-based biomechanical assessment of upper-limb performance compared to clinical scales in post-stroke patients,†pp. 5720–5723, 2015.

      [3] D. Leonardis et al., “An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation,†IEEE Trans. Haptics, vol. 8, no. 2, pp. 140–151, Apr. 2015.

      [4] S. Guo, F. Zhang, W. Wei, F. Zhao, and Y. Wang, “Kinematic analysis of a novel exoskeleton finger rehabilitation robot for stroke patients,†in 2014 IEEE International Conference on Mechatronics and Automation, 2014, pp. 924–929.

      [5] D. Zhang, Y. Geng, X. Zhang, Y. Zhang, and G. Li, “Real-time performance of hand motion recognition using kinematic signals for impaired hand function training,†Neural Eng. (NER), 2013 6th Int. IEEE/EMBS Conf., pp. 339–342, 2013.

      [6] X. Huo, Y. Liu, L. Jiang, and H. Liu, “Optimal kinematic control of humanoid arms with offset wrist,†in 2014 IEEE International Conference on Mechatronics and Automation, 2014, pp. 1011–1016.

      [7] E. Peña-Pitarch, N. T. Falguera, and J. J. Yang, “Virtual human hand: Model and kinematics,†Comput. Methods Biomech. Biomed. Engin., vol. 17, no. 5, pp. 568–579, Apr. 2014.

      [8] S. Guo, W. Zhang, W. Wei, J. Guo, Y. Ji, and Y. Wang, “A kinematic model of an upper limb rehabilitation robot system,†2013 IEEE Int. Conf. Mechatronics Autom., pp. 968–973, 2013.

      [9] J. M. Ibarra Zannatha, A. J. M. Tamayo, Ã. D. G. Sánchez, J. E. L. Delgado, L. E. R. Cheu, and W. A. S. Arévalo, “Development of a system based on 3D vision, interactive virtual environments, ergonometric signals and a humanoid for stroke rehabilitation,†Comput. Methods Programs Biomed., vol. 112, no. 2, pp. 239–249, Nov. 2013.

      [10] G. Du and P. Zhang, “A Markerless Human-Robot Interface Using Particle Filter and Kalman Filter for Dual Robots,†IEEE Trans. Ind. Electron., vol. 62, no. 4, pp. 2257–2264, Apr. 2015.

      [11] P. Prashun, G. Hadley, C. Gatzidis, and I. Swain, “Investigating the Trend of Virtual Reality-Based Stroke Rehabilitation Systems,†in 2010 14th International Conference Information Visualisation, 2010, pp. 641–647.

      [12] J. W. Burke, P. J. Morrow, M. D. J. McNeill, S. M. McDonough, and D. K. Charles, “Vision based games for upper-limb stroke rehabilitation,†Proc. - IMVIP 2008, 2008 Int. Mach. Vis. Image Process. Conf., pp. 159–164, 2008.

      [13] J. Island, G. Energy, and G. Energy, “Combined Kinematic Analysis of a Hybrid 7-DOF Rehabilitation,†pp. 1752–1755, 2012.

      [14] Y. H. Choi, J. Ku, H. Lim, Y. H. Kim, and N. J. Paik, “Mobile game-based virtual reality rehabilitation program for upper limb dysfunction after ischemic stroke,†Restor. Neurol. Neurosci., vol. 34, no. 3, pp. 455–463, 2016.

      [15] K. H. Kong et al., “Efficacy of a virtual reality commercial gaming device in upper limb recovery after stroke: A randomized, controlled study,†Top. Stroke Rehabil., vol. 23, no. 5, pp. 333–340, 2016.

      [16] D. Kairy et al., “Maximizing post-stroke upper limb rehabilitation using a novel telerehabilitation interactive virtual reality system in the patient’s home: Study protocol of a randomized clinical trial,†Contemp. Clin. Trials, vol. 47, pp. 49–53, 2016.

      [17] J. Kim, M. Lee, Y. Kim, S.-D. Eun, and B. Yoon, “Feasibility of an individually tailored virtual reality program for improving upper motor functions and activities of daily living in chronic stroke survivors: A case series,†Eur. J. Integr. Med., vol. 8, no. 5, pp. 731–737, 2016.

      [18] T. D. C. Little and A. Ghafoor, “Interval-based conceptual models for time-dependent multimedia data,†IEEE Trans. Knowl. Data Eng., vol. 5, no. 4, pp. 551–563, 1993.

      [19] A. Rind, T. Lammarsch, W. Aigner, B. Alsallakh, and S. Miksch, “TimeBench: A data model and software library for visual analytics of time-oriented data,†IEEE Trans. Vis. Comput. Graph., vol. 19, no. 12, pp. 2247–2256, 2013.

  • Downloads

  • How to Cite

    Amir Idzham Iberahim, M., Nor Wan Shamsuddin, S., Makhtar, M., Nordin Abdul Rahman, M., & Simbak, N. (2018). Time-based Simplified Denavit-Heartenberg Translation (TS-DH) for Capturing Finger Kinematic Data. International Journal of Engineering & Technology, 7(3.28), 20-23. https://doi.org/10.14419/ijet.v7i3.28.20958

    Received date: 2018-10-04

    Accepted date: 2018-10-04

    Published date: 2018-08-17