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Measurement of Arm and Hand Motion in Performing Activities of Daily Living (ADL) of Healthy and Post-Stroke Subjects—Preliminary Results

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 293)

Abstract

This paper presents an ADL measurement system composed of a measurement table, upper limb measurement system that uses IMU sensors, hand measurement system that uses OLE-based sensor (SmartGlove), and a software system that integrates the sensors and record motion data. Some preliminary data are shown, comparing the motion of a healthy subject to a post-stroke patient’s motion.

Keywords

Activities of daily living ADL Arm motion Hand motion IMU 

References

  1. 1.
    Sager, M. A., Dunham, N. C., Schwantes, A., Mecum, L., Halverson, K., & Harlowe, D. (1992). Measurement of activities of daily living in hospitalized elderly: a comparison of self-report and performance-based methods. Journal of the American Geriatrics Society, 40(5), 457–462.Google Scholar
  2. 2.
    Chen, S. Y., & Winstein, C. J. (2009). A systematic review of voluntary arm recovery in hemiparetic stroke: critical predictors for meaningful outcomes using the international classification of functioning, disability, and health. Journal of Neurolic Physical Therapy, 33(1), 2–13.CrossRefGoogle Scholar
  3. 3.
    Hsueh, I. P., Chen, J. H., Wang, C. H., Hou W. H., & Hsieh, C. l. (2013). Development of a computerized adaptive test for assessing activities of daily living in outpatients with stroke. Journal of Physical Therapy, 93(5), 681–693, doi:  10.2522/?ptj.20120173 Google Scholar
  4. 4.
    Hong, X. (2008). HomeADL for adaptive ADL monitoring within smart homes. Proceedings of 30th Annual International Conference of IEEE Engineering in Medicine and Biology Society, August 2008, pp. 3324–3327.Google Scholar
  5. 5.
    Bang, S. L. Toward real time detection of the basic living activity in home using a wearable sensor and smart home sensors. Proceedings of 30th Annual International Conference of IEEE Engineering in Medicine and Biology Society, (EMBS 2008), August 2008, pp. 5200–5203.Google Scholar
  6. 6.
    Tanaka, E. (2012). Gait and ADL rehabilitation using a whole body motion support type mobile suit evaluated by cerebral activity. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), October 2012, pp. 3286–3291.Google Scholar
  7. 7.
    Sugar, T.G., Jiping, H., Koeneman, E. J., Koeneman, J. B., Herman, R., Huang, H. et al. (2007), Design and control of RUPERT: A device for robotic upper extremity repetitive therapy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(3), 336–346.Google Scholar
  8. 8.
    Li, K., Chen, I. M., & Yeo S. H. (2010). Design and validation of a multi-finger sensing device based on optical linear encoder. IEEE International Conference on Robotics and Automation, pp. 3629–3634.Google Scholar
  9. 9.
    Ding, Z. Q., Luo, Z. Q., Causo, A., Chen, I. M., Yue, K. X., Yeo, S. H., et al. (2011). Inertia sensor-based guidance system for upperlimb posture correction. Journal of Medical Engineering and Physics, 35(2), 269–276.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Robotics Research CentreSchool of Mechanical and Aerospace Engineering, Nanyang Technological UniversitySingaporeSingapore

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