Advertisement

Muscle Synergistic Pattern and Kinematic Sensor Data Analysis During Upper-Limb Reaching in Stroke Patients

  • Bingyu Pan
  • Yingfei Sun
  • Zhipei HuangEmail author
  • Jiateng Hou
  • Jiankang Wu
  • Zhen Huang
  • Bin Xie
  • Yijun Liu
Conference paper
Part of the Internet of Things book series (ITTCC)

Abstract

Quantitative and efficient measurement of motor impairment level is of vital importance in stroke rehabilitation. This paper investigates the muscle synergistic patterns and kinematic sensor data of upper limb reaching in stroke patients with different impairment level. Thirty-three stroke patients and nineteen healthy age-matched subjects serving as the control group were asked to do voluntary upward reaching. Inertial sensors and surface electromyography (sEMG) sensors were attached to subjects’ upper limb to obtain the real-time joint angle through segment position by the inertial sensory data fusion and extract synergistic patterns from sEMG data by applying principal components analysis at the same time. The experimental results show that stroke patients not only have abnormal range of shoulder joint motion, which was correlated with the degree of clinical impairment level; but also have different muscle synergistic patterns at different impairment level, which can be used as a quantitative measurement of functional recovery status.

Keywords

Internal sensors Muscle synergistic pattern Surface electromyography Principal component analysis Stroke rehabilitation 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China, Grant No. 61431017 and 81272166.

References

  1. 1.
    Johansson, B.B.: Brain plasticity and stroke rehabilitation the Willis lecture. Stroke 31, 223–230 (2000)CrossRefGoogle Scholar
  2. 2.
    Kang, N., Idica, J., Amitoj, B., Cauraugh, J.H.: Motor recovery patterns in arm muscles: coupled bilateral training and neuromuscular stimulation. J. Neuroeng. Rehabil. 11, 57 (2014)CrossRefGoogle Scholar
  3. 3.
    Fugl-Meyer, A.R., Jääskö, L., Leyman, I., Olsson, S., Steglind, S.: The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand. J. Rehabil. Med. 7, 13–31 (1974)Google Scholar
  4. 4.
    Bohannon, R.W., Smith, M.B.: Interrater reliability of a modified Ashworth scale of muscle spasticity. Phys. Ther. 67, 206–207 (1987)CrossRefGoogle Scholar
  5. 5.
    Del Din, S., Patel, S., Cobelli, C., Bonato, P.: Estimating Fugl-Meyer clinical scores in stroke survivors using wearable sensors. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5839–5842. IEEE (2011)Google Scholar
  6. 6.
    Bai, L., Pepper, M.G., Yan, Y., Spurgeon, S.K., Sakel, M., Phillips, M.: Quantitative assessment of upper limb motion in neurorehabilitation utilizing inertial sensors. IEEE Trans. Neural Syst. Rehabil. Eng. 23, 232–243 (2015)CrossRefGoogle Scholar
  7. 7.
    Safavynia, S.A., Torres-Oviedo, G., Ting, L.H.: Muscle synergies: implications for clinical evaluation and rehabilitation of movement. Top. Spin. Cord Inj. Rehabil. 17, 16–24 (2011)CrossRefGoogle Scholar
  8. 8.
    Barker, R.N., Brauer, S., Carson, R.: Training-induced changes in the pattern of triceps to biceps activation during reaching tasks after chronic and severe stroke. Exp. Brain Res. 196, 483–496 (2009)CrossRefGoogle Scholar
  9. 9.
    Hughes, A.M., Freeman, C.T., Burridge, J.H., Chappell, P.H., Lewin, P.L., Rogers, E.: Shoulder and elbow muscle activity during fully supported trajectory tracking in people who have had a stroke. J. Electromyogr. Kinesiol. 20, 465–476 (2010)CrossRefGoogle Scholar
  10. 10.
    Cruz, E., Waldinger, H., Kamper, D.: Kinetic and kinematic workspaces of the index finger following stroke. Brain 128, 1112–1121 (2005)CrossRefGoogle Scholar
  11. 11.
    Musampa, N.K., Mathieu, P.A., Levin, M.F.: Relationship between stretch reflex thresholds and voluntary arm muscle activation in patients with spasticity. Exp. Brain Res. 181, 579–593 (2007)CrossRefGoogle Scholar
  12. 12.
    Roh, J., Rymer, W.Z., Perreault, E.J., Yoo, S.B., Beer, R.F.: Alterations in upper limb muscle synergy structure in chronic stroke survivors. J. Neurophysiol. 109, 768–781 (2013)CrossRefGoogle Scholar
  13. 13.
    Roh, J., Rymer, W.Z., Beer, R.F.: Evidence for altered upper extremity muscle synergies in chronic stroke survivors with mild and moderate impairment. Front. Hum. Neurosci. 9 (2015)Google Scholar
  14. 14.
    Berger, D.J., Ferrari, F., Esposito, A., Masciullo, M., Molinari, M., Lacquaniti, F., d’Avella, A.: Changes in muscle synergy organization after neurological lesions. In: Ibáñez, J., González-Vargas, J., Azorín, J.M., Akay, M., Pons, J.L. (eds.) Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016), 18–21 October 2016, Segovia, Spain, pp. 939–943. Springer International Publishing, Cham (2017)Google Scholar
  15. 15.
    Krishnamoorthy, V., Latash, M.L., Scholz, J.P., Zatsiorsky, V.M.: Muscle synergies during shifts of the center of pressure by standing persons. Exp. Brain Res. 152, 281–292 (2003)CrossRefGoogle Scholar
  16. 16.
    Suzuki, K., Nishida, Y., Mitsutomi, K.: Association between muscle synergy and stability during prolonged walking. J. Phys. Ther. Sci. 26, 1637 (2014)CrossRefGoogle Scholar
  17. 17.
    Huang, S., Luo, C., Ye, S., Liu, F., Xie, B., Wang, C., Yang, L., Huang, Z., Wu, J.: Motor impairment evaluation for upper limb in stroke patients on the basis of a microsensor. Int. J. Rehabil. Res. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation 35, 161–169 (2012)CrossRefGoogle Scholar
  18. 18.
    Hermens, H.J., Freriks, B., Merletti, R., Stegeman, D., Blok, J., Rau, G., Disselhorst-Klug, C., Hägg, G.: European recommendations for surface electromyography. Roessingh Res. Dev. 8, 13–54 (1999)Google Scholar
  19. 19.
    Burden, A.: How should we normalize electromyograms obtained from healthy participants? What we have learned from over 25 years of research. J. Electromyogr. Kinesiol. Offic. J. Int. Soc. Electrophysiol. Kinesiol. 20, 1023–1035 (2010)CrossRefGoogle Scholar
  20. 20.
    Perez, M.A., Nussbaum, M.A.: Principal components analysis as an evaluation and classification tool for lower torso sEMG data. J. Biomech. 36, 1225–1229 (2003)CrossRefGoogle Scholar
  21. 21.
    Patil, A.M., Kolhe, S.R., Patil, P.M.: Face recognition by PCA technique. In: 2009 2nd International Conference on Emerging Trends in Engineering and Technology (ICETET), pp. 192–195 (2009)Google Scholar
  22. 22.
    McCrea, P.H., Eng, J.J., Hodgson, A.J.: Saturated muscle activation contributes to compensatory reaching strategies after stroke. J. Neurophysiol. 94, 2999–3008 (2005)CrossRefGoogle Scholar
  23. 23.
    Levin, M.F., Michaelsen, S.M., Cirstea, C.M., Roby-Brami, A.: Use of the trunk for reaching targets placed within and beyond the reach in adult hemiparesis. Exp. Brain Res. 143, 171–180 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bingyu Pan
    • 1
  • Yingfei Sun
    • 1
  • Zhipei Huang
    • 1
    Email author
  • Jiateng Hou
    • 1
  • Jiankang Wu
    • 1
  • Zhen Huang
    • 2
  • Bin Xie
    • 2
  • Yijun Liu
    • 2
  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.Rehabilitation DepartmentPeking University First HospitalBeijingChina

Personalised recommendations