Muscle Synergistic Pattern and Kinematic Sensor Data Analysis During Upper-Limb Reaching in Stroke Patients
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.
KeywordsInternal sensors Muscle synergistic pattern Surface electromyography Principal component analysis Stroke rehabilitation
This work was supported by National Natural Science Foundation of China, Grant No. 61431017 and 81272166.
- 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
- 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
- 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.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
- 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.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
- 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