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)


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.


Internal 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.


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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

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