1(Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering,Yanshan University, Qinhuangdao 066004, Hebei, China) 2(Qinhuangdao Vocational and Technical College, Qinhuangdao 066100, Hebei, China)
Abstract：The human body's natural rhythmic movement is the result of the synergistic and orderly rotation of the joints throughout the body. The coupled timing and dynamic rotational characteristics of the joints embody the synergistic relationship between the limbs in the body's rhythmic movement. In this study, we organized twenty young healthy subjects (10 men and 10 women, 20~26 years old) to perform walking and rope skipping experiments to collect data on the angles of the major joints during exercise, and we introduced the adaptive Hopf oscillator parameter recognition model and combined with the joint synergistic phase distribution to study the problem of portraying the synergistic characteristics of human rhythmic movements. First, by setting up the joint phase reference points, we calculated the coupled rotation timing between human joints. Next, we established a joint unit parameter identification model based on the adaptive Hopf oscillator to obtain the characterizing parameters of the complex joint dynamic rotation patterns. Finally, we used the phase coupling characteristic of central pattern generator network to reconstruct the complete human rhythmic synergetic motion and quantitatively analyzed the accuracy of the reconstruction results. The results showed that the human body’s rhythmic motion posture based on the restoration of the characterization parameters was normal, the joint reconstruction trajectory was highly consistent with the actual data, the correlation coefficient between the two was higher than 0.99, the maximum average error was less than 0.01 rad, and the maximum error was less than 0.03 rad, the absolute threshold deviation was less than 4%. Therefore, the joint rotation timing calculation criteria and the adaptive joint unit parameter identification method proposed in this paper can be used to accurately describe the joint coupling characteristics and synergy laws in a human rhythmic motion.
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