With the progress of social science and technology and the change of people's concept of life, various research fields have gradually influenced each other. Medical image analysis of EMG characteristics is a very powerful research method commonly used in many research fields, including many sports, such as Taekwondo. The research of this paper is based on the medical image analysis of the muscle EMG characteristics of the leg control technique, using the EMG instrument to collect the muscle state and strength required by the action, to further analyze and study the leg control technique. In the experiment, the electromyography testing instrument selected in this paper is wireless electromyography, and through collecting and analyzing the medical images of 16 muscles such as left vertical spine muscle of 10 Taekwondo athletes, and then selecting the mathematical software to further process and analyze the data obtained in the experiment, then building the data model and forming an intuitive chart for analysis. In the experimental analysis, this paper collected the medical image of the EMG characteristics of the leg control and the next split movement to analyze, and studied the muscle force rule of the leg control and the next split movement of Taekwondo athletes, and obtained more detailed and scientific data information of the leg control and the next split movement. So as to provide scientific basis for the daily movement training of Taekwondo athletes and improve the training effect of athletes.

Juan Wang. Medical Image Analysis of the EMG Characteristics of the Main Force Muscles in the Kickboxing Athletes' Leg Control and Lower Split. International Journal of Public Health and Preventive Medicine (2021), Vol. 2, Issue 1: 37-50. https://doi.org/10.38007/IJPHPM.2021.020104.

[1] Yuji, Yamada, Hitoshi, Koda, Yoshihiro, & Kai, et al. (2018). The influence of moment arm of the muscle on myoelectric activity. Japanese Journal of Health Promotion & Physical Therapy. 56(2), 88-93.

[2] Piotr, Gawda, Micha, Ginszt, Apolinary, & Halina, et al. (2018). Differences in myoelectric manifestations of fatigue during isometric muscle actions. Annals of Agricultural & Environmental Medicine Aaem. 25(1), 122-132. https://doi.org/10.26444/aaem/81716

[3] Mengarelli, Alessandro, Maranesi, Elvira, Fioretti, & Sandro, et al. (2015). Gender differences in the myoelectric activity of lower limb muscles in young healthy subjects during walking. Biomedical Signal Processing & Control. 37(7), 131-140.

[4] Frère & Julien. (2017). Spectral properties of multiple myoelectric signals: new insights into the neural origin of muscle synergies. Neuroscience Oxford. 21(6), 1-10.

[5] Frère & Julien. (2017). Spectral properties of multiple myoelectric signals: new insights into the neural origin of muscle synergies. Neuroscience Oxford. 89(5), 1827-1833.

[6] Tianyi, Song, Baowen, Meng, Baoming, & Chen, et al. (2015). Detection of genioglossus myoelectric activity using ica of multi-channel mandible semg. Technology and health care: official journal of the European Society for Engineering and Medicine. 38(9), 3305-3312.

[7] Marco, G., Alberto, B., & Taian, V. (2017). Surface emg and muscle fatigue: multi-channel approaches to the study of myoelectric manifestations of muscle fatigue. Physiological Measurement, 54(5), 323-324. https://doi.org/10.1088/1361-6579/aa60b9

[8] Patel, G. K., Castellini, C., Hahne, J. M., Farina, D., & Dosen, S. (2018). A classification method for myoelectric control of hand prostheses inspired by muscle coordination. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 26(9), 1745-1755. https://doi.org/10.1109/TNSRE.2018.2861774

[9] Budko, R., Starchenko, I., & Budko, A. (2016). Preprocessing Data for Facial Gestures Classifier on the Basis of the Neural Network Analysis of Biopotentials Muscle Signals. International Conference on Interactive Collaborative Robotics. Springer International Publishing. 22(1), 1-10. https://doi.org/10.1007/978-3-319-43955-6_20

[10] Teklemariam, A., Hodson-Tole, E. F., Reeves, N. D., Costen, N. P., & Cooper, G. (2016). A finite element model approach to determine the influence of electrode design and muscle architecture on myoelectric signal properties.  13(4), 912-925. https://doi.org/10.1371/journal.pone.0148275

[11] Frère J. (2017). Spectral properties of multiple myoelectric signals: new insights into the neural origin of muscle synergies. Neuroscience, 24(6), 10-11. https://doi.org/10.1016/j.neuroscience.2017.04.039

[12] De Souza, L. M. L., da Fonseca, Desirée Barros, Cabral, Hélio da Veiga, De Oliveira, L. F., & Vieira, T. M. . (2017). Is myoelectric activity distributed equally within the rectus femoris muscle during loaded, squat exercisesJournal of Electromyography & Kinesiology, 39(16), 1481-1495. https://doi.org/10.1016/j.jelekin.2017.01.003

[13] Cattarello, P., Merletti, R., & Petracca, F.  (2017). Analysis of high-density surface emg and finger pressure in the left forearm of violin players: a feasibility study. Medical Problems of Performing Artists, 32(3), 139-151. https://doi.org/10.21091/mppa.2017.3023

[14] De Souza, L. M. L., da Fonseca, Desirée Barros, Cabral, Hélio da Veiga, De Oliveira, L. F., & Vieira, T. M. . (2017). Is myoelectric activity distributed equally within the rectus femoris muscle during loaded, squat exercisesJournal of Electromyography & Kinesiology 16(1), 132-143. https://doi.org/10.1016/j.jelekin.2017.01.003

[15] Sharma, T., & Veer, K. (2016). Comparative study of wavelet denoising in myoelectric control applications. Journal of Medical Engineering & Technology, 40(3), 80-86. https://doi.org/10.3109/03091902.2016.1139200