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This report will present the work done inMatlab, from the data obtained from the electrooculogram program which come out of an electromyogram in the lower part of the foot at the time of walking. With these measurements and analysis it is intended to observe the activation of the lower muscles and detect if there are anomalies in the behavior.
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Karol Andrea Gil Castillo Estudiante de Ingeniería Biomédica Universidad Autónoma de Occidente Cali, Colombia karol.gil@uao.edu.co - 2195523 Ashley Jhoanna Labrada Sinisterra Estudiante de Ingeniería Biomédica Universidad Autónoma de Occidente Cali, Colombia ashley.labrada@uao.edu.co - 2195412 Abstract— This report will present the work done in Matlab, from the data obtained from the electrooculogram program which come out of an electromyogram in the lower part of the foot at the time of walking. With these measurements and analysis it is intended to observe the activation of the lower muscles and detect if there are anomalies in the behavior. Keywords - EMG, Gait, Anomalies, Muscle, Filter. I. INTRODUCTION Human walking is described as a set of alternating and rhythmic movements of the lower limbs and trunk, which allows the body to move through the coordinated action of each of the components that make up the human locomotor system. [1] In the course of human growth, walking and running is a mechanical learning that becomes functional through the early stages of life, which allows free movement from one place to another; however despite being a vital function may occur in some people conditions different from normal determined by an electromyogram of the muscles and their activations over time. [2] Electromyography (EMG) consists of the recording and analysis of electrical activity generated in nerves and muscles through the use of electrodes (invasive or noninvasive), these data extracted from EMG provide important information about physiology and muscle activation patterns. [3] The properties of EMG signals in both the time and frequency domain depend on factors such as the time and intensity of muscle contraction, the distance between the electrode and the area of muscle activity. The signal quality depends on the ratio between the measured signal and the noise unwanted by the environment, which should maximize the signal amplitude while minimizing the noise. [3]
In order to take the signal, the time and amplitude of activation of 3 leg muscles during certain gait movements were studied. Initially, the patient was prepared by cleaning the sites where the electrodes were to be placed in order to avoid the generation of any type of noise. In order to study the muscles involved in the gait, the electrodes were positioned on the tibial, lateral and medial gastrocnemius muscles. The ground electrode was placed on the iliac crest as shown in the following figures: Figure 1. Position of the electrode on the tibialis anterior Figure 2. Electrode position on the medial gastrocnemius muscle
Figure 3. Electrode position on lateral gastrocnemius Once the electrodes were positioned, they were covered with a bandage so that no noise would be generated by the movement of the electrodes. Once positioned and linked to the electrooculograph by means of the cables, the patient was asked to perform a short walk of a range of 2 to 4 meters so that the electrodes could record the activation of the muscles over time. After making the recordings, these data were stored using Excel to be analyzed and filtered in Matlab, allowing a careful, detailed and accurate analysis of the signal, to generate a comparison between the filtered signal and the unfiltered signal, as well as an analysis as to the relationship between the movement performed and the activated muscles. On the other hand, with respect to the clothing used, it was considered pertinent to use rash guards, in order to expose the muscle to be measured. III. RESULTS The results obtained by the patient can be observed and visualized by means of the plot function provided by Matlab. A. Signal obtained in electromyography Figure 4. Tibialis response. Source: Own. Figure 5. Lateral Gastrocnemius response. Source: Own. Figure 6. Response of the medial Gastrocnemius. Source: Own. Figures 4,5,6 show the EEG of the rectus muscle, in this signal it is possible to observe the moments of tension of the muscle visualized in the high peaks of the signal, however, due to external factors such as friction with clothing or the high impedance of the skin, noise can be observed in the signal acquisition, for this reason it is necessary to filter the signal. Figure 7. Spectrum of the signal found with the FFT in the Tibialis. Source: Own. Figure 8. Spectrum of the signal found with the FFT in the lateral Gastrocnemius. Source: Own. Figure 9. Spectrum of the signal found with the FFT in the medial Gastronemius. Source: Own.
compared to the usual. On the other hand, it is worth mentioning that the electromyographic signal, although it does serve to observe and diagnose the current state of the muscle and how they generate voltage, usually has a large amount of noise so it is necessary to perform a conditioning process. For this, the fourier transform is performed, to know where the energy of the signal lies, and with it to make a filter that shows me only what is desired; this filter must be of Butterworth type since the Sallen-key produces gain which leads to generate an undesired amplitude to the signal. REFERENCES [1] C. Cifuentes, F. Martinez, E. Romero. Análisis teórico y computacional de la marcha normal y patológica, 2010. Disponible en: ANÁLISIS TEÓRICO Y COMPUTACIONAL DE LA MARCHA NORMAL Y PATOLÓGICA: UNA REVISIÓN (scielo.org.co) [2] M. J. Fernández, E. Toledo, M. Cañón, J. C. Manuel-Palazuelos, and J. M Maestre, “Desarrollo y validación de una herramienta para la evaluación de la anastomosis intestinal laparoscópica en simulación,” Cirugía Española , vol. 98, no. 5, pp. 274–280, 2020 [3] A. Arévalo, D. Toloza. Electromiografía (EMG). Disponible: electromiografía (EMG) - Dalcame: Grupo de Investigación Biomédica - El [4] A. Von Boxtel. Optimal signal bandwidth for the recording of surface EMG activity of facial, jaw, oral, and neck muscles. Disponible: Optimal signal bandwidth for the recording of surface EMG activity of facial, jaw, oral, and neck muscles - Boxtel - 2001 - Psychophysiology - Wiley Online Library [5] Mark B. BromBerg. Single fiber EMG reference values:Remormatted in tabular form. Disponible en: 880170720_ftp.pdf (umich.edu)