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With the above, the main causes for which muscle disorders are considered as a case study are exposed, for the development of research that leads to obtaining solutions that improve the quality of life of people who suffer from them. For this, the acquisition of electromyography signals acquired through a MYO is performed in patients who emulate tremor.
International Journal of Mechanical Engineering and Technology (IJMET) Volume 10, Issue 12, December 2019, pp 294-302, Article ID: IJMET_10_12_032 Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=12 ISSN Print: 0976-6340 and ISSN Online: 0976-6359 © IAEME Publication BIOPHYSIOLOGICAL SIGNAL ANALYSIS THROUGH ELECTROMYOGRAPHY IN PEOPLE WITH TREMOR Angie J Valencia C Faculty of Engineering Militar Nueva Granada University, Bogotá D.C., Colombia Mauricio Mauledoux Faculty of Engineering Militar Nueva Granada University, Bogotá D.C., Colombia Edilberto Mejia-Ruda Faculty of Engineering Militar Nueva Granada University, Bogotá D.C., Colombia Ruben D Hernández Faculty of Engineering Militar Nueva Granada University, Bogotá D.C., Colombia Óscar F Avilés Faculty of Engineering Militar Nueva Granada University, Bogotá D.C., Colombia ABSTRACT Tremor is defined by a series of muscular contractions that produce agitated movements in different parts of the body, most often affecting the hands, followed by the arms, head, vocal cords, torso and legs This can be constant or intermittent, sporadic, or as a result of another usually neurological disorder The frequency of the tremor, that is, the "speed" of the shock may decrease as the person ages, while the intensity of the tremor increases Strong emotions, stress, fever, physical exhaustion or low blood sugar can trigger the tremor or increase its intensity [1] With the above, the main causes for which muscle disorders are considered as a case study are exposed, for the development of research that leads to obtaining solutions that improve the quality of life of people who suffer from them For this, the acquisition of electromyography signals acquired through a MYO is performed in patients who emulate tremor Keywords: Quality of Life, Neurodegenerative Diseases, Medium Square Error, Effort, MYO, Oscillating Movements, Tremor http://www.iaeme.com/IJMET/index.asp 294 editor@iaeme.com Biophysiological Signal Analysis Through Electromyography in People with Tremor Cite this Article: Angie J Valencia C, Mauricio Mauledoux, Edilberto Mejia-Ruda, Ruben D Hernández, Óscar F Avilés, Biophysiological Signal Analysis Through Electromyography in People with Tremor International Journal of Mechanical Engineering and Technology 10(12), 2019, pp 294-302 http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=12 INTRODUCTION The first investigations on the suppression of tremor by means of biomechanical mechanisms resulted in devices that were mostly non-ambulatory and that depended on the damping forces [2 3] Then, auxiliary (outpatient) devices were presented that involve the joints of the upper limbs, the wrist and the elbow, with suppression technologies that ranged from passive damping to active damping, using actuators such as electric motors, among others [4, 5, 6, 7] Likewise, functional electrical stimulation and soft actuators, based on conductive polymers and piezoelectric fiber compounds, have been suggested as an alternative to rigid systems [8, 9, 10, 11, 12] The recognized limitation for electrical stimulation works include redundancy and coupling of the muscles involved in joint activations, while the limitation in terms of hardware by surface electrodes consists of access to specific muscles and fatigue muscular For the proper management of the techniques that must be implemented in the development of devices that contribute to the rehabilitation of patients with pathological tremor, the tremor and voluntary components of a registered signal must be distinguished in the first instance, either for diagnosis or treatment In-line decomposition of the signal, in particular, poses a greater challenge than an offline calculation, which has employed strategies ranging from linear filtering to stochastic estimators In [13] an optimal digital filter was designed offline through follow-up tasks In [7] they used a second order low pass filter applied to an electromyography tremor signal to be transmitted to a neural network, intended to control an elbow device In [14] they used a high-pass filter to separate the tremor component before passing it to a repetitive control loop using a functional electrical stimulation system Another recent work used a tremor estimator in the form of a high-pass filter, which resulted in a significant phase change, which was corrected before being applied to the suppressor actuator [15] It is not uncommon to evaluate the methods of tremor suppression by performing simulations, either numerically or experimentally and obtaining access (with the consent of an ethical committee) to patients once the simulations have given optimal results [11, 15, 16, 17, 18, 19, 20, 21] These can promote optimization and debugging of the suppression system and therefore improve performance In addition, patient recordings can be used to simulate the tremor profile by helping to narrow the gap between simulations and tests with subjects As for the control strategies that have been contemplated for the management of biomechanical mechanisms for rehabilitation, those that work on the impedance are taken into account, which attempt to modify the man-machine frequency response so that a greater impedance is present in the tremor frequencies [4, 22, 23] The disadvantage of this control lies in the sensitivity to inaccuracies in the parameters of the human-machine model or changes thereof over time Other techniques to suppress tremor are based on inertial sensors, by means of the detection of interaction forces between the user and the suppression system as a feedback measure In [24], they guide a tremor suppression device to follow the voluntary movement of a user who has tremor The device suppresses the tremor by resisting the respective movement, while simultaneously moving along with the voluntary movement In addition, an orthosis system was developed to validate the tremor suppression approach directed at the http://www.iaeme.com/IJMET/index.asp 295 editor@iaeme.com Angie J Valencia C, Mauricio Mauledoux, Edilberto Mejia-Ruda, Ruben D Hernández, Óscar F Avilés human elbow, consisting of a suppression motor, gears, sensors that include a force transducer and an encoder, next to the upper arms and of the forearm In [25], a neuro prosthesis was implemented that regulated the level of muscle cocontraction by injecting current through transcutaneous neuro-stimulation The co-contraction was adapted to the instantaneous tremor parameters that were estimated from the raw recordings of a pair of solid-state gyros with an adaptive algorithm designed on purpose In the end, the results presented demonstrate that the neuro prosthesis provides a systematic attenuation of the two main types of tremor, regardless of their severity The present work is structured as follows: Section 1, where the procedure for the acquisition of electromyographic signals is shown In section 2, the processing of the acquired signals is performed Finally, in section conclude on the procedures performed ACQUISITION OF ELECTROMIOGRAPHIC SIGNS 2.1 System Calibration For the acquisition of electromyography data in the upper limb of patients with tremor, the MYO is used as a transducer device From there it starts with calibration steps to configure the orientation of the device, from movements such as forearm flexion (a), an extension movement (b), lateral forearm rotation (c), average forearm rotation (d) , supination movement (e) and pronation movement (f), as seen in Figure 1, where the direction of the red vector indicates the position of the hand and the cylinder emulates the behavior of the forearm (a) (b) (c) (d) (e) (f) Figure Calibration Movements 2.2 Acquisition of Data in People with and without Tremor After performing the calibration of the system, we proceed with the acquisition of biophysiological signals of people with and without tremor from the execution of daily tasks such as drinking a drink For this, similar test conditions are stipulated in the test subjects such as a constant vessel weight and a defined trajectory From there, figures and are obtained for a speed behavior on the X axis, figures and for the Y axis, and figures and for the Z axis http://www.iaeme.com/IJMET/index.asp 296 editor@iaeme.com Biophysiological Signal Analysis Through Electromyography in People with Tremor Sick Time Figure Speed person with tremor X axis Healthy Time Figure Speed person without tremor X axis Sick Time Figure Speed person with tremor Y axis Healthy Time Figure Speed person without tremor Y axis http://www.iaeme.com/IJMET/index.asp 297 editor@iaeme.com Angie J Valencia C, Mauricio Mauledoux, Edilberto Mejia-Ruda, Ruben D Hernández, Óscar F Avilés Sick Time Figure Speed person with tremor Z axis Healthy Time Figure Speed person without tremor Z axis The acceleration behaviors of the three quadrants are then obtained as shown in Figure through 13 Sick Time Figure Acceleration person with tremor X axis Healthy Time Figure Acceleration person without tremor X axis http://www.iaeme.com/IJMET/index.asp 298 editor@iaeme.com Biophysiological Signal Analysis Through Electromyography in People with Tremor Sick Time Figure 10 Acceleration person with tremor Y axis Healthy Time Figure 11 Acceleration person without tremor Y axis Sick Time Figure 12 Acceleration person with tremor Z axis Healthy Time Figure 13 Acceleration person without tremor Z axis http://www.iaeme.com/IJMET/index.asp 299 editor@iaeme.com Angie J Valencia C, Mauricio Mauledoux, Edilberto Mejia-Ruda, Ruben D Hernández, Óscar F Avilés RESULTS AND DISCUSSION To finally determine the variability in the data of a person with and without tremor taking into account the values of mean, variance and standard deviation, grouped in table Table 1: Variability calculation Parameter Sick X Speed Healthy X Speed Sick Y Speed Healthy Y Speed Sick Z Speed Healthy Z Speed Sick X acceleration Healthy X acceleration Sick Y acceleration Healthy Y acceleration Sick Z acceleration Healthy Z acceleration Average -2.6027 2.0111 10.4901 -3.9418 -6.0371 -14.7240 -0.0664 0.0373 -0.1472 0.9013 0.9117 0.2428 Variance Standard Deviation 179.3621 13.3926 141.9056 11.9124 276.2873 16.6219 13.7175 3.7037 51.8823 7.2029 635.9661 25.2184 0.0101 0.1006 0.0192 0.1387 0.0040 0.0633 0.0022 0.0474 0.0019 0.0431 0.0014 0.0375 Then it is done in the calculation of the average normalized quadratic error, in order to estimate when the movement has to be corrected in a person with a tremor with respect to one who does not have it This error is stipulated in table Table 2: Error Analysis Parameter Speed X Sick vs Healthy Speed Y Sick vs Healthy Speed ZSick vs Healthy Acceleration X Sick vs Healthy Acceleration Y Sick vs Healthy Acceleration Z Sick vs Healthy Standard Normalized Quadratic Error -88.7124 -13.3943 8.9985 -9.6012 -8.3381 2.0385 CONCLUSIONS With respect to the data obtained, it can be observed that in the X and Y quadrants a person without tremor has freedom of movement and with it more speed in its execution While there is greater variability in movement in the Z axis as seen in Table 2, there are higher speeds in this axis when it comes to a person with involuntary behavior in their upper extremities As for the behavior in acceleration, it can be concluded that in the X and Y quadrants, since there is greater speed in a person without tremor with respect to the one who does not have it, there is directly proportionally greater acceleration And finally, on the Z axis, since there are no oscillations in a healthy person, there is less acceleration compared to that with involuntary behaviors ACKNOWLEDGEMENT The research for paper was supported by Military Nueva Granada University by research project ING 2658 http://www.iaeme.com/IJMET/index.asp 300 editor@iaeme.com Biophysiological Signal Analysis Through Electromyography in People with Tremor REFERENCES [1] Elble, Rodger, and 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Acquisition of Data in People with and without Tremor After performing the calibration of the system, we proceed with the acquisition of biophysiological signals of people with and without tremor from... Ruben D Hernández, Óscar F Avilés, Biophysiological Signal Analysis Through Electromyography in People with Tremor International Journal of Mechanical Engineering and Technology 10(12), 2019, pp... http://www.iaeme.com/IJMET/index.asp 296 editor@iaeme.com Biophysiological Signal Analysis Through Electromyography in People with Tremor Sick Time Figure Speed person with tremor X axis Healthy Time