ELECTROMYOGRAPHIC SIGNAL ANALYSIS OF HEALTHY SUBJECTS ON THE USE OF STROKE REHABILITATION ROBOT

• Main Author: Ivonita Simbolon, Artha
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/50416
• Institution: Institut Teknologi Bandung

Stroke is the second leading cause of death and the third leading cause of adult disability. The number of stroke sufferers increases significantly years by years. Motor impairment in the upper extremities is one of the effects due to a stroke attack. This disorder can occur in people’s arms. Post-stroke patients need to undergo physical rehabilitation to restore their motor function. Physical rehabilitation that provides maximum results has several factors, i.e. the appropriate training load, high-intensity training, repeated training, and high patient willingness. However, a small number of medical personnel is one of the limitations in carrying out physical rehabilitation at high intensity. Therefore, a lot of research has been done to make physical rehabilitation aids, one is a rehabilitation robot. The Laboratory of Control Instrumentation System Management, Bandung Institute of Technology (MSIK ITB Lab) has developed a physical rehabilitation robot. This robot serves to train the arms with movements of shoulder extension and flexion. It is very important to evaluate the robot on healthy subjects before being used by stroke patients to observe the effect of the robot on motor function. This evaluation can be done by analyzing the electromyographic signal (EMG) to obtain a direct condition of the muscles. The results of this analysis are also used as a baseline for evaluating the muscle performance of stroke patients when using the robot. There are 20 healthy male subjects with a mean age of 28 ± 1.7 years participated in this study. The measured muscles were the bicep brachii and tricep brachii. Subjects were instructed to perform movements of shoulder extension and flexion. The movement was carried out in two conditions, namely without and with the robot. Each condition was taken for five sessions and in each session, the subject performed the shoulder extension and flexion for five times. The raw data of the EMG signal was filtered using a bandpass filter (cut off 15-450 Hz) and a notch filter (cut off 50 Hz). Then the EMG signal was analyzed by calculating the value of the coactivation index (CI), root means square (RMS), the slope value of the mean frequency (MNF), and median frequency (MDF). The results then performed with two independent sample T-test to see the significance. The CI value of the biceps brachii and tricep brachii for each subject was close to a value of 1 both for movement without or with the robot, it was 0.8138 for movement without the robot and 0.7638 for movement with the robot. It indicated that the bicep brachii and tricep brachii contract simultaneously or called synergistic. The average RMS value of the EMG signal in the bicep brachii for extension and flexion movement without the robot were 0.2183 ± 0.0613 mV and 0.2392 ± 0.0629 mV, while the extension and flexion with the robot were 0.4892 ± 0.0726 mV and 0.5583 ± 0.0847 mV respectively. The average RMS value of the EMG signal in the tricep brachii for extension and flexion movement without the robot were 0.2663 ± 0.1150 mV and 0.2844 ± 0.1011 mV, whereas extension and flexion with the robot were 0.5548 ± 0.1049 mV and 0.5953 ± 0.0842 mV respectively. The RMS value for movement with the robot was higher than without the robot. It showed that the force generated by the muscles in the movement with the robot was greater than the movement without the robot. The difference in RMS value was significant through the T-test (p <0.05). In the bicep brachii, for movement without the robot, the MNF slope value was -0.798 and MDF was -0.8799. Meanwhile, for movement with the robot, the MNF slope value was 0.1195 and MDF was 0.2729. In the triceps brachii muscle, for movement without the robot, the MNF slope value was -1.029 and MDF -0.9562. As for the movement with the robot, the MNF slope value was -0.1804 and MDF was -0.135. The negative value on the MNF slope and MDF in both muscles were higher when the movement was without the robot. This showed that movement without the robot resulted in greater muscle fatigue than a movement with the robot. The difference in the MNF slope value and MDF of the EMG signal in bicep brachii muscle for movement without and with the robot had a significant value through the T-test (p <0.05). Meanwhile, the MNF slope value and MDF of the triceps brachii muscle signal for movement without and with the robot did not have significant value through the T-test (p> 0.05). In the use of robot, the resulting EMG signal has a greater RMS but does not cause greater fatigue than movement without robot. This is because the movement with robot has resulted in intermittent contraction, where the peak RMS is only for a moment. Intermittent contraction causes increased blood flow which exerts a relaxing effect on the muscles thereby reducing fatigue. Based on the analysis results, the movement with the robot can increase the force generated by the bicep brachii and tricep brachii muscles without causing greater muscle fatigue than the movement without the robot.