During the two-second hold period, we extracted 0.5s of data for subsequent data analyses.īaseline sEMGs were first obtained when the participant was relaxing without generating any torques with their right arm. For each shoulder abduction load, the participant performed 10 trials. When the participant achieved desired shoulder abduction and elbow flexion torques, they were asked to maintain the desired torque for two seconds. This task was completed when abducting to two different shoulder abduction loads: 10% and 50% of their maximum voluntary torque in shoulder abduction. The participant was asked to flex about their elbow to 25% of their maximum voluntary torque in elbow flexion. The participant was asked to maximally activate each of the eight muscles and the maximum sEMG data was obtained for each respective muscle. This task was completed when the participant was relaxing without generating any torques with their right arm. The participant's testing arm was affixed to an isometric measurement device at 85° shoulderĪbduction, 40° shoulder flexion, and 90° elbow flexion. Sat with their torso and waist strapped to the Biodex chair. At the beginning of the testing session, the participant The participant was requested to not exercise the day before and of testing to avoid muscle fatigue. Participants were all right-hand dominant, and their mean ± standard deviation age was 26 ± 3 (range: 22-29) Dataĭata were collected from one male and three female participants. A DAQ card acquires data from these sensors, and a Matlab program streams the data. The sEMG signals indicate theĮlectrical activity within each of the eight testing muscles. Medial deltoid, sEMG5: posterior deltoid, sEMG6: pectoralis major, sEMG7: lower trapezius, and sEMG8: middle trapezius). Quantifies muscle activity using eight surface electromyography (sEMG) electrodes (sEMG1: biceps, sEMG2: triceps lateral, sEMG3: anterior deltoid, sEMG4: The acquired data, in conjunction with aīiomechanical model, indicate the extent to which the participant flexes about the testing elbow and abducts about the shoulder. The system acquires torque data from a six-degree-of-freedom load cell. The experimental setup, as shown in Figure 3, was comprised of a custom mechatronic system, a monitor, speakers, and Biodex chair. Least two synergies for this task: one that is mainly responsible for the elbow flexion and one for shoulder abduction. Based on the muscle synergy hypothesis, we would expect to observe at
Normal muscle activation patterns in a population that is neurologically and orthopaedically intact.įor a task involving abduction at the shoulder and flexion at the elbow, the main muscles involved are the biceps, triceps lateral, anterior deltoid, medialĭeltoid, posterior deltoid, pectoralis major, lower trapezius, and middle trapezius. The goal is to determine how muscles concurrently activate when an individual abducts at the shoulder and flexes at the elbow. In this work, the focus is on muscle activation at the arm. Rather than individual muscles, allows for a simplified control of one’s limb. The activation of muscles in this grouped manner is termed muscle synergies and is part of a hierarchical control strategy. Instead of controlling each muscle individually, our brain is thought to recruit these muscles in set groups. There are more than 600 muscles in our body. In turn, muscles contract and the arm is raised and extended. To achieve this task, movement-related signalsĪre generated from the motor cortex of our brain and relayed to muscle fibers via motor neurons. While seemingly simple, a motor task, such as retrieving an object from a high shelf, is very complicated. This project aims to identify how muscles are synergistically activated at the arm during a multi-joint task. Muscle Synergy Identification Unsupervised Learning Signal Processing Python Matlab