Muscle synergies in joystick manipulation

Abstract

Extracting muscle synergies from surface electromyographic signals (sEMGs) during exercises has been widely applied to evaluate motor control strategies. This study explores the relationship between upper-limb muscle synergies and the performance of joystick manipulation tasks. Seventy-seven subjects, divided into three classes according to their maneuvering experience, were recruited to perform the left and right reciprocation of the joystick. Based on the motion encoder data, their manipulation performance was evaluated by the mean error, standard deviation, and extreme range of position of the joystick. Meanwhile, sEMG and acceleration signals from the upper limbs corresponding to the entire trial were collected. Muscle synergies were extracted from each subject’s sEMG data by non-negative matrix factorization (NMF), based on which the synergy coordination index (SCI), which indicates the size of the synergy space and the variability of the center of activity (CoA), evaluated the temporal activation variability. The synergy pattern space and CoA of all participants were calculated within each class to analyze the correlation between the variability of muscle synergies and the manipulation performance metrics. The correlation level of each class was further compared. The experimental results evidenced a positive correlation between manipulation performance and maneuvering experience. Similar muscle synergy patterns were reflected between the three classes and the structure of the muscle synergies showed stability. In the class of rich maneuvering experience, the correlation between manipulation performance metrics and muscle synergy is more significant than in the classes of trainees and newbies, suggesting that long-term training and practicing can improve manipulation performance, stability of synergy compositions, and temporal activation variability but not alter the structure of muscle synergies determined by a specific task. Our approaches and findings could be applied to 1) reduce manipulation errors, 2) assist maneuvering training and evaluation to enhance transportation safety, and 3) design technical support for sports.

Exploring biomechanical variations in ankle joint injuries among Latin dancers with different stance patterns: utilizing OpenSim musculoskeletal models

“…The sampling frequencies for kinematics and kinetics were 200 and 1,000 Hz ( Xu et al, 2022 ), respectively. The EMG system (Delsys, Boston, Massachusetts, United States) was used to collect surface muscle activation and force data at a frequency of 1,000 Hz ( Cai et al, 2023 ; Cai et al, 2024 ). Surface electromyography (EMG) sensors were placed on the subjects’ vastus medialis, vastus lateralis, rectus femoris, tibialis anterior, medial gastrocnemius, and lateral gastrocnemius muscles.…”

“…The Vicon Nexus software was employed to export data in c3d format for the acquisition of participants’ kinematic and kinetic data ( Li et al, 2022 ). Subsequently, the data undergoes processing using MATLAB R2022a (The MathWorks, Natick, MA, United States) ( Cai et al, 2023 ), involving operations such as coordinate transformation, low-pass filtering, data extraction, and format conversion. The coordinate systems of kinematic and kinetic data were transformed into the coordinate system used in subsequent simulations.…”

“…Consequently, FT dancers may adapt their lower limb usage during dancing to accommodate the reduced stance area. In the evaluation of the significance of foot functionality and the preservation of body posture stability, the pivotal roles played by muscle activation and muscle synergy come to the forefront ( Cai et al, 2023 ). Additionally, the FT movement pattern increases plantar flexion at the ankle joint, effectively increasing the distance between the heel and the ground, akin to an increase in heel height.…”

Remote intelligent perception system for multi-object detection

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Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

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