Estimating Angular Joint Positions Based on Electromyographic (EMG) Activity.

Xiangwei Meng,Teresa Zielinska,Eric Le Carpentier, Yannick Aoustin

International Workshop on Robot Motion and Control（2024）

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摘要

This study aims to estimate the movement of the upper limb using signals collected by surface electromyography (sEMG). Signals recorded for selected shoulder and elbow muscles during limb motion in the sagittal plane were used. An artificial neural network with fuzzy logic was applied to process sEMG signals. The network forecasts joint motion trajectories. These data are then used to determine joint torques based on inverse dynamics model. The angular trajectories obtained in dynamic simulation are compared with the real ones, allowing for the assessment of the motion estimation accuracy.

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关键词

Angular Position,Dynamic Model,Artificial Neural Network,Upper Limb,Sagittal Plane,Fuzzy Logic,Surface Electromyography,Real Ones,Joint Torque,sEMG Signals,Degrees Of Freedom,Gaussian Kernel,Center Of Mass,Training Phase,Transient State,Upper Arm,Muscle Groups,Membership Function,Fuzzy Set,Exoskeleton,Elbow Angle,Force Plate,Elbow Joint,sEMG Data,Biceps Brachii,Shoulder Flexion,Joint Trajectories,Shoulder Angle,Actual Angle,Fuzzy System

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