EMG Driven Musculoskeletal Simulation of the Human Lower Body: A Biomechanical and Electromyographyic Study

EMG Driven Musculoskeletal Simulation of the Human Lower Body: A Biomechanical and Electromyographyic Study Review

Predictive musculoskeletal models have the potential to positively influence the orthopaedic management of movement pathologies. A scalable geometric model of the lower-body was defined from an adult database. A Hill-type EMG driven dynamic computational muscle model was developed and validated against published and new experimental data. A lower-body simulation model was constructed incorporating skeletal joint definitions, musculotendon actuators, passive joint dynamics and ground reactions forces. A 13-muscle EMG driven inverse-kinetic simulation model of knee flexion-extension contraction was developed and evaluated using dynamometric data covering a wide- range of contraction speeds and modes (isokinetic, isotonic, eccentric, and isometric) for five able- bodied adult male subjects. Both shape and transfer function based Hill-type muscle models were evaluated. For the transfer function-based model, across all subjects the average correlations ranged between r = 0.61-0.77 and average RMS error = 21- 29%. For the shape function-based model, the average correlations ranged between r = 0.76-0.92 and an average RMS error = 25-31%.

Design and Analysis of Virtual Human Arm Driven by EMG Signal: Simulation with GUI design

Design and Analysis of Virtual Human Arm Driven by EMG Signal: Simulation with GUI design Review

The real human arm is a complex kinematics system. It works according to instructions calculated by a highly accurate method, and has a moving system working by deity manner. The design and simulation analysis of arm moving system model with 7-Degree of Freedom (DOF) are presented. The electromyography (EMG) signal is the activation signal for muscles in human arm and accordingly the arm will have a certain movement. The arm dynamic system in real environment simulation was implemented using the inverse kinematics problem (IKP) analytical solution. The computation time of this simulation is very fast and has the ability to include any real constrain. The finite recurrent back propagation neural network ( FRBP-NN ) is used with the simulated system for identification, classification, and human arm movements recognition with respect to a specific EMG signal. Virtual Reality Toolbox, which is interfaced with the Simulink MATLAB. Satisfactory results are obtained, which give the solution of the forward and inverse kinematic human arm and the usefulness of using FRBP-NN to recognize the movements of human arm.