Control of human spine in repetitive sagittal plane flexion and extension motion using a CPG based ANN approach.

ABSTRACT The complexity associated with musculoskeletal modeling, simulation, and neural control of the human spine is a challenging problem in the field of biomechanics. This paper presents a novel method for simulation of a 3D trunk model under control of 48 muscle actuators. Central pattern generators (CPG) and artificial neural network (ANN) are used simultaneously to generate muscles activation patterns. The parameters of the ANN are updated based on a novel learning method used to address the kinetic redundancy due to presence of 48 muscles driving the trunk. We demonstrated the feasibility of the proposed method with numerical simulation of experiments involving rhythmic motion between upright standing and 55 degrees of flexion. The tracking performance of the model is accurate to within 2° while reciprocal muscle activation patterns were similar to the observed experimental coordination patterns in normal subjects. The suggested method can be used to map high-level control strategies to low-level control signals in complex biomechanical and biorobotic systems. This will also provide insight about underlying neural control mechanisms.

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    ABSTRACT: This study evaluated an adaptive control system (the PG/PS control system [2]) that had been designed for generating cyclic movements using functional neuromuscular stimulation (FNS). Extensive simulations using computer-based models indicated that a broad range of control system parameter values performed well across a diverse population of model systems. The fact that manual tuning is not required for each individual makes this control system particularly attractive for implementation in FNS systems outside of research laboratories.
    IEEE Transactions on Biomedical Engineering 10/2000; 47(9):1287-92. DOI:10.1109/10.867965 · 2.23 Impact Factor
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    ABSTRACT: The role of motor control in development of low back pain is subject of many researches both in theoretical and experimental fields. In this work flexion-extension movement of lumbar spine have been controlled by three different methods, including feedback linearization (FBL), PD control and their combinations. The model involves 7 links: 1 link for pelvis, 5 links for lumbar vertebrae and 1 link for trunk. Torque actuators have been used on each joint to make them follow desired trajectory. In linear control method, equations of motion have been linearized with respect to upright position and then control signals have been applied in the direction of eigenvectors. Robustness of each method against noises, sensory delay and parameters uncertainty have been investigated. Desired trajectory of each joint has been produced by Central Pattern Generators (CPGs), which was the subject of our previous work. The results show that PD controller in comparison with feedback linearization method is more robust in presence of noise and parameters uncertainty, but FBL controller is better when we have sensory delay. Combination of these methods leads to better control in all three simulations.
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    ABSTRACT: Computation of muscle excitation patterns that produce coordinated movements of muscle-actuated dynamic models is an important and challenging problem. Using dynamic optimization to compute excitation patterns comes at a large computational cost, which has limited the use of muscle-actuated simulations. This paper introduces a new algorithm, which we call computed muscle control, that uses static optimization along with feedforward and feedback controls to drive the kinematic trajectory of a musculoskeletal model toward a set of desired kinematics. We illustrate the algorithm by computing a set of muscle excitations that drive a 30-muscle, 3-degree-of-freedom model of pedaling to track measured pedaling kinematics and forces. Only 10 min of computer time were required to compute muscle excitations that reproduced the measured pedaling dynamics, which is over two orders of magnitude faster than conventional dynamic optimization techniques. Simulated kinematics were within 1 degrees of experimental values, simulated pedal forces were within one standard deviation of measured pedal forces for nearly all of the crank cycle, and computed muscle excitations were similar in timing to measured electromyographic patterns. The speed and accuracy of this new algorithm improves the feasibility of using detailed musculoskeletal models to simulate and analyze movement.
    Journal of Biomechanics 04/2003; 36(3):321-8. DOI:10.1016/S0021-9290(02)00432-3 · 2.50 Impact Factor