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Flexion and Extension of Spine in Sagittal Plane with Muscles Under the Control of Central Pattern Generators

Seddighi, Alireza | 2011

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 42002 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Parnianpour, Mohamad; Sadati, Nasser; Narimani, Roya
  7. Abstract:
  8. Low back pain is a widespread disorder in industrialized countries. Based on epidemiological reports, 80% of the population faces this activity limitation at least once in their lifetime which places tremendous human and economic costs to individuals and societies. Handling heavy loads, with fast trunk motions, repetitive movements, and awkward postures are some of the risk factors related to low back injuries. Hence, better understanding of the neuro-musculo-skeletal system performance would help us to recognize various abnormalities in spine behavior and assist us in a way to design the workplace to reduce the risk of injuries. For this purpose, we can use biomechanicals model to investigate the consequences of various movement strategies for estimation of muscle forces and joint reaction forces affecting the spine.This study, presents a novel method for simulation of a 3D trunk model under control of 48 muscle actuators, then we propose two different scenarios. In the first control system, 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 to address the kinetic redundancy due to presence of 48 muscles driving the trunk. In addition, in the second stage, CPG are utilized as desired trajectories generation, and a neuro-fuzzy system is used to prudence muscles’ activation as controller’s signals. In this part, the parameters of neuro-fuzzy network are updated with help of emotional learning to solve kinetic redundancy. 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. Furthermore, experimental tests are designed that tests the muscle recruitment patterns and movement profiles for point to point and repetitive trunk planar and complex movements with different cycle time, range of motion and directions. The results of experimental and theoretical predictions are compared. These models are too complex to be validated completely, but the similarity of predictions under similar boundary conditions gives additional confidence about feasibility of the complex mathematical model. 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
  9. Keywords:
  10. Spine ; Neural Network ; Intelligent Control ; Central Pattern Generator ; Computational Motor Control

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