Dynamic Modeling and Optimization of a Fall Prevention Device Using Genetic Algorithm

Nerve injury after stroke usually weakens the trunk muscle strength (TMS) and balance function, which increases the risk of falling and affects the Activities of Daily Living (ADL). Thus a novel fall prevention device is proposed to reinforce the TMS and retrain the sense of trunk position. Firstly, the structure of the fall prevention device for protection is introduced, the forward and inverse kinematic models are calculated. Then, the kinetic equation of the system based on the Lagrange function are derived out, the structural parameters incorporated into the structural dynamic model include those related to the feedback force in a given motion trajectory of trunk. Lastly, the numerical simulation using the MATLAB is carried out to study the influence of those parameters. It is found that both the length of link and the stiffness of spring have strong influence on the feedback force during gait training. Global optimization of these structural parameters is carried out to demonstrate the practical application of the proposed method for the forming of force field of the system.