A novel artificial neural network biodynamic model for prediction seated human body head acceleration in vertical direction - Gohari, Mohammad (2012)
Nowadays, usage of vehicles increases due to modern lifestyles, and many people are exposed to vibrations in vehicles. Vibrations in low frequency range cause some serious long-term diseases in both aspects physically and psychologically. Vibration model helps researchers to have better interpretation of vibrations transmitting to human organs. Lumped models are very popular in this field, and different types of models with various degrees of freedom have been introduced. The main disadvantage of lumped models is that due to its fixed weight, some modifications need to be made to new subjects. Therefore, a new biodynamic model with artificial neural network method was constructed to simulate transmitted vibration to head for seated human body by conducting indoor vertical vibration experiments. Five healthy males participated in the tests. They were subjected to vertical vibration, and their responses were recorded. A neural network model was trained by input-output accelerations. The developed model was able to predict head acceleration from exciting vibration at the pelvic. In addition, weight and height of human body were considered as input factors. The comparison between the model evaluation results and the experimental and other lumped models affirmed high accuracy of the achieved artificial neural network biodynamic model.