The aim of this paper is to analyze driver's hands coordinates on the steering wheel for an optimal and safe driving experience. A good coordination of the driver's action on the controls is the result of a comfortable position that leads to an optimal reaction while driving. The presented study implies using a thermal imaging camera for analysing palms temperature changes in the contact area with the steering wheel. The resulting data shows that the optimal driving position of drivers' hands is 0° and 180° associating the steering wheel with and trigonometric circle.

The paper presents artificial neural networks (ANNs) as a tool for validating the mathematical model. The neural network architecture must learn the relationship between the inputs (which are the parameters of the biomechanical mathematical model, such as height and body mass) and the desired outputs (i.e. the perturbing forces of the biomechanical model elements. We used part of the perturbing force values of the muscle groups responsible for the spine, neck, and head movement, determined using the mathematical model and the C++ application, to train the neural network. We used the remaining data to validate the neural network. The neural network architecture was created using the Easy NN application. After training the network, we concluded that the subject's height has the most significant impact on generating muscle force and is also the most sensitive parameter. The muscle force values of the data used for validation are almost equal to those determined using the mathematical model. Therefore, we can conclude that the mathematical model is correct, and the neural network can make predictions for various subject dimensions, even if their values are not within the range of values for which we trained the network. Keywords: mathematical model; neural networks; human body

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