Conventional and non conventional body motions modelling and control. Application to the handwriting process

Abstract

The handwriting process is one of the most important and complex activities of the human body. In recent years, modelling and analyzing and controlling it have received great attention leading to large important motion studies results. This paper attempts to present an overview of recent works about our proposed approaches and other ones to model and control this process. Hence, an overall model of the handwriting process has been proposed and developed by the coauthors, showing the interest of using the soft computing concepts, namely neural networks and fuzzy logic. The design of this model requires several undertaken steps. Starting from an experimental approach to acquire electromyographic signals and writing positions, different conventional and non conventional models have been proposed. Besides some control strategies have been considered in particular neural and fuzzy approaches in addition to non conventional neural and neuro-fuzzy Internal Model Control strategy. The proposed global model, inspired from the biological structure system, has been designed and validated through simulation results.

Tensor product‐based model transformation approach to cart position modeling and control in pendulum‐cart systems

“…Another direction of future research will be dedicated to the development of hybrid control techniques with focus on TP‐MT fuzzy control. Several intelligent control features [38–44] will be integrated including fuzzy model features as, for example, those given in other studies [45–56]. The systematic analysis of developed CSSs is also needed as both research directions are focused on the performance improvement, which should be guaranteed accordingly.…”

The application of hybrid feature based on local mean decomposition for motor imagery electroencephalogram signal classification

“…The brain-computer interface (BCI) uses neural signals to control external devices [1,2] and exchange information between the brain and the devices. In recent years, with the deepening of people's cognition of the brain and the development of computer technology, it has become the focus of many scientific and technological workers around the world and has been widely used in many fields [3,4]. When the subject only imagined an action without performing it, the same sensorimotor areas of the brain were activated as when the action was performed and produced similar electroencephalogram (EEG) patterns, which are known as motor imagery (MI)-related EEG signals [5,6].…”

Fuzzy‐HMM modeling for emotion detection using electrocardiogram signals

“…Modeling problems are important issues in many applications, especially in control systems design [13–16]. Furthermore, probability models are efficient for modeling a dynamic system [17].…”