This work illustrates the use and some results of artificial neural networks (ANNs) for data quality control of air pollutants. ANNs are applied to the short-term predicting of air pollutant concentrations in urban areas. Observed versus predicted data are compared to test the efficacy of ANNs in simulating environmental processes. Statistical analysis is used for choice of neural structure. The model is validated on original data.

An engagement of humanoid social robots in special education may raise ethics questions. Hence, the development of useful therapeutic treatments may be hindered. To circumvent the latter, we propose training a humanoid robot R1 toward simulating the behavior of a child. Then, use another humanoid robot R2 to operate on robot R1 toward developing an effective treatment in special education without raising ethics questions. Experiments here consider children with autism. Preliminary application results have been encouraging. This work also suggests a novel social robot modeling based on gestalt representations.

Using robots to reward and stimulate children doing tasks together can be helpful in improving their social skills. Robots implemented 3 types of behavior scenarios - imitate, enhance and counteract - in a collaborative game of teaching robots perform movements shown by hand gestures. The paper presents a novel account of ASD symptoms based on the interplay of empathic concern and empathic accuracy in different personalities with and without cognitive deficits. Performance of autistic children was compared with two age groups of typically developing children - under and above the age of 7. The study confirmed the limited resource hypothesis - cognitive and motivational - bringing about the observed phenomena in situations of limited attention resources and performance under distraction. The robotic framework for educating children has shown potential for developing of various complex cognitive and social skills and has substantial developmental impact.

This research aims to improve the pedagogical rehabilitation of children with Special Educational Needs (SEN) based on play-like activities for orientation in space, shapes, colors and emotions. The designed EEG-based portable Brain-Computer Interface (BCI) measures and features in real time the brain signals in order to analyze the attentional or emotional states of a child. The output performance scores are used either as an input control in the play scenarios or to facilitate the assessment of the emotional states and cognitive performance of a child by special educators. The proposed integration of BCI with programmable robots assists in creating an engaging environment for learning skills by imitation and forming alternative BCI training session adapted to children with SEN. The concept of added BCI into the play-like activities evolves into technology for self-regulatory training of emotional or cognitive skills via neurofeedback exposed on the robot. A low resolution EMOTIV brain-listening headset is used to translate the current child brain activity into robot commands in real time. Then, they are wirelessly transmitted to robot sensors, modules and controllers and consequently used as an input neural control. Thus, the attention or positive emotional responses of children with SEN are reinforced.

Editorial: Emerging technologies for assistive robotics: current challenges and perspectives