The aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in terms of optimizing the path selection. The second improvement uses fuzzy inference to optimize each of the fixed parameters’ values to increase the algorithm performance. Nevertheless, a simple fuzzy inference system (FIS) was not used for this particular study; instead, an artificial neuro-fuzzy inference system (ANFIS) was used, thus making it possible to develop a FIS system with a back-propagation technique. Each parameter would have a particular ANFIS, in order to modify the ${\mathsf{\alpha }}_{\mathrm{D}}$ , ${\mathsf{\beta }}_{\mathrm{D}}$ , and ${\mathsf{\gamma }}_{\mathrm{D}}$ parameters individually. At the end of the article, different scenarios are analyzed to determine whether the developments in this article have improved the DWA behavior. The results show that the prediction step and ANFIS adapt DWA performance by optimizing the path resolution.

Stress is a prevalent global concern impacting individuals across various life aspects. This paper investigates stress detection using electroencephalographic (EEG) signals, which have proven valuable for studying neural correlates of stress. Stress was induced in students, and physiological data was recorded as part of the experimental setup. Different feature sets were extracted and four machine learning models, including LightGBM, Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), were utilized for classification tasks. The findings indicate that the mean and standard deviation of 19 channels consistently outperform other feature sets. LightGBM demonstrates superior performance across all scenarios compared to CNN, KNN, and SVM. Overall, this study presents an effective stress detection approach using EEG signals and demonstrates the potential of integrating simple statistical features for enhanced classification accuracy. The findings contribute to the advancement of stress monitoring technologies, with potential applications in wearables and BCIs for real-time stress management.

Nowadays, considering society’s highly demanding lifestyles, it is important to consider the usefulness of relaxation from the perspective of both psychology and clinical practice. The response towards relaxation (RResp) is a mind-body interaction that relaxes the organism or compensates for the physiological effects caused by stress. This work aims to automatically detect the different mental states (relaxation, rest and stress) in which RResps may occur so that complete feedback about the quality of the relaxation can be given to the subject itself, the psychologist or the doctor. To this end, an experiment was conducted to induce both states of stress and relaxation in a sample of 20 university students (average age of $$25.76\pm 3.7$$25.76 ± 3.7 years old). The electrocardiographic and electrodermal activity signals collected from the participants produced a dataset with 1641 episodes or instances in which the previously mentioned mental states take place. This data was used to extract up to 50 features and train several supervised learning algorithms (rule-based, trees, probabilistic, ensemble classifiers, etc.) using and not using feature selection techniques. Besides, the authors synthesised the cardiac activity information into a single new feature and discretised it down to three levels. The experimentation revealed which features were most discriminating, reaching a classification average accuracy of up to $$94.01\pm 1.73$$94.01 ± 1.73 % with the 6 most relevant features for the own-collected dataset. Finally, being restrictive, the same solution/subspace was tested with a dataset referenced in the bibliography (WESAD) and scored an average accuracy of $$90.36\pm 1.62$$90.36 ± 1.62 %.