The study of cognitive responses and processes while using applications is a critical field in human–computer interaction. This paper aims to determine the mental effort required for different typical tasks with smartphones. Mental effort is typically associated with the concept of cognitive load, and has been studied by analyzing electroencephalography (EEG) signals. Thus, this paper shows the results of analyzing the cognitive load of a set of characteristic tasks on smartphones. To determine the set of tasks to analyze, this paper proposes a taxonomy of smartphone-based actions defined after considering the related proposals in the literature and identifying the significant characteristics of the tasks to classify them. The EEG data was obtained through an experiment with real users doing tasks from the aforementioned taxonomy. The results show significant differences in the cognitive load of each task category and identify those tasks that involve a higher degree of mental effort. The results will be the starting point of the M⁴S project that aims to contribute to the early diagnosis of mild cognitive impairment through monitoring everyday dual-tasking in terms of interaction with smartphones.

Prolonged incorrect sitting postures can lead to various health issues, including musculoskeletal disorders and reduced quality of life. Efficient and accurate methods for monitoring and assessing sitting postures are crucial for promoting overall well-being in our increasingly sedentary society. This study compares the performance of Kolmogorov-Arnold Networks (KANs) and Multilayer Perceptrons (MLPs) in sitting posture recognition, utilizing a multi-task classification approach to simultaneously identify upper and lower body positions. A comprehensive dataset of sitting postures was developed using MediaPipe for skeletal extraction, labeled for both body regions, and encompassing a range of common scenarios. The models were implemented and evaluated using Leave-One-Subject-Out (LOSO) cross-validation to assess generalization capability across different individuals. Results indicated that the KAN model achieved higher accuracy (97.03% for upper body and 92.11% for lower body) with fewer parameters (4,320) compared to the MLP (93.87% and 91.47% respectively, with 7,662 parameters). Although the MLP demonstrated faster inference, both models achieved accuracies exceeding 91% in LOSO validation, suggesting their potential for real-time applications. This study significantly contributes to the field of automatic posture recognition, offering insights into the applicability of KANs and MediaPipe-based pose estimation in ergonomic recognition systems and demonstrating the effectiveness of a multi-task approach for comprehensive sitting posture analysis.

Video games and electroencephalography (EEG) can be used together in more than one way: cognitive analysis, mood analysis or Brain-Computer Interfaces (BCI), for instance. Nowadays, these two fields are gaining popularity when working together. We have consider that it is important to know what approaches are the most used when using video games and EEG, so we have performed a systematic review through the literature about these two fields together to find the most relevant techniques. Once identified a list of techniques, we briefly explained for what they can be used. Also, we have made a ranking of these techniques by their popularity to get some perspective about them all. After the process, we have concluded that the used techniques are really diverse and that approaches used to BCI and cognitve analysis are very similar between them.

Gait analysis has proven to be a key process in the functional assessment of people involving many fields, such as diagnosis of diseases or rehabilitation, and has increased in relevance lately. Gait analysis often requires gathering data, although this can be very expensive and time consuming. One of the main solutions applied in fields when data acquisition is a problem is augmentation of datasets with artificial data. There are two main approaches for doing that: simulation and synthetic data generation. In this article, we propose a parametrizable generative system of synthetic walking simplified human skeletons. For achieving that, a data gathering experiment with up to 26 individuals was conducted. The system consists of two artificial neural networks: a recurrent neural network for the generation of the movement and a multilayer perceptron for determining the size of the segments of the skeletons. The system has been evaluated through four processes: (i) an observational appraisal by researchers in gait analysis, (ii) a visual representation of the distribution of the generated data, (iii) a numerical analysis using the normalized cross-correlation coefficient, and (iv) an angular evaluation to check the kinematic validity of the data. The evaluation concluded that the system is able to generate realistic and accurate gait data. These results reveal a promising path for this research field, which can be further improved through increasing the variety of movements and the user sample.