In this project, we aim to control the position of a robotic arm for surgical tasks using coordinates obtained through computer vision with a synchronous 3D stereo VR USB camera. The precision of the tool center point (TCP) positioning is evaluated using experimental methodologies for camera calibration and stereoscopy, and validated through real-world measurement tests. Once the x-y-z coordinates and the manipulator’s kinematics are known, any task requiring robotic control can be executed without the user needing to manually specify the desired TCP position. This methodology could be integrated as part of the control algorithm for a robotic arm designed to assist or perform surgical procedures.

Background/Objectives: Fear detection through EEG signals has gained increasing attention due to its applications in affective computing, mental health monitoring, and intelligent safety systems. This systematic review aimed to identify the most effective methods, algorithms, and configurations reported in the literature for detecting fear from EEG signals using artificial intelligence (AI). Methods: Following the PRISMA 2020 methodology, a structured search was conducted using the string (“fear detection” AND “artificial intelligence” OR “machine learning” AND NOT “fnirs OR mri OR ct OR pet OR image”). After applying inclusion and exclusion criteria, 11 relevant studies were selected. Results: The review examined key methodological aspects such as algorithms (e.g., SVM, CNN, Decision Trees), EEG devices (Emotiv, Biosemi), experimental paradigms (videos, interactive games), dominant brainwave bands (beta, gamma, alpha), and electrode placement. Non-linear models, particularly when combined with immersive stimulation, achieved the highest classification accuracy (up to 92%). Beta and gamma frequencies were consistently associated with fear states, while frontotemporal electrode positioning and proprietary datasets further enhanced model performance. Conclusions: EEG-based fear detection using AI demonstrates high potential and rapid growth, offering significant interdisciplinary applications in healthcare, safety systems, and affective computing.

The proposal is focused on the development of an operating system that reduces the gap in the needed technological knowledge for the installation of an intelligent environment. The aim is to develop an operating system that is sufficiently advanced to autonomously manage the inclusion of new devices, and that at hardware level becomes largely versatile to integrate new devices and components into the environment. The proposal is based on modular hardware composed on three main elements: a brain, a module (a transductor) and a power supply. Consequently, the software must be able to recognize the installed hardware and subsequently manage communication with other devices with minimal human intervention, being helped by algorithms and fuzzy logic. Therefore, the contribution focuses on the creation of ubiquitous and pervasive systems, where the system manages itself and benefit.

In this work, preliminary research was carried out considering Smart Hospitals as antecedents, with the purpose of developing a proposal for the automation of a hospital-type room. This room will have the purpose of monitoring bedridden patients, through the paradigm of intelligent agents and will be able to contribute to improving patient care and supporting the main caregiver in this arduous task.

This paper presents a technical approach to the design of a cost-effective BCI platform from the hardware construction based on commercial components to the firmware. This device may include drivers for signal acquisition, preprocessing, artifact removal, band separation, feature extraction, and classification via compressed ANN with the TensorFlowLite framework. It consists of an STM32-based EEG platform built-oriented as a tool for BCI experiments in the constraints of education and research fields, capable of sending data to PC or mobile devices via a wireless connection, allowing external processing capabilities alongside the main feature of embedded signal classification (TinyML) focused on early detection by discriminating children with ASD from non-ASD ones.

This paper proposes the creation of an intelligent system capable of differentiating between basic emotions as a result of identifying micro expressions in children with Autism Spectrum Disorder (ASD). This system will be developed with the aim of serving as a tool for therapists and caregivers of children with ASD; so that, when the child expresses an emotion that can’t be readily identified, the system can recognize it and allow the therapist or caregiver to give the child the necessary tools to help them understand their emotional state. This way they can process the emotion, regulate it, and if necessary, express it in a better way. All with the goal of improving the emotional intelligence of the child. The model was trained with Google’s Teachable Machine software using a dataset that contains images of micro-expressions referencing six basic emotions provided by the Chinese Academy of Sciences Micro-expression (CASME) Databases.

Sierra, Francisco Baltazar, Rosario Pineda, Anabel Casillas, Miguel-’Angel Díaz, Claudia Rocha, Martha-AliciaThe potential use of EEG data along with a Multi-Agent System can offer great benefits to the medical and technological area, and this conjunction is used to provide electronic device control through a BCI that can contribute to elderly people or with motor disabilities as well. This work-in-progress paper focuses primarily on the feature discrimination of an EEG dataset that follows the motor-imaginary paradigm, by applying classification techniques and comparing the accuracy between them will allow us to select the best technique to identify between four different classes, finally those trained datasets will serve as supervised learning data, as reference for real-time EEG signal acquisition, and to program commands.

In the field of neuropsychology, it is common to use physical, analog tests to evaluate different cognitive functions. Due to recent world events, the need for technologies to adapt and contribute with digital adaptations that can be relied upon has increased. In this research project, a small team of researchers combined efforts to develop software that could accurately measure outcomes from the Tower of Hanoi test, which include the number of moves and the time taken to finish the task, so that it could be used safely, efficiently, and remotely. While the digital adaptation served its purpose, there appears to be a subjective component that some could find more satisfactory in the physical implementation, although the efficacy of the test remained high.

Quality control at industry is an important factor to find the desired specifications of the manufactured products, this is often achieved manually with the support of the work staff. It implies that some defects are not detected by workers due to fatigue. This article describes a technological proposal, applied to the quality control of injected parts, using vision machine to give support to quality control. In this article we achieve obtain a set of PVC tee in a previously were environment it for four different faces and two types of parts found: defective and non-defective. Then image processing where we obtain the characteristic vector and using feed forward artificial neuronal network based on a multilayer perceptron mentioned images are classified account to the type expected. An n training procedure and is performance to classifi images are discussed in detail in the contect of quality control.

The present paper analyzes one of the latest NLP Google Bert algorithms in a particular use case that allows business administrators to not only gather useful business statistics for managing the Data Manipulation Languages, but it is also capable of managing the Data Definition Languages in a flexible ERP system by voice command entry, with more than only English languages for voices entries to be analyzed with Google Bert NLP. supporting automatic vertical and horizontal data growth considering the infrastructure complexity in manufacturing organization in the industry 4.0 and their considerations and inconveniences to be implement.

This article presents the analysis and design of the intelligent agent model “IA-ACR”, which has the objective of monitoring movements, which are carried out in a coordinated and intelligent way in a robot, which will have the task of performing routines of physical exercises and dance, these routines will then be imitated by children with neurodevelopmental disorders (NDD), in order to capture their attention so that therapies are more effective, which will be evaluated by the specialist (psychologist). Due to the current situation of the pandemic that is being experienced due to COVID-19, health protocols were established, such as avoiding contact between people, given this restriction, a digital platform was developed that serves as support for children in order to receive their sessions, where the robot appears through videos, this being an advantage of telehealth.

Intelligent buildings are at the forefront due to its main objective of providing comfort to users and saving energy through intelligent control systems. Intelligent systems have been reported to offer comfort to a single user or averaging the comfort of multiple users without considering that their needs may be different from those of other users. This work defines a versatile model for a multi-user intelligent system that negotiates with the resources of the environment to offer visual comfort to multiple users with different profiles, activities and priorities using soft-computing algorithms. In addition, this model makes use of external lighting to provide the recommended amount of illumination for each user without having to totally depend on artificial lighting, inducing there will be an energy efficiency but without measuring it.

This paper presents the design and implementation of a speech recognition software module for a smart closet. This is a work in progress, and it is focusing on people with visual disabilities as an assistive technology for the storage, searching, and extraction of their garments. Basically, through this human–computer interface, the users use speech to make requests. These instructions are translated into commands or actions for the smart closet such as insertion of garments, search for garments by description, and/or extract their clothes from their intelligent wardrobe. The speech recognition software module has based its development on agents and is centered on Web-based environments as a user interface. The preliminary results show the proposal’s potential and highlights the contributions: improving the quality of life for people with vision disabilities and integrating them into the digital age in the near future.

This working in progress will focus on a certain sector of the population such as the elderly people, who face difficulties in their cognitive and physical capacities declining as the years progress, it is essential to provide better living conditions through the use of technology that must be friendly with users. The development will be of a web system with agents and include sensors in the room to regulate the temperature, light, and a bracelet-shaped sensor to detect the user’s heart rate, the use of algorithms is proposed of learning and proactive stage adapted with classification algorithms to measure the efficiency with the best performance.

This working progress paper will focus on determining the extent to which the Electroencephalogram (EEG) signal can be subjected to treatment and classification techniques in order to determine whether it is possible to differentiate between language disorders, as well as learn more about the behavior of these language alterations at the brain level, and provide a tool to support the medical diagnosis. We have established the hypothesis that, through a Brain Computer Interface (BCI) as well as through EEG signal treatment and classification techniques, in conjunction with the application of medical Neurofeedback techniques, and identify relevant information that allows grouping of language disorders; this by measuring concentration levels among patients with these conditions.

En este artículo se presenta un estudio de la clasificación del grado de afectación en pacientes diagnosticados con la enfermedad de Parkinson. La captura de las señales de vibración de vibración se realiza usando un acelerómetro de una pulsera bluetooth. El procesamiento de las señales de vibración se realiza procesando el valor rms y valores pico de las señales de los pacientes. Finalmente se utilizan diferentes clasificadores para evaluar el grado de afectación de la vibración en pacientes con Parkinson.

The realization of activities in daily life can generate cognitive processes such as paying attention and having meditation to what is done, and with the use of these abilities, it is sought to obtain better results or to carry out the desired activity of the best possible way. Although people have the same capacities, there are activities where there may be differences between men and women or vice versa, for this reason, it is important to consider through gender, what are the attention level and the level of meditation presented during the performance of a specific activity. With this information, clustering of the elements is applied to visualize different levels and how it behaves in each gender, as well as in general.

The technology just like exact sciences and computation take an important role in our daily life; the union of these generate applications with great utility and provide efficient communication alternatives; thinking in this objective the ConsultApp is developed with the purpose to give a communication alternative to verbally disabled people, with the benefit of expressing and transmitting their discomforts as well as medical conditions, through the use of an EEG (Electroencephalogram).

One of the primary concerns of humanity today is developing strategies for saving energy and promoting environmental sustainability. This paper suggests the development of an intelligent Internet of Things based system with the use of meta-heuristics that will be able to find optimal energy saving configurations. This system takes into account the activity of the users, size of area, state of lights, and blinds. A comparative study of four optimization techniques (GA, PSO, DBDE, and BSO) with the use of the Friedman test is shown.