Luis Márquez-Carpintero

Luis Marquez-Carpintero, Francisco Gomez-Donoso, Zuria Bauer et al.

This paper describes an artificial intelligence-based assistant application, AIDEN, developed during 2023 and 2024, aimed at improving the quality of life for visually impaired individuals. Visually impaired individuals face challenges in identifying objects, reading text, and navigating unfamiliar environments, which can limit their independence and reduce their quality of life. Although solutions such as Braille, audio books, and screen readers exist, they may not be effective in all situations. This application leverages state-of-the-art machine learning algorithms to identify and describe objects, read text, and answer questions about the environment. Specifically, it uses You Only Look Once architectures and a Large Language and Vision Assistant. The system incorporates several methods to facilitate the user's interaction with the system and access to textual and visual information in an appropriate manner. AIDEN aims to enhance user autonomy and access to information, contributing to an improved perception of daily usability, as supported by user feedback.

Luis Marquez-Carpintero, Sergio Suescun-Ferrandiz, Carolina Lorenzo Álvarez et al.

In this paper, a novel dataset is introduced, designed to assess student attention within in-person classroom settings. This dataset encompasses RGB camera data, featuring multiple cameras per student to capture both posture and facial expressions, in addition to smartwatch sensor data for each individual. This dataset allows machine learning algorithms to be trained to predict attention and correlate it with emotion. A comprehensive suite of attention and emotion labels for each student is provided, generated through self-reporting as well as evaluations by four different experts. Our dataset uniquely combines facial and environmental camera data, smartwatch metrics, and includes underrepresented ethnicities in similar datasets, all within in-the-wild, in-person settings, making it the most comprehensive dataset of its kind currently available. The dataset presented offers an extensive and diverse collection of data pertaining to student interactions across different educational contexts, augmented with additional metadata from other tools. This initiative addresses existing deficiencies by offering a valuable resource for the analysis of student attention and emotion in face-to-face lessons.

Luis Marquez-Carpintero, Sergio Suescun-Ferrandiz, Monica Pina-Navarro et al.

The monitoring and prediction of in-class student activities is of paramount importance for the comprehension of engagement and the enhancement of pedagogical efficacy. The accurate detection of these activities enables educators to modify their lessons in real time, thereby reducing negative emotional states and enhancing the overall learning experience. To this end, the use of non-intrusive devices, such as inertial measurement units (IMUs) embedded in smartwatches, represents a viable solution. The development of reliable predictive systems has been limited by the lack of large, labeled datasets in education. To bridge this gap, we present a novel dataset for in-class activity detection using affordable IMU sensors. The dataset comprises 19 diverse activities, both instantaneous and continuous, performed by 12 participants in typical classroom scenarios. It includes accelerometer, gyroscope, rotation vector data, and synchronized stereo images, offering a comprehensive resource for developing multimodal algorithms using sensor and visual data. This dataset represents a key step toward scalable solutions for activity recognition in educational settings.