Please use this identifier to cite or link to this item: https://accedacris.ulpgc.es/handle/10553/139734
Title: AI-Driven Gesture and Action Recognition for Learning Medicine Through Virtual Reality
Authors: Arias Ruiz-Esquide, Daniel 
Reyes Cabrera, José Juan 
Hernández Guedes, Abián 
Trujillo-Pino, Agustín 
Rodríguez-Florido, Miguel Angel 
UNESCO Clasification: 33 Ciencias tecnológicas
Keywords: Action Prediction
Artificial Intelligence
Gesture Recognition
Immersive Technologies
Medical Education, et al
Issue Date: 2025
Journal: Lecture Notes in Computer Science 
Conference: 19th International Conference on Computer Aided Systems Theory, EUROCAST 2024 
Abstract: Virtual reality (VR) has emerged as a prominent immersive technology, demonstrating significant potential for enhancing education and has been shown as an effective and efficient tool specifically for medical education. VR requires the utilization of dedicated wearable devices that, when worn by students, facilitate the creation of an immersive virtual environment and enable them to interact with both other students and virtual objects within it. However, the use of these gadgets may produce a distortion of the gestures and actions actually performed by the student, causing errors within the environment due to the technology itself and not the student’s mistaken intention. In medical education, this effect of convoluting the student’s own intention with what the hardware interface might limit is of vital importance, as it may result in a, medically speaking, erroneous action or gesture in the virtual scenario. Therefore, in medical education, virtual reality applications should be smart and able to correct these technology-related issues and limitations. In this work, we present our analysis, strategy, and approach to propose AI-based solutions that minimize this limitation in VR-based medical education, describing some experiments that we will conduct with medical students at our University to improve the response of virtual reality in medical learning evaluation.
URI: https://accedacris.ulpgc.es/handle/10553/139734
ISBN: 9783031829598
ISSN: 0302-9743
DOI: 10.1007/978-3-031-82957-4_8
Source: Lecture Notes in Computer Science[ISSN 0302-9743],v. 15173 LNCS, p. 77-87, (Enero 2025)
Appears in Collections:Actas de congresos
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