Abstract
In recent years, there has been a growing emphasis on Autism Spectrum Disorder (ASD), marked by evolving classifications and increasing global prevalence. Early intervention is underscored as crucial for effective management of ASD, given that emotional dysregulation is a prominent characteristic, leading to challenges in regulating and expressing emotions in a typical manner. This study aims to propose the utilization of Empatica E4 (at the physiological level) and Face tracking (at the behavioral level) as a real-time emotion detection system, exploring its potential impact on interventions based on the Pivotal Response Treatment method (PRT) for children with autism. The goal is to enhance understanding of the child’s emotions, facilitate communication, strengthen emotional connections during interventions, and promote strategies for managing intense emotions.
Integrating a real-time emotion detection system into PRT interventions holds promise for significantly enhancing the therapeutic process. By providing valuable insights into the child’s emotional state, this approach offers a promising avenue to tailor treatments to individual needs and provide greater emotional support and social development for children with ASD.
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Acknowledgements
This project has received funding by grant PID2020-115220RB-C22 funded by MCIN/AEI/ 10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR”, and was funded in part by grants DTS19/00175 and PDC2022-133952-100 from the Spanish “Ministerio de Ciencia, Innovación y Universidades” and by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 899287 (NeuraViPeR), as well as the “Premio Tecnologías Accesibles de Indra y Universia Fundación”.
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Benedicto, G., la Paz, F.d., Fernández-Caballero, A., Fernandez, E. (2024). Real-Time Emotion Detection System’s Impact on Pivotal Response Training Protocol. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_34
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