Abstract
People with Autism Spectrum Disorder (ASD) show a great heterogeneity in their atypical sensory behaviours due to they often suffer from a Sensory Processing Disorder (SPD). This nervous system condition is associated with the social interaction, learning and behavioural problems experienced by people with ASD. This work shows a study conducted in a clinical scenario where the participating users carry out tasks to improve their skills. During the execution of these activities, data about the physiological state of the user and the stimuli present in the environment is recorded using an electronic monitoring platform for people with ASD. From the acquired signals by the devices, different features are created, and a dataset is built with them. Finally, information is extracted from the dataset using machine learning techniques to try to relate changes in the user’s state with environmental stimuli.
This work was partially funded by Spanish Research State Agency and European Regional Development Fund through “Race” Project (PID2019-111023RB-C32). The work of J.M.V.-S. is supported by the Conselleria d’Educació, Investigació, Cultura i Esport (GVA) through FDGENT/2018/015 project.
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Acknowledgements
The authors would like to thank the participants and their families for their participation in the study. In addition, they would like to thank the collaboration of the professionals of the Clínica Universitaria.
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Vicente-Samper, J.M., Ávila-Navarro, E., Sabater-Navarro, J.M. (2022). Feasibility Study of a ML-Based ASD Monitoring System. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_27
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DOI: https://doi.org/10.1007/978-3-031-06242-1_27
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