Citas bibligráficas
Baquerizo, M., (2024). Uso de sensores biométricos en la investigación del estrés y la función autonómica de personas con condiciones del espectro autista [Universidad Peruana Cayetano Heredia]. https://hdl.handle.net/20.500.12866/15417
Baquerizo, M., Uso de sensores biométricos en la investigación del estrés y la función autonómica de personas con condiciones del espectro autista []. PE: Universidad Peruana Cayetano Heredia; 2024. https://hdl.handle.net/20.500.12866/15417
@mastersthesis{renati/911257,
title = "Uso de sensores biométricos en la investigación del estrés y la función autonómica de personas con condiciones del espectro autista",
author = "Baquerizo Sedano, Marilia Lucia",
publisher = "Universidad Peruana Cayetano Heredia",
year = "2024"
}
Differences in autonomic function have been described in people with autism spectrum conditions during rest and stress. These differences may be associated with atypical sensory processes and mental health conditions, and would have a cascading effect on the development of other features of autism. The present narrative review aims to synthesize the available scientific literature on the use of biometric sensors in the investigation of stress and autonomic function of people with autism spectrum conditions. The electronic databases Scopus, Web of Science, PubMed and Medline were searched for articles published in English from January 1, 2000 to April 1, 2023. Based on the studies reviewed, it is concluded that autonomic function in Autism is heterogeneous, there is evidence of several atypical profiles, the most frequent being hyperarousal/hyperarousal and sympathetic overexcitation with low parasympathetic influence under stress conditions. Studies are required with larger samples, in everyday settings, with records of longer duration and that complement physiological information with clinical measures and data from multiple levels of analysis. Devices with biometric sensors constitute a great opportunity to carry out these studies; To accurately detect stress, it is convenient to analyze signals such as cardiac measurements (HR, HRV), electrodermal activity (EDA), and skin temperature (SKT) together, and build a robust model with learning techniques automatic.
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