Abstract
Autism spectrum disorder (ASD) is a condition that is being diagnosed in a growing portion of the population. ASD represents a range of complex disorders with a number of symptoms including social difficulties and behavioral issues. Some individuals suffering from ASD are prone to incidents of agitation that can lead to escalation and meltdowns. Such incidents represent a risk to the individuals with ASD and others who share their environment. This paper introduces a novel approach to monitor triggers for these incidents with an aim to detect and predict an incident happening. Non-invasive sensors monitor factors within an environment that may indicate such an incident. Combined with an NFC and smart phone based mechanism to report incidents in a relatively friction free manner. These reports will be combined with sensor records to train a prediction system based on supervised machine learning. Future work will identify the best performing machine-learning technique and will evaluate the approach.
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Notes
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Electroencephalography - recording electrical brain activity through electrodes on the scalp.
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Acknowledgments
The authors gratefully acknowledge the contributions of Dr. Jonathan Synnott for his exemplary work on the thermal vision processing core used in this approach. This work received support from Invest NI under the Competence Centre Programme Grant RD0513853.
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Rafferty, J., Synnott, J., Nugent, C. (2015). An Approach for Agitation Detection and Intervention in Sufferers of Autism Spectrum Disorder. In: Cleland, I., Guerrero, L., Bravo, J. (eds) Ambient Assisted Living. ICT-based Solutions in Real Life Situations. IWAAL 2015. Lecture Notes in Computer Science(), vol 9455. Springer, Cham. https://doi.org/10.1007/978-3-319-26410-3_13
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DOI: https://doi.org/10.1007/978-3-319-26410-3_13
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