Autism spectrum disorder (ASD) is a condition that is being diagnosed in a growing portion of the population. ASD represents a range of complex dis-orders 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 intro-duces 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 pre-diction system based on supervised machine learning. Future work will identify the best performing machine-learning technique and will evaluate the approach.
|Title of host publication||mbient Assisted Living and Daily Activities|
|Publication status||Published - 9 Dec 2015|
- Assistive technologies
- Computer Vision
- Machine learning
Rafferty, J., Synnott, J., & Nugent, C. (2015). An approach for agitation detection and intervention in sufferers of autism spectrum disorder. In mbient Assisted Living and Daily Activities (pp. 127-132). Springer. http://uir.ulster.ac.uk/32590/1/An_approach_for_agitation_detection_and_intervention_in_sufferers_of_autism_spectrum_disorder.pdf