Automatic Detection of Pain in Autism Using Physiological Measurements
Pain sensitivity is a prevalent sensory processing abnormality in autism spectrum disorder (ASD). While some individuals with ASD are intolerant of seemingly unremarkable sensations, others can be completely unresponsive and leave medical issues unattended. Pain is often accompanied by a heightened response in the autonomic nervous system. Thus, physiological measurements (such as cardiac activity) can be used as an objective proxy for self-reports to automatically detect a state of pain. Physiological measurements can be acquired using wearable sensors to detect changes in sensory state and may aid in detecting heightened sensory states in ASD in everyday settings.
The student will be responsible for developing the necessary analytical methods to classify changes in physiological measurements to automatically detect changes in sensory state in ASD (response to a noxious stimulus vs neutral baseline). The student will 1) inspect the data to determine intervals of usable and unusable data 2) implement and evaluate the necessary signal preprocessing algorithm to automatically remove unusable data due to artifacts and 3) develop an artificial intelligence algorithm to automatically detect changes in sensory state across individuals with ASD.
Strong signal processing signals.
Larissa Schudlo : Automatic Detection of Pain in Autism Using Physiological Measurements | Tuesday March 27th 2018 03:48 PM