Year: 2014
Type: conference-paper
Abstract: A lack of control over their autonomic nervous system presents a major challenge for many children with Autism Spectrum Disorder (ASD). Autonomic biofeedback training is a promising treatment for managing anxiety and ASD symptoms more generally. We describe software that tunes four autonomic measurements to the best abilities and needs of each individual patient. Using this dynamic feedback signal set (DyFSS), a strength-based, self-customizing algorithm, we aim to address the autonomic heterogeneity of youth with ASD. The DyFSS may improve autonomic biofeedback training for the user by making it more understandable and easier to accomplish. Because it is self-adjusting, it may also ease the integration of autonomic biofeedback training into clinical work. Initial feasibility testing of this algorithm in youth with ASD with a five-session autonomic biofeedback training protocol showed improved behavior in relation to ASD symptoms Initial reactions show that youth with ASD are readily engaged through technological interventions such as autonomic biofeedback. Keywords— autism spectrum disorder; autonomic
Institution: Rochester Institute of Technology
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W1608422040