Predicting Onset of Aggression in Minimally Verbal Youth with Autism Using Biosensor Data and Machine Learning Algorithms
Predicting Onset of Aggression in Minimally Verbal Youth with Autism Using Biosensor Data and Machine Learning Algorithms
Predicting Onset of Aggression in Minimally Verbal Youth with Autism Using Biosensor Data and Machine Learning Algorithms is a project funded by [Simons18, Army18]. This project team consists of researchers at Maine Medical Center Research Institute, University of Pittsburgh Medical Center, and Northeastern, including Matthew Siegel, Matthew Goodwin, Stratis Ioannidis, and Deniz Erdogmus, who collectively bring expertise in autism spectrum disorders, wearable sensors for monitoring and predicting behavior, machine learning, and multimodal sensor fusion. The consortium is collecting a large scale multimodal dataset from minimally verbal children with Autism and developing sensor fusion algorithms to predict upcoming aggressive behavior onset, which is a significant detrimental factor in these individuals participating successfully in social engagements with family, friends, and caregivers.
Affiliated Faculty: Deniz Erdogmus in partnership with Matthew Goodwin (College of Health Sciences, College of Computer Science) and Stratis Ioannidis (Electrical + Computer Engineering, WIoT).
