Using Wearable Devices and Machine Learning to Forecast Preschool Tantrums and Identify Clinically Significant Variants

Abstract

Difficulty differentiating early symptoms of psychopathology from normative misbehavior is a significant challenge for researchers, providers, and caregivers, and is a longstanding obstacle to more effective early assessment and intervention. The proposed study will use wearable devices and a machine learning approach to record naturally occurring tantrums in preschool children in order to identify early psychopathology and predict tantrums before they occur. The anticipated products of this project will be algorithms designed to shift the field toward the development of automated, home-based, early mental health detection and intervention