Multi-level modeling with nonlinear movement metrics to classify self-injurious behaviors in autism spectrum disorder.

ABSTRACT: Self-injurious behavior (SIB) is among the most dangerous concerns in autism spectrum disorder (ASD), often requiring detailed and tedious management methods. Sensor-based behavioral monitoring could address the limitations of these methods, though the complex problem of classifying variable behavior should be addressed first. We aimed to address this need by developing a group-level model accounting for individual variability and potential nonlinear trends in SIB, as a secondary analysis of existing data. Ten participants with ASD and SIB engaged in free play while wearing accelerometers. Movement data were collected from > 200 episodes and 18 different types of SIB. Frequency domain and linear movement variability measures of acceleration signals were extracted to capture differences in

SUBMITTER: Cantin-Garside KD 

PROVIDER: S-EPMC7542156 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature