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 behaviors, and metrics of nonlinear movement variability were used to quantify the complexity of SIB. The multi-level logistic regression model, comprising of 12 principal components, explained > 65% of the variance, and classified SIB with > 75% accuracy. Our findings imply that frequency-domain and movement variability metrics can effectively predict SIB. Our modeling approach yielded superior accuracy than commonly used classifiers (~ 75 vs. ~ 64% accuracy) and had superior performance compared to prior reports (~ 75 vs. ~ 69% accuracy) This work provides an approach to generating an accurate and interpretable group-level model for SIB identification, and further supports the feasibility of developing a real-time SIB monitoring system.
Highlights
Self-injurious behavior (SIB) is among the most dangerous concerns in autism spectrum disorder (ASD), often requiring detailed and tedious management methods
Earlier findings support the feasibility of tracking behaviors in ASD, stereotypical motor movements (SMM) such as hand-flapping or rocking, which may relate to SIB and be repetitive and r hythmic[2]
Fifty-nine variables were selected from lasso and were input in Principal component analysis (PCA) for the group-level multilevel logistic regression (MLR) model; these variables included both linear and nonlinear features of motor variability features from each sensor channel
Summary
Self-injurious behavior (SIB) is among the most dangerous concerns in autism spectrum disorder (ASD), often requiring detailed and tedious management methods. Nonwearable and wearable technologies, such as embedded camera systems or accelerometers in everyday items (e.g., cellphones), could record data continuously for SIB monitoring without requiring high levels of caregiver or clinician c ompliance[19,20]. The former study extended models that were trained on imitated movement to one child with SIB, and found that classification with individual accelerometry data yielded accuracies on the order of 60–70%18. Naturally-collected episodes of aggression were classified with high accuracy using physiological and movement sensors (area under the curve: AUC = 71–80 for individuals; AUC = 69 for group performance), though SIB was not included in the activities of interest[30]. Sensory aversions prevalent in SIB31 may preclude the physiological sensors that require skin contact, which were used in Ozdenizci et al.[30], so other sensor and classification methods may be preferable for our application
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