Feasibility of forecasting self-injurious behavior among autistic youth using wearable sensors and machine learning models

Abstract

Self-injurious behavior (SIB) is a substantial clinical challenge for many individuals on the autism spectrum, and support strategies are often only reactive. Forecasting of SIB could enable timely support, especially using wearable sensors, but its feasibility is not well understood. We evaluated SIB forecasting using a previously collected dataset (n = 9) comprising motion and physiological data. We compared the performance of four machine learning models-Random Forest, AdaBoost.M2, Long Short-Term Memory (LSTM), and a Double-Stacked LSTM-across five forecast horizons (3s to 120s) and three feature sets: Motion-Only (from accelerometers), Physiological-Only (e.g., heart rate, skin conductance), and Combined. Performance was measured with a range of metrics, using Leave-One-Subject-Out cross-validation. We found a significant main effect of forecast horizon on the Area Under the Precision-Recall Curve; performance rose from near-chance at short horizons to having median scores above chance at one minute or longer. While aggregated results showed no significant differences between models or feature sets, subject-level analysis suggested predictive feasibility and that the optimal model configuration were highly person-specific. Our findings demonstrate that forecasting using wearable sensor data is feasible, but the substantial performance variability highlights a critical need for person-specific approaches to enable the development of clinically useful, proactive support systems.