From motion to emotion: exploring challenging behaviors in autism spectrum disorder through analysis of wearable physiology and movement.

Objective: This study aims to evaluate the efficacy of wearable physiology and movement sensors in identifying a spectrum of challenging behaviors, including self-injurious behavior (SIB), in children and teenagers with autism spectrum disorder (ASD) in real-world settings.

Approach: We utilized a long-short-term memory (LSTM) network with features derived using the wavelet scatter transform to analyze physiological biosignals, including electrodermal activity and skin temperature, alongside three-dimensional movement data captured via accelerometers. The study was conducted in naturalistic environments, focusing on participants' daily activities.

Main Results: Our findings indicate that the best performance in detecting challenging behaviors was achieved using movement data. The results showed a sensitivity of 0.62, specificity of 0.71, F1-score of 0.36, and an area under the ROC curve of 0.71. These results are particularly significant given the study's focus on real-world scenarios and the limited existing research in this area.

Significance: This study demonstrates that using wearable technology to record physiological and movement signals can detect challenging behaviors in children with ASD in real-world settings. This methodology has the potential to greatly improve the management of these behaviors, thereby enhancing the quality of life for children with ASD and their caregivers. This approach marks a significant step forward in applying the outcome of ASD research in practical, everyday environments.