m-Health and Autism: Recognizing Stress and Anxiety with Machine Learning and Wearables Data

Registration Now Open for

ADOS-2 Clinical Training Workshop

November 25-27. Click here for info!

Titlem-Health and Autism: Recognizing Stress and Anxiety with Machine Learning and Wearables Data
Publication TypeConference Paper
Year of Publication2019
AuthorsMasino, AJ, Forsyth, D, Nuske, H, Herrington, J, Pennington, J, Kushleyeva, Y, Bonafide, CP
Conference Name2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)
PublisherIEEE
Abstract

Consumer-grade wearables provide physiological measurements which may inform m-health applications that predict adverse outcomes. Autism Spectrum Disorder (ASD) represents a compelling example. Many individuals with ASD present with challenging behaviors that are preceded by physiological changes. Physiological measures could, therefore, support real-time interventions to avert challenging behaviors in various social settings. However, no prior research has demonstrated a methodological approach to detect these changes using wearable device data. We sought to demonstrate a machine learning approach that uses wearables data to differentiate physiological states associated with stressful and non-stressful scenarios in children with ASD. In a controlled laboratory setting, we collected heart rate and RR interval measurements during rest and during activities designed to mimic stress using a consumer-grade wearable device. Our analysis included 38 participants (22 ASD, 16 non-ASD). Following outlier removal, we extracted 20 statistical features from data collected during each patient's rest and stressful periods. Using nested leave-one-out cross-validation over 76 sample periods (38 rest / 38 stress), we trained and evaluated logistic regression (LR) and support vector machine (SVM) classifiers to label each validation sample as a rest or stressful period. The SVM and LR models achieved 93% and 87% accuracy, respectively. These results suggest that machine learning models combined with wearables data may support real-time m-health intervention applications.