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Commercially available wearable biosensors have the potential to enhance psychophysiology research and digital health technologies for autism by enabling stress or arousal monitoring in naturalistic settings. However, such monitors may not be comfortable for children with autism due to sensory sensitivities. To determine the feasibility of wearable technology in children with autism age 8-12 years, we first selected six consumer-grade wireless cardiovascular monitors and tested them during rest and movement conditions in 23 typically developing adults. Subsequently, the best performing monitors (based on data quality robustness statistics), Polar and Mio Fuse, were evaluated in 32 children with autism and 23 typically developing children during a 2-h session, including rest and mild stress-inducing tasks. Cardiovascular data were recorded simultaneously across monitors using custom software. We administered the Comfort Rating Scales to children. Although the Polar monitor was less comfortable for children with autism than typically developing children, absolute scores demonstrated that, on average, all children found each monitor comfortable. For most children, data from the Mio Fuse (96%-100%) and Polar (83%-96%) passed quality thresholds of data robustness. Moreover, in the stress relative to rest condition, heart rate increased for the Polar, F(1,53) = 135.70, p

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.

While cognitive modeling has begun to make good progress in accounting for human multitasking behavior, current models typically focus on externally-driven task switching in laboratory-task settings. In contrast, many real-world complex tasks, particularly time-critical ones, involve internally-driven multitasking in which people themselves decide when to switch between tasks. In this paper, we propose an adaptation of the ACT-R cognitive architecture that incorporates a notion of elapsed time for the current goal and uses time to determine when to switch away from the current task. We demonstrate the usefulness of this mechanism in an application to a dynamic, time-critical dual search task, showing how an ACT-R model can account for various aspects of human subjects' multitasking behavior.