Predicting aggression to others in youth with autism using a wearable biosensor

Abstract

Unpredictable and potentially dangerous aggressive behavior by youth with Autism Spectrum Disorder (ASD) can isolate them from foundational educational, social, and familial activities, thereby markedly exacerbating morbidity and costs associated with ASD. This study investigates whether preceding physiological and motion data measured by a wrist-worn biosensor can predict aggression to others by youth with ASD. We recorded peripheral physiological (cardiovascular and electrodermal activity) and motion (accelerometry) signals from a biosensor worn by 20 youth with ASD (ages 6-17 years, 75% male, 85% minimally verbal) during 69 independent naturalistic observation sessions with concurrent behavioral coding in a specialized inpatient psychiatry unit. We developed prediction models based on ridge-regularized logistic regression. Our results suggest that aggression to others can be predicted 1 min before it occurs using 3 min of prior biosensor data with an average area under the curve of 0.71 for a global model and 0.84 for person-dependent models. The biosensor was well tolerated, we obtained useable data in all cases, and no users withdrew from the study. Relatively high predictive accuracy was achieved using antecedent physiological and motion data. Larger trials are needed to further establish an ideal ratio of measurement density to predictive accuracy and reliability. These findings lay the groundwork for the future development of precursor behavior analysis and just-in-time adaptive intervention systems to prevent or mitigate the emergence, occurrence, and impact of aggression in ASD.Lay Summary: Unpredictable aggression can create a barrier to accessing community, therapeutic, medical, and educational services. The present study evaluated whether data from a wearable biosensor can be used to predict aggression to others by youth with autism spectrum disorder (ASD). Results demonstrate that aggression to others can be predicted 1 min before it occurs with high accuracy, laying the groundwork for the future development of preemptive behavioral interventions and just-in-time adaptive intervention systems to prevent or mitigate the emergence, occurrence, and impact of aggression to others in ASD.

Multimodal Learning Analytics research with young children: A systematic review

“…In some studies, these sensors were connected to a laptop or mini‐PC using cables (Rahman & Bhuiyan, 2015 proposes keeping this hardware in a cartridge case). Wireless wristbands are more usual and allow the data related to heart rate, electrodermal activity and temperature to be collected (Goodwin, Mazefsky, Ioannidis, Erdogmus & Siegel, 2019; Kourakli et al ., 2017; Malmberg et al ., 2019; Noroozi et al ., 2019). Rudovic et al .…”

“…(2016) mention another ethical design principle: guaranteeing that children’s personal information cannot be tracked. Other authors expressed concerns about ensuring anonymity, but few explicitly reported having obtained the ethical approval or pre‐approval of an institutional review board (Goodwin et al ., 2019; Kosmas et al ., 2019; Martinez‐Maldonado et al ., 2017; Papavlasopoulou, Sharma & Giannakos, 2018; Rudovic et al ., 2018). Regarding data protection, Ochoa and Worsley (2016) reflect on the management, classification and storage of LA, indicating that all these processes generate questions about privacy.…”

An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors

“…Unfortunately, the detection only relies in this sensor. Goodwin et al [23] also presented a remarkable way to detect violence by using cardiovascular, electrodermal activity and motion (accelerometry) but focused on people with autism spectrum disorder, so the circumstances are not exactly similar to a IPV context. Therefore, although being some proposal very complete [24], previous proposals have a lack of completeness, this is, relying in a couple of features and other works with a more complete monitoring in order to continuously monitor daily activities (which could include an assault) like the exposed ones in the review done by Kumari et al [25] are not specifically designed for IPV survivors monitoring.…”

Framework for Selecting and Benchmarking Mobile Devices in Psychophysiological Research

“…Such capabilities are advantageous because they are more likely to capture events that are rare and unpredictable (e.g., panic attacks or cardiac arrhythmias; Leibold & Schruers, 2018;Mittal, Movsowitz, & Steinberg, 2011;Mittal et al, 2011), events that unfold over longer periods of time (e.g., sleep across days or metabolic changes with physical activity; Gao, Brooks, & Klonoff, 2018;Sano, Picard, & Stickgold, 2014), or salient events that may be unethical to elicit experimentally (e.g., receiving news about the death of a loved one; Wilhelm & Grossman, 2010). Ambulatory physiological recordings have also demonstrated utility performing dynamic assessments of symptoms over time in patients with cancer (Savard et al, 2013), Parkinson's disease (Moore et al, 2008), Autism Spectrum Disorder (Goodwin et al, 2019), borderline personality disorder (Ebner-Priemer et al, 2008), and seizures (Michel et al, 2015). Telemetric devices are also beginning to be used to deliver interventions to treat symptoms or disease (e.g., exercise interventions for patients with cancer; Schaffer et al, 2019).…”