Wearable sensors and machine learning diagnose anxiety and depression in young children

This paper describes a new approach for diagnosing anxiety and depression in young children. Currently, diagnosis in this population requires hours of structured clinical interviews spread over days and weeks. In contrast, we propose the use of a 90-second fear induction task during which time participant motion is monitoring using a commercially available wearable sensor. Machine learning and data extracted from one 20-second phase of the task are used to predict diagnosis in a large sample of children with and without an internalizing diagnosis. We examine the performance of a variety of feature sets and model configurations to identify the best performing approach that provides a diagnostic accuracy of 75%. This accuracy is comparable to existing diagnostic techniques, but at a small fraction of the time and cost currently required. These results point toward the future use of this approach in a clinical setting for diagnosing children with internalizing disorders.