Daily Living Activity Dataset of Juvenile Rheumatic Patients From Wearables Data.

Abstract

In the medical field, the use of sensors and wearable devices is now widely regarded as a routine support technique for clinical evaluations. Given the advancements in activity recognition from wearable devices in recent years, it is reasonable to explore the potential of similar data as clinical assessment tools for monitoring the progression of chronic diseases. In the current state of the art, datasets collected using wearable devices for subjects with diseases are relatively rare, and their availability becomes even more limited when focusing on pediatric subjects. Therefore, we decided to record a new dataset in collaboration with the Istituto Giannina Gaslini (Genoa, Italy), a center of excellence in pediatric rheumatology. In this article, we present and describe a dataset collected using accelerometers in FDA-approved wearable devices, positioned on the wrist and ankle of the subjects. This dataset included patients between the ages of 2 and 18 years with chronic diseases and age-matched healthy children as a control group. The activities of daily living to be recorded were selected in collaboration with medical specialists, as the diseases considered may potentially affect functional abilities. This shared dataset could enable the scientific community to develop machine learning-based methods to assess severity and monitor the progression of these diseases in a remote, objective, and non-intrusive manner. The recorded dataset is published and fully accessible on the Harvard Dataverse web portal.