A machine learning technique for device non-wear detection in children with ADHD.

Wearable devices dotted with accelerometers are widely used to study motor overflow in children with attention deficit/hyperactivity disorder (ADHD), as they provide accurate and reliable measurements of physical activity. To accurately determine the time spent in different intensities of physical activity (sedentary, light, moderate or vigorous), it is necessary to identify periods when devices are worn or not. However, this can be problematic because children’s sedentary activities may be mistaken for periods when devices are not worn.In this paper we propose a machine learning approach to detect non-wear periods using data collected from 18 children with ADHD and 18 healthy children using the triaxial accelerometer Actigraph GT9X worn on the non-dominant wrist for one week. The objective is to reduce the overestimation of time spent in activities obtained after data reduction with algorithms using long non-wear time detection periods.The agreement between real data and the classification performed by SVM model (Concordance Correlation Coefficient > 0.95) supported the use of reduced time intervals for detecting non-wear periods.