Predicting children's energy expenditure during physical activity using deep learning and wearable sensor data.

This study examined a series of machine learning models, evaluating their effectiveness in assessing children's energy expenditure, in terms of the metabolic equivalents (MET) of physical activity (PA), from triaxial accelerometery. The study also determined the impact of the sensor placement (waist, ankle or wrist) on the machine learning model's predictive performance. Twenty-eight healthy Caucasian children aged 8-11years (13 girls, 15 boys) undertook a series of activities reflective of different levels of PA (lying supine, seated and playing with Lego, slow walking, medium walking, and a medium paced run, instep passing a football, overarm throwing and catching and stationary cycling). Energy expenditure and physical activity were assessed during all activities using accelerometers (GENEActiv monitor) worn on four locations (i.e. non-dominant wrist, dominant wrist, dominant waist, dominant ankle) and breath-by-breath calorimetry data. MET values ranged from 1.2 ± 0.2 for seated playing with Lego to 4.1 ± 0.8 for running at 6.5 kmph