Carmen J Byker, Alisha R Farris, Michael Marcenelle, George C Davis, Elena L Serrano
Food waste in a school nutrition program after implementation of new lunch program guidelines
Journal of nutrition education and behavior
sleep stages provide valuable insights individuals sleep quality leveraging movement heart rate data collected modern smartwatches possible enable sleep staging feature enhance users understanding sleep health conditions paper present validate recurrent neural network based model 23 input features extracted accelerometer photoplethysmography sensors data healthy sleep apnea populations designed lightweight fast solution enable prediction sleep stages 30s epoch solution developed using large dataset 1522 night recordings collected highly heterogeneous population different versions samsung smartwatch classification four sleep stages wake light deep rapid eye movements sleep proposed solution achieved 716 balanced accuracy cohens kappa 056 test set 586 recordings results presented paper validate proposal competitive wearable solution sleep staging additionally use large diverse data set contributes robustness solution corroborates validation algorithms performance additional analysis performed healthy sleep apnea population demonstrated algorithms performance low correlation demographic variables