Felix Gille, Anja Frei, Marco Kaufmann, Anja Lehmann, Javier Muñoz Laguna, Kimon Papadopoulos, Angela Spörri, Mina Stanikić, Martin Tušl, Federica Zavattaro, Milo Alan Puhan
A guide for a student-led doctoral-level qualitative methods short course in epidemiology: faculty and student perspectives
International journal of epidemiology
human activity recognition har using machine learning ml methods continuously developed method collecting analyzing large amounts human behavioral data using special wearable sensors past decade main goal find reliable method could automatically detect various playful daily routine activities children defined 40 activities ml recognition collected activity motion data means wearable smartwatches special senskid software analyzed data 34 children 19 girls 15 boys age range 659838 median age 747 children typically developing first graders three elementary schools activity recognition binary classification task evaluated light gradient boosted machine lgbm learning algorithm decision tree based method threefold cross validation used sliding window technique signal processing aimed finding best window size analysis behavior element achieve effective settings seventeen activities 40 successfully recognized auc values 08 window size significant effect summary lgbm promising solution har line previous findings results provide firm basis precise effective recognition system make human behavioral analysis faster objective