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Human activity recognition based on machine learning classification of smartwatch accelerometer dataset
Raspoznavanje ljudskih aktivnosti klasifikacijom akcelerometarskih podataka sa pametnih satova pomoću modela mašinskog učenja
2021
FME Transaction
Raspoznavanje ljudskih aktivnosti klasifikacijom akcelerometarskih podataka sa pametnih satova pomoću modela mašinskog učenja
This paper presents two Machine Learning models that classify time series data given from smartwatch accelerometer of observed subjects. For the purpose of classification we use Deep Neural Network and Random Forest classifier algorithms. The comparison of both models shows that they have similar performance with regard to recognition of subject's activities that are used in the test group of the dataset. Training accuracy reaches approximately 95% and 100% for Deep Learning and Random Forest
doi:10.5937/fme2101225r
fatcat:eumkojbzkbgzzcubdrv3v6rctq