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

Dušan Radivojević, Nikola Mirkov, Slobodan Maletić
2021 FME Transaction  
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
more » ... del respectively. Since the validation and recognition, reached about 81% and 75% respectively, a tendency for improving accuracy as a function of number of participants is considered. The influence of data sample precision to the accuracy of the models is examined since the input data could be given from various wearable devices.
doi:10.5937/fme2101225r fatcat:eumkojbzkbgzzcubdrv3v6rctq