Human activity recognition based on machine learning classification of smartwatch accelerometer dataset

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 pe...

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Bibliographic Details
Main Authors: Radivojević Dušan S., Mirkov Nikola S., Maletić Slobodan
Format: Article
Language:English
Published: University of Belgrade - Faculty of Mechanical Engineering, Belgrade 2021-01-01
Series:FME Transactions
Subjects:
Online Access:https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2021/1451-20922101225R.pdf
Description
Summary: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 model 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.
ISSN:1451-2092
2406-128X