The main aim of this work is to compare different algorithms for human physical activity recognition from accelerometric and gyroscopic data which are recorded by a smartphone. Three classification algorithms were compared: the Linear Discriminant Analysis, the Random Forest, and the K-Nearest Neighbours. For better classification performance, two feature extraction methods were tested: the Correlation Subset Evaluation Method and the Principal Component Analysis. The results of experiment were expressed by confusion matrixes.
CITATION STYLE
Peterek, T., Penhaker, M., Gajdoš, P., & Dohnálek, P. (2014). Comparison of Classification Algorithms for Physical Activity Recognition. In Advances in Intelligent Systems and Computing (Vol. 237, pp. 123–131). Springer Verlag. https://doi.org/10.1007/978-3-319-01781-5_12
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