Classification Models for Activity Recognition using Smart Phone Accelerometers

Abstract: The huge amount of data generated by accelerometers in smartphones creates new opportunities for useful data mining applications. Machine Learning algorithms can be effectively used for tasks such as the classification and clustering of physical activity patterns. This paper builds and evaluates a system that uses real-world smartphone-based tri-axial accelerometers labeled data to perform activity recognition tasks. Over a million data recorded at the frequency 20Hz, was filtered and pre-processed to extract relevant features for the classification task. The features were selected to obtain higher classification accuracy. These supervised classification models, namely, random forest, support vector machines, decision tree, naïve Bayes classifier, and multinomial logistic regression are evaluated and finally compared with a few unsupervised classification models such as k-means and self-organizing map (SOM) technique built on an unlabelled dataset. Statistical model evaluation metrics such as accuracy-precision-recall are used to compare the classification performances of the models. It was interesting to see that all supervised learning methods achieved very high accuracy (over 95%) on labeled datasets as against 65% by unsupervised SOM. Moreover, they registered very low similarity (23%) among themselves on unlabelled datasets with the same selected features.