Performance evaluation of classification methods for online activity recognition on smart phones

This paper analyzes the performance of different classification methods for online activity recognition on mobile phones using the built-in accelerometers. First, we evaluate the performance of activity recognition using Naïve Bayes and minimum distance classifiers and next we propose an improvement of Minimum Distance and K-Nearest Neighbor (KNN) classification algorithms called Clustered KNN. Clustered KNN eliminates the computational complexity of KNN by creating clusters, training sets for each activity. Classification is performed based on these reduced sets. We evaluate the performance of these classifiers on five test subjects for activities of walking, running, sitting and standing, and find that Naïve Bayes provides not satisfactory results whereas Clustered KNN gives promising results.