Online Human Activity Recognition on Smart Phones

This paper analyzes the performance of different classification methods for online activity recognition on smart phones using the built-in accelerometers. First, we evaluate the performance of activity recognition using the Naïve Bayes classifier and next we utilize an improvement of Minimum Distance and K-Nearest Neighbor (KNN) classification algorithms, called Clustered KNN. For the purpose of online recognition, clustered KNN eliminates the computational complexity of KNN by creating clusters, i.e., smaller training sets for each activity and classification is performed based on these compact, 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 compared to the previous studies and even with the ones which consider offline classification.