Efficient feature selection for online activity recognition on smart phones

Activity Recognition (AR) is an active area of research that has direct applications on life quality and health of human beings. Related studies aim to classify different daily activities of people with high accuracy rates using various types of sensors. Becoming an essential part in our daily lives, smartphones are now suitable tools that enable people to make use of AR technologies without being obliged to use or wear some extra device. However, due to power and computational constraints of these devices, it becomes a challenging task to attain accurate results by using power and CPU-intensive classifiers. In this study, we present an efficient selection of features to attain high accuracies in recognizing five daily activities with a lightweight classifier, K Nearest Neighbors (KNN). Since previous studies in this area show that it is possible to obtain high recognition performance with the KNN classification algorithm, we focused on the problem of feature selection to see how far this performance can be enhanced by employing the most appropriate feature sets for the KNN algorithm. We use some well-known features together with some more specific features and in order to keep the system energy-efficient, all features are extracted from the readings of a single accelerometer on a smartphone that is carried in the trousers' pocket with different orientations. In this study, we also evaluated the effect of different window lengths and window functions that are used for segmenting the data prior to feature extraction. The results show that by having an efficient selection of features it is possible to obtain promising accuracy rates with a simple classification algorithm like KNN which facilitates online and real-time activity recognition on smartphones.