Robust human activity recognition using single accelerometer via wavelet energy spectrum features and ensemble feature selection

Abstract

ABSTRACT Wearable sensor-based human activity recognition has been widely used in many fields. Considering that a multi-sensor based recognition system is not suitable for practical applications and long-term activity monitoring, this paper proposes a single wearable accelerometer-based human activity recognition approach. In order to improve the reliability of the recognition system and remove redundant features that have no effect on recognition accuracy, wavelet energy spectrum features and a novel feature selection method are introduced. For each activity sample, wavelet energy spectrum features of the acceleration signal are extracted and the activity is represented by a feature set including wavelet energy spectrum features and features of other attributes. Then, considering the limitation of single filter feature selection method, this paper proposes an ensemble-based filter feature selection (EFFS) approach to optimize the feature set. Features that are robust to sensor placement and highly distinguishable for different activities are selected. In the experiment, the acceleration data around waist is collected and two classifiers: k-nearest neighbour (KNN) and support vector machine (SVM) are utilized to verify the effectiveness of the proposed features and EFFS method. Experiment results show that the wavelet energy spectrum features can increase the discrimination between different activities and significantly and improve the activity recognition accuracy. Compared with other four popular feature selection methods, the proposed EFFS approach provides higher accuracy with fewer features.