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.

This article investigates the robust optimal consensus for nonlinear multiagent systems (MASs) through the local adaptive dynamic programming (ADP) approach and the event-triggered control method. Due to the nonlinearities in dynamics, the first part defines a novel measurement error to construct a distributed integral sliding-mode controller, and the consensus errors can approximately converge to the origin in a fixed time. Then, a modified cost function with augmented control is proposed to deal with the unmatched disturbances for the event-based optimal consensus controller. Specifically, a single network local ADP structure with novel concurrent learning is presented to approximate the optimal consensus policies, which guarantees the robustness of the MASs and the uniform ultimate boundedness (UUB) of the neural network (NN) weights’ estimation error and relaxes the requirement of initial admissible control. Finally, an illustrative simulation verifies the effectiveness of the method.