A Novel Selective Ensemble Learning Method for Smartphone Sensor-Based Human Activity Recognition Based on Hybrid Diversity Enhancement and Improved Binary Glowworm Swarm Optimization

Human activity recognition (HAR) is gaining interest with many important applications including ubiquitous computing, health-care services and detection of diseases. Smartphone sensors have high acceptance and adherence in daily life and they provide an alternative and economic way for activity recognition. To improve the performance of smartphone sensor-based HAR, a novel smartphone sensor-based HAR method (hybrid diversity enhancement with selective ensemble learning, HDESEN) that utilizes selective ensemble learning with differentiated extreme learning machines (ELMs) is proposed, where hybrid diversity enhancement is proposed to boost the diversity of base models and an improved binary glowworm swarm optimization (IBGSO) is employed to effectively enhance the learning process by choosing a superior subset for ensemble instead of all. Firstly, statistical features in the time domain and frequency domain are extracted and integrated from smartphone sensors and then three filter-based feature selection methods are utilized for desirable base models. Secondly, to enhance the diversity of the base models, three types of diversities are introduced to construct different base models, respectively. Among them, Bootstrap is introduced to design distinctive training data subsets for differential base models, random subspace and optimized subspace are proposed to obtain different feature spaces for constructing base models. Thirdly, a pruning method based on glowworm swarm optimization (GSO) is proposed to find the optimal sub-ensemble from the pool of models from all diverse types to implement selective ensemble learning. The experimental results on tow publicly available datasets (UCI-HAR and WISDM) demonstrate the proposed HDESEN can reliably improve the performance of HAR and outperforms the relevant state-of-the-art approaches.