Multicolumn Bidirectional Long Short-Term Memory for Mobile Devices-Based Human Activity Recognition

The ever-growing popularity of mobile devices equipped with accelerometers has provided the opportunity to capture the semantic aspects of human activity and improve user experiences with behavior-based recommendations. These functions depend heavily on the accuracy of human activity recognition, and thus real applications that use mobile devices-based human activity recognition systems (MARSs) need to seamlessly incorporate the information carried by newly labeled training samples. Motivated by the success of the weightlessness feature, we propose a new two-directional feature for bidirectional long short-term memory (BLSTM) for incremental learning in human activity recognition. To further improve the performance, we also present a new ensemble classifier termed multicolumn BLSTM (MBLSTM), which effectively combines different acceleration signal features to further improve activity recognition accuracy. Experiments on the naturalistic mobile devices-based human activity dataset suggest that MBLSTM is superior to other state-of-the-art MARS methods.