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Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human-computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

This paper investigates the cooperative optimal guidance of hypersonic glide vehicles (HGVs) in the terminal phase with consideration on time-varying velocity, aerodynamic forces, and practical constraints. The cooperative optimal guidance problem is formulated as an optimal control problem with time as the independent variable, which brings great convenience in controlling the impact time. We propose proper convexification techniques to convexify this problem and apply successive convex optimization to get the solution of the original problem. To achieve cooperative guidance of multiple HGVs with time coordination constraint, a deep learning–based approach is proposed to find an optimal common impact time assigned to all the HGVs. The training samples required by deep learning are obtained by convex optimization. An algorithm is then presented to summarize the cooperative optimal guidance strategy. In each guidance loop, the common impact time is updated according to mission conditions, and the guidance commands are generated by the successive solution procedure in real time. Numerical examples will be provided to demonstrate that the proposed cooperative optimal guidance algorithm is effective and efficient, and it can achieve better performance than a popular cooperative proportional navigation guidance law. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG)-based automatic emotion recognition technologies are gaining significant attention and have become crucial in the field of brain–computer interfaces (BCIs). In particular, deep learning methods have been widely used in emotion recognition in recent years. However, most existing methods generally tend to focus on EEG spatiotemporal information, which ignores the potential relationships between brain activity signals and the differences in functional connectivity under different emotions. Here, we raise a functional connectivity-enhanced feature-grouped attention network (FC-FAN) for cross-subject emotion recognition. The FC-FAN model developed is a dual-input model. One input consists of the differential entropy data derived from the original EEG signals, while the other input comprises the functional connectivity data obtained through the calculation of the phase synchronization index. Then the primary EEG features of the two groups' input data are extracted through two specific residual blocks. Next, the designed time-series feature grouped attention module (TFGAM) and functional connectivity feature grouped attention model (F 2 GAM) are utilized to mark interested information or suppress uninterested features for the two groups' features, respectively. Finally, generated information interacts through a fusion operator. The designed framework could not only sufficiently learn the spatiotemporal features of EEG signals but also clearly analyze nonlinear correlations between electrode signals. Comprehensive tests confirm that the FC-FAN has an excellent effect on subject-independent emotion recognition tasks. [ABSTRACT FROM AUTHOR]