Fall detection and recognition play a crucial role in enabling timely medical interventions for people who are at risk of falls, especially among vulnerable populations like older adults and those with mobility limitations. In this paper, a cost-effective integrated sensing and communication system, namely FallDR, is presented for fall detection and recognition using ultra-wideband communication. Firstly, we collected the time of flight information of falls (four types) and non-fall events by 10 participants using FallDR. We then proposed a convolutional neural network incorporated with squeeze-and-excitation blocks to detect and recognize falls based on fall trajectories. It proves that the proposed model is accurate, energy-efficient, and lightweight to achieve 100% accuracy in fall detection and recognition. Our proposed solution is proven to be highly robust against environmental changes such as interference, distance, and direction changes. Further tests in an office showed that FallDR could achieve nearly 100% accuracy, even when the environment was changed. FallDR efficiently employs the characteristics of fall trajectory and the advanced modeling ability of the neural network. We have published our archived datasets and code for comparisons and improvements.

Waypoint estimation (WE) has a wide range of applications for indoor walkers, such as fire rescue and navigation to find exit doors, lifts, or stairs as examples of waypoints, etc. Data-driven WE has been on the rise with advancements in deep learning algorithms. The current WE methods, however, face two challenges. On the one hand, most waypoint detection approaches rely on visual sensors, hence, their estimation performance is limited by light when collecting visual data. On the other hand, data-driven methods necessitate a large number of labeled data to train a WE model, which significantly increases the time spent manually marking labels. Targeting the above two challenges, our work first proposes a novel deep Bayesian active learning waypoint estimator for indoor walkers (DeepWE) based on human activity recognition (HAR). This estimates six indoor waypoints through walkers’ daily activities due to the strong correlation between human activities and waypoints. First, an initial DeepWE model is developed using a Bayesian ensembled convolutional neural network (B-CNN) using the accelerometer and gyroscope data. Then, active learning is employed to query the most formative samples from pool points with four acquisition functions, and only these queried samples are labeled manually. Finally, the initial DeepWE model is updated from this labeled data using an incremental learning algorithm. Empirical results on two publicly available USC-HAD and OPPORTUNITY data sets show DeepWE performs a considerable accuracy boost for WE, with a substantial amount of acquired pool points reduction (more than 40%).

Data-driven human localization technology has been on the rise with advancements in end-to-end Artificial Neural Networks (ANNs) in recent years. Different from the traditional Pedestrian Dead Reckoning (PDR) algorithms, the data-driven method can significantly reduce cumulative error over time arising from integration and improve the accuracy and efficiency of localization. However, the computation complexity of ANNs imposes high requirements on hardware conditions and heavily hinders its application on mobile devices. Targeting the above challenges, we design a Position Regression algorithm with a Deep Spiking Neural Network (called SpikePR)-an architecture inspired by biological neurons-to regress the user’s position when collecting a sequence of raw IMU measurements from mobile devices. This architecture integrates ANNs and the Spiking Neural Network (SNN) with a Leaky Integrate-and-Fire (LIF) mechanism due to its low-power computation with binary spikes and capability to model the temporal dynamics in time series data. We conduct extensive experiments on four open-source datasets with the proposed SpikePR algorithm. The experiment results demonstrate that compared to the state-of-the-art driven-based position regression algorithms, the proposed SpikePR can save more than 90% energy consumption while achieving similar location errors.

Ship detection based on Synthetic Aperture Radar (SAR) is crucial for maritime surveillance, yet existing methods struggle with two critical challenges: (1) missed detections in nearshore scenarios due to highly similar radar reflections between ships and shore objects (e.g., buildings, docks), and (2) feature vanishing of small ships caused by SAR resolution limits and downsampling operations. To address these issues, we propose Kolmogorov-Arnold-Enhanced YOLO (KaneYOLO), a novel SAR ship detector designed for robust discrimination in complex nearshore scenes and enhanced small-target detection. Firstly, we introduce KAN Block, a feature extraction module leveraging the Kolmogorov-Arnold theorem to model complex nonlinear relationships in SAR images. By integrating Gram polynomials and B-spline-based activation functions, KAN Block dynamically calibrates feature responses to suppress background clutter (e.g., shore structures) while amplifying subtle intensity patterns and edge transitions unique to ships. Secondly, we design a Feature Fusion Allocation Structure (FFAS) with parallelized deep convolutions to aggregate multi-scale features. This structure preserves small-target details by propagating enriched semantic information across detection layers, mitigating feature loss during downsampling. Finally, a Detail Enhancement Detection Head (DEDH) is proposed to reduce computational overhead through shared convolutional layers, while enhancing local feature utilization via direction-sensitive convolutions and group normalization. KaneYOLO achieved 93.9% AP on HRSID and 98.3% AP on SSDD. These results validate its robustness in handling SAR-specific challenges, offering significant potential for real-world maritime security applications.