Integrated Sensing and Communications (ISAC) integrates sensing and communication functions through ubiquitous wireless signals, providing a seamless and flexible interaction experience, making it an ideal choice for intelligent Human-Computer Interaction (HCI). Among various interaction methods, gesture recognition has garnered widespread attention. However, current RF-based gesture recognition methods within ISAC are constrained by single-target sensing and insufficient robustness. In this paper, we propose MultiGes, a real-time multi-user gesture recognition system designed to support ISAC-driven scenarios. MultiGes employs dual commercial Impulse Radio Ultra-Wideband (IR-UWB) devices to create multiple wireless links, capturing dynamic motion features from multiple targets. First, a human energy map is constructed based on the reflected signal energy to determine multi-target coordinates. Then, a Differential Human Relative Velocity (DHRV) matrix is extracted to capture fine-grained motion information. Finally, we design a lightweight STNet model to extract spatiotemporal gesture features from the DHRV matrix, enabling real-time multi-target gesture recognition. We implement the MultiGes system prototype and conduct extensive experiments on ten common gestures in HCI scenarios. Experimental results demonstrate that MultiGes achieves efficient recognition for 2 to 5 users, with an average accuracy of over 90%, providing a robust, scalable, and real-time solution for multi-target gesture recognition in ISAC-driven smart environments.

Current RF-based solutions have demonstrated that human heartbeat activity induces millimeter-scale chest displacements, changing RF reflection paths and making contact-free heartbeat monitoring possible. However, human heartbeat activity is very weak and can be hidden by out by the body movements or breathing, which poses significant challenges in accurately extracting heartbeat information. To solve this, in this paper we propose a contact-free heartbeat rate estimation system based on the periodic variation feature, namely UWHeart. UWHeart utilizes Impulse Radio-Ultra Wideband (IR-UWB) to capture reflection signals from the monitoring target and outputs the heartbeat rate. This system first filters out the noise unrelated to heartbeat through band-pass filtering. Then, we leverage the periodicity of heartbeat activity to construct autocorrelation matrix features of the denoised signal. Finally, we develop a one-dimensional Temporal Convolutional Network (TCN) model to realize the mapping between the heartbeat autocorrelation features and actual heartbeats, and accurately estimate the heartbeat rate of the monitoring target. We also implement the UWHeart system on commercial IR-UWB equipment to verify our method through a series of experiments, and the experimental results prove the effectiveness of our method.

Alzheimer’s Disease (AD) is the primary cause of dementia in the elderly. Utilizing advanced Graph Neural Networks (GNNs) to map the relationships between various brain regions and the AD using structural magnetic resonance imaging (sMRI) is one of the effective diagnostic methods. However, current GNN-based methods suffer from significant data redundancy, resulting in high computational costs and overlooking the critical role of connectivity between different brain regions. To solve this, in this paper we propose a lightweight Alzheimer’s disease diagnosis method based on Gaussian Graphical Lasso (GLasso) estimation. Our method first compresses features from traditional MRI and maps them to corresponding Regions of Interest (ROIs) using the Automated Anatomical Labeling (AAL) template. Subsequently, a Gaussian Graphical Model (GGM) is employed to construct the graph network, and iterative GLasso estimation is applied to retain significant ROI connections while removing redundant and irrelevant connections. Finally, a graph transformer network with edge features is used to achieve efficient diagnosis of AD symptoms. Extensive testing on the open-source dataset ADNI demonstrates that our method achieves precise diagnostic results, outperforming state-of-the-art approaches, and significantly reduces training costs. Furthermore, it effectively identifies critical region connections associated with AD, providing a feasible solution for the early diagnosis of Alzheimer’s disease.

Passive human tracking plays a critical role in the field of ubiquitous sensing, offering customized services such as real-time location tracking for vital sign monitoring and motion detection. Traditional contact-free tracking systems are primarily designed for Line-of-Sight (LOS) scenarios, requiring a direct path between the radio device and the target. However, in Non-Line-of-Sight (NLOS) scenarios, where obstacles obstruct this direct path, these systems suffer from sensing failures and are unable to accurately obtain the motion trajectory of the sensing target. In this paper, we propose the UWTracking system, which utilizes the Commercial Off-the-Shelf (COTS) Impulse Radio-Ultra Wideband (IR-UWB) radars to enable precise indoor passive human tracking in both LOS and NLOS scenarios. To effectively capture the motion information of a moving target in NLOS scenarios, we present the Reconstructed Distributed- Doppler Frequency Shift (RD-DFS) features. We then binarize the RD-DFS features and design the Distance Extraction Algorithm (DEA) to obtain the target's distance in both scenarios. Subsequently, the Circle Intersection Method with Distance Stretching (CIM-DS) algorithm is developed to determine the indoor position of the sensing target, and the Scanning Angle and Velocity Particle Filter (SAV-PF) algorithm facilitates high-precision trajectory tracking. We implement a prototype of UWTracking system and conduct extensive evaluations to showcase its trajectory tracking performance under various scenarios. The results demonstrate that UWTracking achieves effective real-time tracking, with a median tracking error of 17.65 cm in the LOS scenario and 23.34 cm in the NLOS scenario, outperforming the state-of-the-art trajectory tracking systems based on COTS IR-UWB Radars.