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.

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.

In most RFID localization systems, acquiring a reader antenna's position at each sampling time is challenging, especially for those antenna-carrying robot or drone systems with unpredictable trajectories. In this article, we present RF-MVO that fuses RFID and computer vision for stationary RFID localization in 3D space by attaching a light-weight 2D monocular camera to two reader antennas in parallel. First, the existing monocular visual odometry only recovers a camera/antenna trajectory in the camera view from 2D images. By combining it with RF phase, we design a model to estimate a scale factor for real-world trajectory transformation, along with spatial directions of an RFID tag relative to a virtual antenna array due to the mobility of each antenna. Then we propose a novel RFID localization algorithm that does not require exhaustively searching all possible positions within the pre-specified region. Second, to speed up the searching process and improve localization accuracy, we propose a coarse-to-fine optimization algorithm. Third, we introduce the concept of horizontal dilution of precision (HDOP) to measure the confidence level of localization results. Our experiments demonstrate the effectiveness of proposed algorithms and show RF-MVO can achieve 6.23 cm localization error.

Recent RFID systems start attaching a tag array consisting of two or more tags on an object to deal with polarization mismatch and RF phase periodicity for battery-free sensing and localization. The multi-tag solution can also provide target orientation estimation. However, when these tags are closely spaced apart, mutual coupling will be induced, producing the unexpected changes in reported RSSI and RF phase. In this paper, we present RF-Mirror that enables compensating the distortion in a two-tag array labeled RFID system. The system would output the accurate difference in tag-to-antenna distances between two tags, which is a fundamental parameter in previous works for use. Firstly, we model the backscatter signal of a responding tag in a two-tag scenario, and then formulate novel RSSI- and RF phase-distance models with coupling terms. Secondly, we design an algorithm to characterize the coupling effect on tag gain by fusing RSSI and RF phase. Thirdly, we design a decoupling algorithm based on an observation that tag mutual coupling is independent of the position of a tag array relative to a reader antenna. Our experiments show the effectiveness of our models and RF-Mirror achieves the decoupling error of 0.197 cm in calculating the tag-to-antenna distance difference.

Most of the existing RFID-based localization systems cannot well locate RFID-tagged objects in a 3D space. Limited robot-based RFID solutions require reader antennas to be carried by a robot moving along an already-known trajectory at a constant speed. As the first attempt, this paper presents RF-MVO, which fuses battery-free RFID and monocular visual odometry to locate stationary RFID tags in a 3D space and recover an unknown trajectory of reader antennas binding with a 2D monocular camera. The proposed hybrid system exhibits three unique features. Firstly, since the trajectory of a 2D monocular camera can only be recovered up to an unknown scale factor, RF-MVO combines the relative-scale camera trajectory with depth-enabled RF phase to estimate an absolute scale factor and spatially incident angles of an RFID tag. Secondly, we propose a joint optimization algorithm consisting of coarse-to-fine angular refinement, 3D tag localization and parameter nonlinear optimization, to improve real-time performance. Thirdly, RF-MVO can determine the effect of relative tag-antenna geometry on the estimation precision, providing optimal tag positions and absolute scale factors. Our experiments show that RF-MVO can achieve 6.23cm tag localization accuracy in a 3D space and 0.0158 absolute scale factor estimation accuracy for camera trajectory recovery.

A see-through-wall system can be used in life detection, military fields, elderly people surveillance. and gaming. The existing systems are mainly based on military devices, customized signals or pre-deployed sensors inside the room, which are very expensive and inaccessible for general use. Recently, a low-cost RFID technology has gained a lot of attention in this field. Since phase estimates of a battery-free RFID tag collected by a commercial off-the-shelf (COTS) RFID reader are sensitive to external interference, the RFID tag could be regarded as a battery-free sensor that detects reflections off targeted objects. The existing RFID-based system, however, needs to first learn the environment of the empty room beforehand to separate reflections off the tracked target. Besides, it can only track low-speed metal objects with high-positioning accuracy. Since the human body with its complex surface has a weaker ability to reflect radio frequency (RF) signals than metal objects, a battery-free RFID tag can capture only a subset of the reflections off the human body. To address these challenges, a RFID-based human motion sensing technology, called RF-HMS, is presented to track device-free human motion through walls. At first, we construct transfer functions of multipath channel based on phase and RSSI measurements to eliminate device noise and reflections off static objects like walls and furniture without learning the environment of the empty room before. Then a tag planar array is grouped by many battery-free RFID tags to improve the sensing performance. RF-HMS combines reflections from each RFID tag into a reinforced result. On this basis, we extract phase shifts to detect the absence or presence of any moving persons and further derive the reflections off a single moving person to identify his/her forward or backward motion direction. The results show that RF-HMS can effectively detect the absence or presence of moving persons with 100% accuracy and keep a high accuracy of more than 90% to track human motion directions.

Exploiting radio frequency signals is promising for locating and tracking objects. Prior works focus on per-tag localization, in which each object is attached with one tag. In this paper, we propose a comprehensive localization and tracking scheme by attaching two RFID tags to one object. Instead of using per-tag localization pattern, adding one-more RFID tag to the object exhibits several benefits: 1) providing rich freedom in RFID reader's antenna spacing and placement; 2) supporting accurate calibration of the reader's antenna location and spacing, and 3) enabling fine-grained calculation on the orientation of the tags. All of these advantages ultimately improve the localization/tracking accuracy. Our extensive experimental results demonstrate that the average errors of localization and orientation of target tags are 6.415 cm and 1.330°, respectively. Our results also verify that the reader's antenna geometry does have impact on tag positioning performance.

The convergence of Internet of Things, cloud computing, and wireless body-area networks (WBANs) has greatly promoted the industrialization of electronic-/mobile-healthcare (e-/m-healthcare). However, the further flourishing of e-/m-healthcare still faces many challenges including information security and privacy preservation. To address these problems, a healthcare system (HES) framework is designed that collects medical data from WBANs, transmits them through an extensive wireless sensor network infrastructure, and finally, publishes them into wireless personal-area networks via a gateway. Furthermore, HES involves the groups of send-receive model scheme to realize key distribution and secure data transmission, the homomorphic encryption based on matrix scheme to ensure privacy, and an expert system able to analyze the scrambled medical data and feedback the results automatically. Theoretical and experimental evaluations are conducted to demonstrate the security, privacy, and improved performance of HES compared with current systems or schemes. Finally, the prototype implementation of HES is explored to verify its feasibility.

The degree of modernization of transportation is currently an important criterion for urban development. Progress in communication techniques and networking, together with vehicle localization methods, have become the key enablers of innovative transportation systems.

In this paper, we examine the merit of a simple and lightweight stochastic transmission strategy based on the ALOHA protocol for underwater wireless sensor networks (UWSNs). We use a stochastic scheduling approach in which time is slotted, and each network component transmits according to some probability during each slot. We present objective functions for assigning the transmission probabilities that are aimed at optimizing network performance with respect to the overall network latency and the overall network reliability. We show that there is an easily distributed heuristic policy based on local network density that works well in practice. We also evaluate our approach using numerical simulations. The evaluation results show that even without using explicit control signaling, our lightweight stochastic scheduling method is effective for data transmission in underwater sensor networks.