Search Results (26)

Two-dimensional-material-based FET biosensors have gained attention for being label-free and having ultra-sensitive detection capability. The high carrier mobility and large surface-to-volume ratio of 2D materials enable low detection limits under buffer conditions; however, practical detection still faces many challenges. Current reviews have largely summarized materials, functionalization routes, or target classes separately, but a clearer framework linking interface design, device architecture, and practical sensing performance is still needed. In this review, we examine how interfacial engineering and device architecture govern signal transduction and sensing behavior in 2D material FET biosensors. We also analyze the major barriers to real-sample detection, including Debye screening, nonspecific adsorption, and signal drift, together with commonly used mitigation strategies. On this basis, an “interface–device–performance” framework is discussed as a conceptual approach for understanding the relationship between molecular recognition, electrical response, and sensing performance. This review mainly focuses on the key challenges of 2D material FET biosensors in practical medical applications, discusses the differences between material and application perspectives, and examines the major factors limiting clinical translation. Full article

Population aging is driving the need for unobtrusive, continuous monitoring solutions in residential care environments. Radio-frequency (RF)-based technologies such as Ultra-Wideband (UWB) and millimeter-wave (mmWave) radar are particularly attractive for providing detailed information on presence and movement while preserving privacy. Building on a UWB–mmWave localization system deployed in a senior living residence, this paper focuses on the data-processing methodology for extracting quantitative mobility indicators from long-term indoor monitoring data. The system combines a device-free mmWave radar setup in bedrooms and bathrooms with a tag-based UWB positioning system in common areas. For mmWave data, an adaptive short-term average/long-term average (STA/LTA) detector operating on an aggregated, normalized radar energy signal is used to classify micro- and macromovements into bedroom occupancy and non-sedentary activity episodes. For UWB data, a partially constrained Kalman filter with a nearly constant velocity dynamics model and floor-plan information yields smoothed trajectories, from which daily gait- and mobility-related metrics are derived. The approach is illustrated using one-day samples from three users as a proof of concept. The proposed methodology provides individualized indicators of bedroom occupancy, sedentary behavior, and mobility in shared spaces, supporting the feasibility of combined UWB and mmWave radar sensing for longitudinal routine analysis in real-world elderly care environments. Full article

Palladium (Pd) and titanium (Ti) exhibit opposite dielectric responses upon hydrogenation, with stronger effects observed in the near-infrared (NIR) region. Leveraging this contrast, we investigated Ti/Pd bilayer thin films as a platform for NIR hydrogen sensing—particularly at telecommunication-relevant wavelengths, where such devices have remained largely unexplored. Ti/Pd bilayers coated with Teflon AF (TAF) and fabricated via sequential electron-beam and thermal evaporation were characterized using optical transmission measurements under repeated hydrogenation cycles. The Ti (5 nm)/Pd (x = 2.5 nm)/TAF (30 nm) architecture showed a 2.7-fold enhancement in the hydrogen-induced optical contrast at 1550 nm compared to Pd/TAF reference films, attributed to the hydrogen ion exchange between the Ti and Pd layers. The optimized structure, with a Pd thickness of x = 1.9 nm, exhibited hysteresis-free sensing behavior, a rapid response time (${\mathrm{t}}_{90}$ < 0.35 s at 4% ${\mathrm{H}}_2$), and a detection limit below 10 ppm. It also demonstrated excellent selectivity with negligible cross-sensitivity to $\mathrm{C}{\mathrm{O}}_2$, $\mathrm{C}{\mathrm{H}}_4$, and CO, as well as high durability, showing less than 6% signal degradation over 135 hydrogenation cycles. These findings establish a scalable, room-temperature NIR hydrogen sensing platform with strong potential for deployment in automotive, environmental, and industrial applications. Full article

Human activity recognition (HAR) using Wi-Fi-based sensing has emerged as a powerful, non-intrusive solution for monitoring human behavior in smart environments. Unlike wearable sensor systems that require user compliance, Wi-Fi channel state information (CSI) enables device-free recognition by capturing variations in signal propagation caused by human motion. This makes Wi-Fi sensing highly attractive for ambient healthcare, security, and elderly care applications. However, real-world deployment faces two major challenges: (1) significant cross-subject signal variability due to physical and behavioral differences among individuals, and (2) limited labeled data, which restricts model generalization. To address these sensor-related challenges, we propose TCN-MAML, a novel framework that integrates temporal convolutional networks (TCN) with model-agnostic meta-learning (MAML) for efficient cross-subject adaptation in data-scarce conditions. We evaluate our approach on a public Wi-Fi CSI dataset using a strict cross-subject protocol, where training and testing subjects do not overlap. The proposed TCN-MAML achieves 99.6% accuracy, demonstrating superior generalization and efficiency over baseline methods. Experimental results confirm the framework’s suitability for low-power, real-time HAR systems embedded in IoT sensor networks. Full article

WiFi-based human sensing has emerged as a transformative technology for advancing sustainable living environments and promoting well-being by enabling non-intrusive and device-free monitoring of human behaviors. This offers significant potential in applications such as smart homes and sustainable urban spaces and healthcare systems that enhance well-being and patient monitoring. However, current research predominantly addresses single-user scenarios, limiting its applicability in multi-user environments. In this work, we introduce “MultiSenseX”, a cutting-edge system leveraging a multi-label, multi-view Transformer-based architecture to achieve simultaneous localization and activity recognition in multi-occupant settings. By employing advanced preprocessing techniques and utilizing the Transformer’s self-attention mechanism, MultiSenseX effectively learns complex patterns of human activity and location from Channel State Information (CSI) data. This approach transcends traditional sequential methods, enabling accurate and real-time analysis in dynamic, multi-user contexts. Our empirical evaluation demonstrates MultiSenseX’s superior performance in both localization and activity recognition tasks, achieving remarkable accuracy and scalability. By enhancing multi-user sensing technologies, MultiSenseX supports the development of intelligent, efficient, and sustainable communities, contributing to SDG 11 (Sustainable Cities and Communities) and SDG 3 (Good Health and Well-being) through safer, smarter, and more inclusive urban living solutions. Full article

By integrating sensing capability into wireless communication, wireless sensing technology has become a promising contactless and non-line-of-sight sensing paradigm to explore the dynamic characteristics of channel state information (CSI) for recognizing human behaviors. In this paper, we develop an effective device-free human gesture recognition (HGR) system based on WiFi wireless sensing technology in which the complementary CSI amplitude and phase of communication link are jointly exploited. To improve the quality of collected CSI, a linear transform-based data processing method is first used to eliminate the phase offset and noise and to reduce the impact of multi-path effects. Then, six different time and frequency domain features are chosen for both amplitude and phase, including the mean, variance, root mean square, interquartile range, energy entropy and power spectral entropy, and a feature selection algorithm to remove irrelevant and redundant features is proposed based on filtering and principal component analysis methods, resulting in the construction of a feature subspace to distinguish different gestures. On this basis, a support vector machine-based stacking algorithm is proposed for gesture classification based on the selected and complementary amplitude and phase features. Lastly, we conduct experiments under a practical scenario with one transmitter and receiver. The results demonstrate that the average accuracy of the proposed HGR system is 98.3% and that the F1-score is over 97%. Full article

The present investigation focuses on the design and mathematical modeling of a microelectromechanical (MEMS) mode-localized based sensor/actuator system. This device incorporates a sensitive clamped–clamped shallow arch microbeam with an initial curvature shaped to resemble one of the first two symmetric and asymmetric modes of free oscillations of a clamped–clamped beam. The analysis reveals that with a suitable arrangement of the initial shape of the device flexible electrode and a proper tuning of the maximum initial rise and the actuating dc load enables the transition to display certain bistable behavior. This could be a better choice to build a device with a large stroke. Furthermore, the generated data showed the occurrence of mode-veering, indicating a coupling between the concerned symmetric and asymmetric modes of vibrations, and offering the possibility for such a device to be used as a mode-localized MEMS-based sensor utilizing veering and crossing phenomena. Indeed, where a certain energy is exchanged between symmetric and asymmetric modes of a microbeam, it can be utilized to serve as a foundation for the development of a new class of highly precise resonant sensors that can capture, with a certain level of precision, any of the sensed signal amplitudes. Full article

Radio frequency (RF) technology has been applied to enable advanced behavioral sensing in human-computer interaction. Due to its device-free sensing capability and wide availability on Internet of Things devices. Enabling finger gesture-based identification with high accuracy can be challenging due to low RF signal resolution and user heterogeneity. In this paper, we propose MeshID, a novel RF-based user identification scheme that enables identification through finger gestures with high accuracy. MeshID significantly improves the sensing sensitivity on RF signal interference, and hence is able to extract subtle individual biometrics through velocity distribution profiling (VDP) features from less-distinct finger motions such as drawing digits in the air. We design an efficient few-shot model retraining framework based on first component reverse module, achieving high model robustness and performance in a complex environment. We conduct comprehensive real-world experiments and the results show that MeshID achieves a user identification accuracy of $95.17\%$ on average in three indoor environments. The results indicate that MeshID outperforms the state-of-the-art in identification performance with less cost. Full article

The usage scenarios defined in the ITU-M2150-1 recommendation for IMT-2020 systems, including enhanced Mobile Broadband (eMBB), Ultra-reliable Low-latency Communication (URLLC), and massive Machine Type Communication (mMTC), allow the possibility of accessing different services through the set of Radio Interface Technologies (RITs), Long-term Evolution (LTE), and New Radio (NR), which are components of RIT. The potential of the low and medium frequency bands allocated by the Federal Communications Commission (FCC) for the fifth generation of mobile communications (5G) is described. In addition, in the Internet of Things (IoT) applications that will be covered by the case of use of the mMTC are framed. In this sense, a propagation channel measurement campaign was carried out at 850 MHz and 5.9 GHz in a covered corridor environment, located in an open space within the facilities of the Pedagogical and Technological University of Colombia campus. The measurements were carried out in the time domain using a channel sounder based on a Universal Software Radio Peripheral (USRP) to obtain the received signal power levels over a range of separation distances between the transmitter and receiver from 2.00 m to 67.5 m. Then, a link budget was proposed to describe the path loss behavior as a function of these distances to obtain the parameters for the close-in free space reference distance (CI) and the floating intercept (FI) path loss prediction models. These parameters were estimated from the measurements made using the Minimum Mean Square Error (MMSE) approach. The estimated path loss exponent (PLE) values for both the CI and FI path loss models at 850 MHz and 3.5 GHz are in the range of 2.21 to 2.41, respectively. This shows that the multipath effect causes a lack of constructive interference to the received power signal for this type of outdoor corridor scenario. These results can be used in simulation tools to evaluate the path loss behavior and optimize the deployment of device and sensor network infrastructure to enable 5G-IoT connectivity in smart university campus scenarios. Full article

With the rapid development of technologies such as wireless communications and the Internet of Things (IoT), the proliferation of IoT devices will intensify the competition for spectrum resources. The introduction of cognitive radio technology in IoT can minimize the shortage of spectrum resources. However, the open environment of cognitive IoT may involve free-riding problems. Due to the selfishness of the participants, there are usually a large number of free-riders in the system who opportunistically gain more rewards by stealing the spectrum sensing results from other participants and accessing the spectrum without spectrum sensing. However, this behavior seriously affects the fault tolerance of the system and the motivation of the participants, resulting in degrading the system’s performance. Based on the energy-harvesting cognitive IoT model, this paper considers the free-riding problem of Secondary Users (SUs). Since free-riders can harvest more energy in spectrum sensing time slots, the application of energy harvesting technology will exacerbate the free-riding behavior of selfish SUs in Cooperative Spectrum Sensing (CSS). In order to prevent the low detection performance of the system due to the free-riding behavior of too many SUs, a penalty mechanism is established to stimulate SUs to sense the spectrum normally during the sensing process. In the system model with multiple primary users (PUs) and multiple SUs, each SU considers whether to free-ride and which PU’s spectrum to sense and access in order to maximize its own interests. To address this issue, a two-layer game-based cooperative spectrum sensing and access method is proposed to improve spectrum utilization. Simulation results show that compared with traditional methods, the average throughput of the proposed TL-CSAG algorithm increased by $26.3\%$ and the proposed method makes the SUs allocation more fair. Full article

The technological step towards sensors’ miniaturization, low-cost platforms, and evolved communication paradigms is rapidly moving the monitoring and computation tasks to the edge, causing the joint use of the Internet of Things (IoT) and machine learning (ML) to be massively employed. Edge devices are often composed of sensors and actuators, and their behavior depends on the relative rapid inference of specific conditions. Therefore, the computation and decision-making processes become obsolete and ineffective by communicating raw data and leaving them to a centralized system. This paper responds to this need by proposing an integrated architecture, able to host both the sensing part and the learning and classifying mechanisms, empowered by ML, directly on board and thus able to overcome some of the limitations presented by off-the-shelf solutions. The presented system is based on a proprietary platform named SENSIPLUS, a multi-sensor device especially devoted to performing electrical impedance spectroscopy (EIS) on a wide frequency interval. The measurement acquisition, data processing, and embedded classification techniques are supported by a system capable of generating and compiling code automatically, which uses a toolchain to run inference routines on the edge. As a case study, the system capabilities of such a platform in this work are exploited for water quality assessment. The joint system, composed of the measurement platform and the developed toolchain, is named SENSIPLUS-LM, standing for SENSIPLUS learning machine. The introduction of the toolchain empowers the SENSIPLUS platform moving the inference phase of the machine learning algorithm to the edge, thus limiting the needs of external computing platforms. The software part, i.e., the developed toolchain, is available for free download from GitLab, as reported in this paper. Full article

This paper explores the possibility of using the impedance measurement method used to monitor morphological changes in culture cells for use in cultures in the presence of an electromagnetic field generated by a mobile phone. For this purpose, we used Electric Cell–Substrate Impedance Sensing (ECIS), which is a real-time, label-free, impedance-based method to study cell behaviors in tissue culture. As part of the work, a device enabling the connection in a climatic chamber was prepared without the need to interfere with environmental conditions, and a test culture of mouse fibroblasts was performed. The device based on the Arduino UNO programmable platform worked like a mobile phone. During cell proliferation, it was connected to the device three times and a change in electrical parameters in the measuring system was observed. During the phone call, there was a clear change in the values of the measured parameters. However, analysis of the obtained results indicated that there was little or no effect of the presence of the electromagnetic field on the cell culture, while the observed changes in the values of impedance, resistance, and capacitance are most likely due to the separation of positive and negative medium ions in the electromagnetic field. The application of the presented method seems possible; however, in order to eliminate the separation of ions, a different type of antenna should be designed to emit a homogeneous field to the entire well. Full article

Piezoelectric wearable electronics, which can sense external pressure, have attracted widespread attention. However, the enhancement of electromechanical coupling performance remains a great challenge. Here, a new solid solution of Ba_1−xSr_xSn_0.09Ti_0.91O₃ (x = 0.00~0.08) is prepared to explore potential high-performance, lead-free piezoelectric ceramics. The coexistence of the rhombohedral phase, orthorhombic phase and tetragonal phase is determined in a ceramic with x = 0.06, showing enhanced electrical performance with a piezoelectric coefficient of d₃₃~650 pC/N. Furthermore, Ba_0.94Sr_0.06Sn_0.09Ti_0.91O₃ (BSST) is co-blended with PDMS to prepare flexible piezoelectric nanogenerators (PENGs) and their performance is explored. The effects of inorganic particle concentration and distribution on the piezoelectric output of the composite are systematically analyzed by experimental tests and computational simulations. As a result, the optimal V_OC and I_SC of the PENG (40 wt%) can reach 3.05 V and 44.5 nA, respectively, at 138.89 kPa, and the optimal sensitivity of the device is up to 21.09 mV/kPa. Due to the flexibility of the device, the prepared PENG can be attached to the surface of human skin as a sensor to monitor vital movements of the neck, fingers, elbows, spine, knees and feet of people, thus warning of dangerous behavior or incorrect posture and providing support for sports rehabilitation. Full article

Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect only the presence of a subject’s motion in their field of view (FOV) without other information about the actual location, information from overlapping FOVs of multiple sensors can be useful for localization. This study introduces the PIRILS (pyroelectric infrared indoor localization system), in which the sensing signal processing algorithms are augmented by deep learning algorithms that are designed based on the operational characteristics of the PIR sensor. Expanding to the detection of multiple targets, the PIRILS develops a quantized scheme that exploits the behavior of an artificial neural network (ANN) model to demonstrate localization performance in tracking multiple targets. To further improve the localization performance, the PIRILS incorporates a data augmentation strategy that enhances the training data diversity of the target’s motion. Experimental results indicate system stability, improved positioning accuracy, and expanded applicability, thus providing an improved indoor multi-target localization framework. Full article

The regulation of nervous and cardiovascular systems and some brain-related behaviors, such as stress, panic, anxiety, and depression, are strictly dependent on the levels of the main catecholamines of clinical interest, dopamine (DA), epinephrine (EP), and norepinephrine (NEP). Therefore, there is an urgent need for a reliable sensing device able to accurately monitor them in biological fluids for early diagnosis of the diseases related to their abnormal levels. In this paper, we present the first tyrosinase (Tyr)-based biosensor based on chitosan nanoparticles (ChitNPs) for total catecholamine (CA) detection in human urine samples. ChitNPs were synthetized according to an ionic gelation process and successively characterized by SEM and EDX techniques. The screen-printed graphene electrode was prepared by a two-step drop-casting method of: (i) ChitNPS; and (ii) Tyr enzyme. Optimization of the electrochemical platform was performed in terms of the loading method of Tyr on ChitNPs (nanoprecipitation and layer-by-layer), enzyme concentration, and enzyme immobilization with and without 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC) and N-hydroxysuccinimide (NHS) as cross-linking agents. The Tyr/EDC-NHS/ChitNPs nanocomposite showed good conductivity and biocompatibility with Tyr enzyme, as evidenced by its high biocatalytic activity toward the oxidation of DA, EP, and NEP to the relative o-quinone derivatives electrochemically reduced at the modified electrode. The resulting Tyr/EDC-NHS/ChitNPs-based biosensor performs interference-free total catecholamine detection, expressed as a DA concentration, with a very low LOD of 0.17 μM, an excellent sensitivity of 0.583 μA μM⁻¹ cm⁻², good stability, and a fast response time (3 s). The performance of the biosensor was successively assessed in human urine samples, showing satisfactory results and, thus, demonstrating the feasibility of the proposed biosensor for analyzing total CA in physiological samples. Full article