Accuracy Of Sensor Research Articles

GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean

Abstract. The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates retrievals of liquid precipitation over snow-free and non-mountainous surfaces from GPROF V7 and multiple configurations of GPROF-NN against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years, and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable estimates of liquid precipitation over the CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reliably reproduces each region's diurnal and seasonal precipitation characteristics. The effective resolution of GPROF V7 is found to be 51 km over the CONUS and 18 km over the tropical Pacific. GPROF V7 also produces robust precipitation estimates for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPM PMW precipitation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error of instantaneous precipitation estimates by about 20 % for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar–radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used to generate the training data for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals.

Electrochemical analysis of harmful dichlorvos insecticides based on a high‐performance composite sensor

AbstractBACKGROUNDOrganophosphorus pesticides (OPs) were used in large quantities, and pesticide residues have caused different degrees of environmental pollution. Because the current laboratory detection techniques cannot meet the requirements of quickly and accurately monitoring pesticide concentration, there is an urgent need for a portable and accurate method to trace pesticide residues. This paper reports an effective electrochemical sensor composed of ZrO2@PDA electrode material and glassy carbon electrode (GCE).RESULTSThis sensor displayed optimal selectivity for dichloride among various OPs with a low limit of detection (LOD = 1.318 × 10−3 mg L−1). Wherein the accuracy and reliability of this electrochemical sensor were verified through the standard method of gas chromatography–mass spectrometry (GC–MS) (p = 0.977 > 0.05), as well as the stability and anti‐interference capabilities. In addition, the results of recovery experiments have shown that the recovery rate of the electrochemical sensor for dichlorvos ranged from 89.2% to 103.8%, with RSD less than 5%, indicating its practical application in the actual sample detection.CONCLUSIONSFinally, the portable sensing platform for on‐site analysis of dichlorvos concentration in farmland water samples was also demonstrated with excellent results. All the results suggested that the prepared ZrO2@PDA composites could serve as a prospective microchip sensor for the on‐site detection of dichlorvos. © 2024 Society of Chemical Industry (SCI).

Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing

Inaccuracies of traffic sensors during traffic counting and vehicle classification have persisted as transportation agencies have been prompted to calibrate sensors periodically. Detection of multiple objects, heavy occlusions, and similar appearances in congested places are some causes of computer vision model inaccuracies. This paper used the YOLOv5 model for detection and the DeepSORT model for tracking objects. Due to the nature of the reported problem caused by many misses and mismatches, the power of quantum computing with the alternating direction method of multipliers (ADMM) optimizer was leveraged. A basic Kalman filter and the Hungarian algorithm features were used in combination with a quantum optimizer to present robust multiple object tracking (MOT) algorithms. This hybrid combination of the classical and quantum model has fastened learning the occludes during frame matching of tracks and detections by generating minimum quantum cost function value. Comparisons with the existing models indicated a significant increase in the primary MOT metric multiple object tracking accuracy (MOTA) by 16% more than the regular YOLOv5-DeepSORT model when using a quantum optimizer. Also, a 6% multiple object tracking precision (MOTP) increases and a 6% identification metrics (F1) score increase were observed using the quantum optimizer with identity switching reduced from 6 to 4. This model is expected to assist transportation officials in improving the accuracy of traffic counts and vehicle classification and reduce the need for regular computer vision software calibration.

Research on human gait sensing based on triboelectric nanogenerator

To address the problem of frequent battery replacement for wearable sensors applied to fall detection among the elderly, a portable and low-cost triboelectric nanogenerator (TENG)-based self-powered sensor for human gait monitoring is proposed. The main fabrication materials of the TENG are polytetrafluoroethylene (PTFE) film, aluminum (Al) foil, and polyimide (PI) film, where PTFE and Al are the friction layer materials and the PI film is used to improve the output performance. Exploiting the ability of TENGs to monitor changes in environmental conditions, a self-powered sensor based on the TENG is placed in an insole to collect gait information. Since a TENG does not require a power source to convert physical and mechanical signals into electrical signals, the electrical signals can be used as sensing signals to be analyzed by a computer to recognize daily human activities and fall status. Experimental results show that the accuracy of the TENG-based sensor for recognizing human gait is 97.2%, demonstrating superior sensing performance and providing valuable insights for future monitoring of fall events in the elderly population.

Investigating the Mechanical Properties and Temperature Compensation of a Spot-Welded Strain Sensor within an Intelligent Steel Strand Cable

According to current regulations, welding is strictly prohibited for prestressed and tension cables. In response, this article proposes the use of a portable spot-welding machine to spot weld steel strands. This method generates a small current during spot welding, with a voltage of only 3 V to 5 V, and does not damage the internal structure of the steel strand. To effectively monitor cable tension in cable-supported structures, a novel approach utilizing a chip-based, encapsulated spot-welded strain sensor was investigated. The strain sensing capability, temperature sensitivity, stress relaxation, and static load responses were investigated on the proposed smart steel strand cables with spot-welded strain sensors. The theoretical analyses and finite element simulations revealed that the strain transfer efficiency of the spot-welded strain sensor exceeded 96%. The experimental results demonstrated that the load-strain relationship of the smart steel strand cable had a fitting degree greater than 0.999, and the tension errors obtained under different loads were within 1.26%. The tension full capacity errors measured at different temperatures were generally within 1.0%. The relaxation rate of the smart steel strand cable after 120 h was 3.78% and reduced the sensor accuracy error by 3.97%. Thus, the proposed strain sensor equipped with a smart steel strand cable is suitable for use in long-term tension monitoring.

Numerical Study of Early Detection of Tuberculosis Infected with High Sensitivity Plasmonic Sensor

In this work, a photonic crystal fiber based on a plasmonic sensor for the early detection of tuberculosis has been designed with finite element analysis. The component is constructed with a substrate layer made of fused silica material, which is then coated with a thin film of TiO2 layer as an adhesive layer to strongly attach the Au layer with the silica fiber surface. The TiO2 layer has an optimal thickness of 45 nm, while the Au layer has a thickness of 50 nm. The sensor design has a refractive index (RI) detection range from 1.27 RIU to 1.37 RIU, it also shows a maximum wavelength sensitivity (WS), maximum amplitude sensitivity (AS), sensor resolution (SR), and sensor accuracy (SA) of 20,000 nm/RIU (x-polarized) and 17.000 nm/RIU( y-polarized), -211.38 1/RIU (x-polarized) and -211.211 1/RIU (y-polarized), 9.17 x 10−5 RIU (x-polarized) and 1 x 10−4 RIU (y-polarized), and 0.025/nm respectively. Tuberculosis exhibits a normal and infected RI range of 1.343 RIU to 1.351 RIU. Therefore, the proposed sensor design is capable of detecting four types of TB infections with high sensitivity.

Low Part-Per-Trillion, Humidity Resistant Detection of Nitric Oxide Using Microtoroid Optical Resonators.

The nitric oxide radical plays pivotal roles in physiological as well as atmospheric contexts. Although the detection of dissolved nitric oxide in vivo has been widely explored, highly sensitive (i.e., low part-per-trillion level), selective, and humidity-resistant detection of gaseous nitric oxide in air remains challenging. In the field, humidity can have dramatic effects on the accuracy and selectivity of gas sensors, confounding data, and leading to overestimation of gas concentration. Highly selective and humidity-resistant gaseous NO sensors based on laser-induced graphene were recently reported, displaying a limit of detection (LOD) of 8.3 ppb. Although highly sensitive (LOD = 590 ppq) single-wall carbon nanotube NO sensors have been reported, these sensors lack selectivity and humidity resistance. In this report, we disclose a highly sensitive (LOD = 2.34 ppt), selective, and humidity-resistant nitric oxide sensor based on a whispering-gallery mode microtoroid optical resonator. Excellent analyte selectivity was enabled via novel ferrocene-containing polymeric coatings synthesized via reversible addition-fragmentation chain-transfer polymerization. Utilizing a frequency locked optical whispering evanescent resonator system, the microtoroid's real-time resonance frequency shift response to nitric oxide was tracked with subfemtometer resolution. The lowest concentration experimentally detected was 6.4 ppt, which is the lowest reported to date. Additionally, the performance of the sensor remained consistent across different humidity environments. Lastly, the impact of the chemical composition and molecular weight of the novel ferrocene-containing polymeric coatings on sensing performance was evaluated. We anticipate that our results will have impact on a wide variety of fields where NO sensing is important such as medical diagnostics through exhaled breath, determination of planetary habitability, climate change, air quality monitoring, and treating cardiovascular and neurological disorders.

Exploring the Performance of an Artificial Intelligence-Based Load Sensor for Total Knee Replacements.

Using tibial sensors in total knee replacements (TKRs) can enhance patient outcomes and reduce early revision surgeries, benefitting hospitals, the National Health Services (NHS), stakeholders, biomedical companies, surgeons, and patients. Having a sensor that is accurate, precise (over the whole surface), and includes a wide range of loads is important to the success of joint force tracking. This research aims to investigate the accuracy of a novel intraoperative load sensor for use in TKRs. This research used a self-developed load sensor and artificial intelligence (AI). The sensor is compatible with Zimmer's Persona Knee System and adaptable to other knee systems. Accuracy and precision were assessed, comparing medial/lateral compartments inside/outside the sensing area and below/within the training load range. Five points were tested on both sides (medial and lateral), inside and outside of the sensing region, and with a range of loads. The average accuracy of the sensor was 83.41% and 84.63% for the load and location predictions, respectively. The highest accuracy, 99.20%, was recorded from inside the sensing area within the training load values, suggesting that expanding the training load range could enhance overall accuracy. The main outcomes were that (1) the load and location predictions were similar in accuracy and precision (p > 0.05) in both compartments, (2) the accuracy and precision of both predictions inside versus outside of the triangular sensing area were comparable (p > 0.05), and (3) there was a significant difference in the accuracy of load and location predictions (p < 0.05) when the load applied was below the training loading range. The intraoperative load sensor demonstrated good accuracy and precision over the whole surface and over a wide range of load values. Minor improvements to the software could greatly improve the results of the sensor. Having a reliable and robust sensor could greatly improve advancements in all joint surgeries.

Tool-integrated thin-film sensor systems for measurement of cutting forces and temperatures during machining

Effective in-process control mechanisms are vital for the future development of production processes and ask for reliable monitoring of process variables. For this, new tool-integrated sensor systems for measuring cutting forces and temperature for machining of hardened steel are proposed. Thin-film technology is used to apply a thermoresistive temperature sensor in the chip-workpiece contact area for the first time on industrial cutting inserts with chip breaker geometry. Complementary piezoresistive thin-film sensors based on diamond-like carbon (DLC) and manganin layers were developed and deposited on a washer. It was placed beneath the indexable insert to measure the cutting forces. Turning experiments were conducted to study sensor life and accuracy. All sensors showed an adequate lifetime for laboratory purposes. Temperature compensation methods were investigated for force measurement and results compared to forces recorded by a dynamometric platform. While temperature compensation for the DLC-based sensor needs further optimization, results of the manganin-based sensor compared well with the expected cutting forces. The manganin thin-film sensor exhibited rapid responsiveness and great reproducibility, underscoring its prospective utility for real-time monitoring of cutting forces in machining operations.

Temperature Compensation of Laser Methane Sensor Based on a Large-Scale Dataset and the ISSA-BP Neural Network.

We aimed to improve the detection accuracy of laser methane sensors in expansive temperature application environments. In this paper, a large-scale dataset of the measured concentration of the sensor at different temperatures is established, and a temperature compensation model based on the ISSA-BP neural network is proposed. On the data side, a large-scale dataset of 15,810 sets of laser methane sensors with different temperatures and concentrations was established, and an Improved Isolation Forest algorithm was used to clean the large-scale data and remove the outliers in the dataset. On the modeling framework, a temperature compensation model based on the ISSA-BP neural network is proposed. The quasi-reflective learning, chameleon swarm algorithm, Lévy flight, and artificial rabbits optimization are utilized to improve the initialization of the sparrow population, explorer position, anti-predator position, and position of individual sparrows in each generation, respectively, to improve the global optimization seeking ability of the standard sparrow search algorithm. The ISSA-BP temperature compensation model far outperforms the four models, SVM, RF, BP, and PSO-BP, in model evaluation metrics such as MAE, MAPE, RMSE, and R-square for both the training and test sets. The results show that the algorithm in this paper can significantly improve the detection accuracy of the laser methane sensor under the wide temperature application environment.

High-Fidelity, Low-Hysteresis Bionic Flexible Strain Sensors for Soft Machines.

Stretchable flexible strain sensors based on conductive elastomers are rapidly emerging as a highly promising candidate for popular wearable flexible electronic and soft-mechanical sensing devices. However, due to the intrinsic limitations of low fidelity and high hysteresis, existing flexible strain sensors are unable to exploit their full application potential. Herein, a design strategy for a successive three-dimensional crack conductive network is proposed to cope with the uncoordinated variation of the output resistance signal arising from the conductive elastomer. The electrical characteristics of the sensor are dominated by the successive crack conductive network through a greater resistance variation and a concise sensing mechanism. As a result, the developed elastomer bionic strain sensors exhibit excellent sensing performance in terms of a smaller overshoot response, a lower hysteresis (∼2.9%), and an ultralow detection limit (0.00179%). What's more, the proposed strategy is universal and applicable to many conductive elastomers with different conductive fillers (including 0-D, 1-D, and 2-D conductive fillers). This approach improves the sensing signal accuracy and reliability of conductive elastomer strain sensors and holds promising potential for various applications in the fields of e-skin and soft robotic systems.

Blue-light-enhanced avalanche photodiode with amulti-finger grating structure for fluorescence detection.

The responsiveness of the photodetectors is critical to the accuracy of the fluorescent fiber optical temperature sensor. However, the current gain and signal-to-noise ratio (SNR) of traditional photodiodes (PDs) is low, which makes it difficult to meet the high-precision detection requirements of the system. In response to the above problems, this paper achieves a novel, to the best of our knowledge, multi-finger grating (MFG) avalanche photodiode (APD). The device combines the polysilicon gate and the space charge region formed by P+/N-Well to detect photon signals. The conversion capability of the photodetector can be significantly enhanced by the MFG structure. The principle of the device is simulated and verified by technology-computer-aided design (TCAD). The standard grating APD (SG-APD), 2-finger grating APD (2FG-APD), 3-finger grating APD (3FG-APD), and 4-finger grating APD (4FG-APD) are fabricated based on 0.18µm CMOS process. The optoelectronic detection characteristics of these devices are analyzed by establishing an optoelectronic test platform. At 480nm, the responsivity of 2FG-APD, 3FG-APD, and 4FG-APD increases by 79.3%, 96.9%, and 70.2%, respectively, compared to SG-APD (4.021A/W). The test results indicate that 3FG-APD exhibits a strong photon response in the blue light range. The device has broad application prospects in the field of fluorescence detection.

Fabrication of flexible glucose sensor based on heterostructure ZnO nanosheets decorated PU/Chitosan-PANI hybrid nanofiber

Designing and fabrication of immensely accurate, affordable, sensitive, and portable glucose sensors is rapidly increasing in clinical applications. However, commercialization of wearable sensors has been impeded by inadequate conductivity, insufficient durability, poor flexibility, and enzyme immobilization. Herein, a scalable approach for fabricating a nanofiber-based highly electroactive, flexible, and disposable (PUC-PANI-ZnO@GOx) sensor that can assess glucose stimuli with sufficient liquid permeability by anchoring ZnO nanosheets over polyaniline (PANI) coated polyurethane-chitosan (PU-Chitosan; PUC) fibrous mat followed by glucose oxidase (GOx) immobilization is described. Regarding the electrocatalytic performance, PUC-PANI-ZnO@GOx sensor showed a high sensitivity (∼998.72 µA mM−1 cm−2) and a low detection limit (∼7.31 µM) for a broad linear range of 0.01 – 9.48 mM with acceptable stability and reproducibility. The fabricated sensor was highly selective towards glucose in the presence of exogenous and endogenous interfering species. To the best of our knowledge, this novel polymer-based nanocomposite adorned on nanofiber substrate was utilized for the first time for the fabrication of a flexible glucose sensor. It can be used for producing a large-scale, cost-effective, and high-performing sensor. Hence, this sustainable and eco-friendly approach can lead to new techniques for designing next-generation wearable and implantable glucose sensors.

Improvement in efficiency of MoS2 nanoflower based ethylene gas sensor on transition metal doping: An experimental and theoretical investigation

There is an immense need for inexpensive, accurate, and repeatable ethylene sensors for post-harvest management. This paper presents resistive-type sensors based on transition metal (Ni, Fe, Co) doped molybdenum disulfide (MoS2) nanoflowers synthesized via simple hydrothermal route. A comparative study of sensing properties of pristine MoS2 and doped (Ni, Fe, Co)-MoS2 was performed in natural ambient conditions. The results reveal that Ni-doped MoS2 shows the optimized result in sensitivity as well as lower detection limit, compared to other three materials. Density functional theory (DFT) studies were carried out to evaluate interaction of ethylene molecules with MoS2 and doped MoS2. The sensing mechanism for all the materials was investigated in terms of adsorption energy, density of states (DOS), and charge transfer. The calculated results indicate large adsorption energy and higher charge transfer in the case of Ni-doped MoS2 supporting the experimental findings.

Validation of Temperature Sensors to Monitor Thermoplastic Splint Wear in Hand Surgery Patients.

The aim of this study was to validate the use of temperature sensors to accurately measure thermoplastic volar forearm splint wear in a healthy cohort of volunteers using 5- and 15-minute temperature measurement intervals. A prospective diagnostic study was performed to evaluate the diagnostic accuracy of temperature sensors in monitoring splint wear in 8 healthy volunteers between December 2022 and June 2023. Temperature sensors were molded into thermoplastic volar forearm splints. Volunteers who were familiar with the study aims were asked to keep an exact log of the time spent wearing the splint ("actual wear time"). Sensors recorded temperatures every 5 or 15 minutes, and separate algorithms were developed to determine the sensor-detected wear time compared with the actual wear time as the gold standard. The algorithms were then externally validated with the total population. The 5-minute and 15-minute algorithms demonstrated excellent sensitivity (99.1% vs 96.6%), specificity (99.9% vs 99.9%), positive (99.4% vs 99.5%) and negative (99.9% vs 99.3%) predictive value, and diagnostic accuracy (99.8% vs 99.3%), respectively. The 5-minute algorithm recorded 99.5% of the total splint hours, whereas the 15-minute algorithm recorded 96.1%. There was no significant difference between the actual time per wear session (5.4 ± 2.7 hours) and the time estimated by the 5-minute algorithm (5.4 ± 2.6 hours; P = .40), but there was a significant difference for the 15-minute algorithm (5.2 ± 2.6 hours; P < .001). Temperature sensors can be used to accurately monitor thermoplastic volar forearm splint wear. Diagnostic II.

A highly selective nickel-aluminum layered double hydroxide nanostructures based electrochemical sensor for detection of pentachlorophenol

With the widespread use of pesticides, environmental pollution has elevated to a top priority for humans. The pentachlorophenol (PCP) is one of the most dangerous chlorophenols, employed as pesticides, fungicides, and wood preservatives. In the current study, a nickel-aluminum layered double hydroxide modified glassy carbon electrode (Ni-Al-LDH@GCE) was used to build a straight forward, environmentally friendly, and accurate electrochemical sensor for the measurement of PCP. The fabricated Ni-Al-LDH was principally assessed using a variety of characterization methods to confirm its functionalities, morphology, porosity and crytallanity. The proposed Ni-Al-LDH@GCE sensor was also characterized electrochemically for the evaluation of its conductivity using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The linear dynamic range of the developed ultra-sensitive Ni-Al-LDH@GCE based electrochemical method was found as 0.05 to 50 µM at a scan rate of 50 mV/s in Britton-Robinson buffer of pH 6 for PCP. The limit of detection (LOD) and limit of quanitification (LOQ) of electrochemical sensor for PCP were determined as 0.004 μM and 0.0132 μM, respectively. The sensor's analytical suitability was evaluated using real water samples that showed the acceptable recovery values.

Takes Two to Tango: Digital Twins and AI Revolutionize Sports Science and Medicine

Purpose: Technological advancements are transforming the field of sports science and medicine, leading to a new era of performance improvement and injury prevention. Digital twins and artificial intelligence (AI) are at the forefront of these innovations, working together to redefine athletic training and monitoring. This editorial offers a comprehensive overview of the integration of digital twins and AI in sports science, with a focus on their potential applications, challenges, and future developments. By utilizing sensor data, AI algorithms, and biometrics, digital twins create virtual replicas of athletes, enabling precise performance monitoring and personalized training programs. Conclusions: Large datasets generated by AI can be used to predict and prevent injuries, as well as to enhance communication among stakeholders. Despite the promises, challenges such as privacy concerns and data accuracy need to be addressed. Future advancements will concentrate on sensor accuracy, AI algorithm refinement, and broader applications. The editorial highlights exciting research opportunities, including predictive injury models, real-time performance monitoring, and longitudinal health studies. Ultimately, the collaboration between digital twins and AI represents a paradigm shift in sports science, with the potential to revolutionize athlete well-being and performance optimization.

Emerging Trends: Nano-Scale Wireless Sensor Networks and Applications

New Adaptive Nano-Scale Sensor Network (ANSN) can quickly feel nanoscale surroundings. ANSN uses data in many scenarios to improve networks, consume less energy, and gain more accurate data. ANSI essentials are covered in detail here. This group has numerous parts. Making service better, collecting data with less energy, sending data with Q-learning, merging sensor data to increase accuracy, controlling power dynamically, and protecting data using AES are examples. Energy collection and sensor use are key to this effort. Academic research has proven that ANSN outperforms other techniques in several areas. Improvements include speed, security, latency, sensor accuracy, and network stability. With these changes, ANSN may be suitable for small wireless sensor networks.

Application of Unmanned Aerial Vehicles in Construction Industry

Technological advances in the field of electronics, such as miniature electromechanical devices and small powerful electric motors, have made it possible to develop small and light devices, such as unmanned aerial vehicles (UAVs). Recently, civilian UAVs are rapidly gaining popularity. Undoubtedly, UAVs will be used for many services in the future. There is already a growing demand for such fields of application of unmanned aerial vehicles as agriculture, emergency services, energy, fuel, mining, construction, geodesy (cartography), transportation, etc. Thanks to modern technologies it possible to produce light and low-power but accurate sensors that can be used by controllers with high computing power and low energy consumption. This makes it possible to develop complex control systems for UAVs that can be implemented on board. Today’s quadcopters are used for design, surveillance, search, construction inspections, and a variety of other applications.

Recent advances of aggregation-induced emission luminogens for point-of-care biosensing systems.

The rapid and sensitive detection of chemical compounds in body fluids and tissues is important for diagnosis of diseases and assessment of the effectiveness of treatment programs. Point-of-care (POC) sensors based on fluorescence signals have been widely used in the rapid detection of various infectious diseases. However, the aggregation-caused quenching phenomenon of conventional fluorescent probes limits the sensitivity and accuracy of fluorescent POC sensors. In this review, we first focus on aggregation-induced emission (AIE)-based POC detection for early diagnosis of diseases and then describe how to use mechanisms of AIE to improve the sensitivity of POC testing. This review gives a summary of the design mechanisms of AIE probes in AIE-based biosensors. Subsequently, it summarizes the design strategies of AIE-based POC sensors in the detection of ions, small molecules, nucleic acids, proteins, and whole entity (cells, bacteria, viruses, and exosomes), placing an emphasis on signal amplification. Finally, it gives an overview of AIE-based POC biosensors, including probes, instruments, and applications. We hope that this review will provide valuable guidance for further expanding the application of AIE luminogens in POC biosensors.