Sensor System Research Articles

Triboelectric Mat Multimodal Sensing System (TMMSS) Enhanced by Infrared Image Perception for Sleep and Emotion-Relevant Activity Monitoring.

To implement digital-twin smart home applications, the mat sensing system based on triboelectric sensors is commonly used for gait information collection from daily activities. Yet traditional mat sensing systems often miss upper body motions and fail to adequately project these into the virtual realm, limiting their specific application scenarios. Herein, triboelectric mat multimodal sensing system is designed, enhanced with a commercial infrared imaging sensor, to capture diverse sensory information for sleep and emotion-relevant activity monitoring without compromising privacy. This system generates pixel-based area ratio mappings across the entire mat array, solely based on the integral operation of triboelectric outputs. Additionally, it utilizes multimodal sensory intelligence and deep-learning analytics to detect different sleeping postures and monitor comprehensive sleep behaviors and emotional states associated with daily activities. These behaviors are projected into the metaverse, enhancing virtual interactions. This multimodal sensing system, cost-effective and non-intrusive, serves as a functional interface for diverse digital-twin smart home applications such as healthcare, sports monitoring, and security.

Pressure Difference Measuring System for Clean Rooms

This article presents the development and implementation of a pressure difference measuring system tailored for clean rooms, essential in medical facilities to control airflow and reduce nosocomial infections. The study evaluates various pressure sensors, leading to the selection of intelligent sensors that meet specific performance criteria. Key features and operating principles of the system are outlined, including sensor types, measurement techniques, and system architecture. A detailed comparison of sensors such as BMP180 and BMP280 highlights their suitability based on parameters like resolution, power consumption, and communication interfaces. The system’s structure includes dual pressure sensor units connected to a main control unit, enabling real- time pressure monitoring across different room zones. The sensors, equipped with a microcontroller for initial data processing, relay information to the control unit for further analysis and display. Designed with a calibration feature, the system ensures measurement accuracy by allowing recalibration under identical atmospheric conditions. Operating on low voltage, the system provides safe and reliable performance with applications extending to isolation rooms and environments requiring precise pressure regulation. Integrating the measuring system with ventilation systems significantly mitigates the risk of airborne disease transmission, contributing to the maintenance of sterile environments. The article concludes that this cost-effective, intelligent pressure monitoring solution not only enhances safety in healthcare settings but also supports regulatory compliance and improves overall infection control measures in high-risk areas.

Spatial extension of soil water regime variables derived from soil moisture values using geomorphological variables in Hungary

Accurate measurement and spatial extension of soil properties are essential in geoinformatics and precision agriculture for effective resource management, particularly irrigation planning. This study addresses the challenge of extending soil moisture data and related soil water regime variables in heterogeneous agricultural landscapes by integrating geomorphological variables (GVs) derived from high-resolution digital elevation models (DEM). In digital soil mapping, machine learning and geostatistical models often struggle with validation due to data scarcity and variability across space through many geographical regions that come from the point readings of soil properties. A different approach was developed in the form of a new methodology combining two hourly Sentek soil moisture measurements from the topsoil with DEM-derived GVs to model and extend soil water regime variables. The research was conducted on an agricultural field in a hilly area with diverse geomorphological variability. The model’s performance was validated using cross-validation techniques. The monitoring and spatial extension results indicate that GVs enhance the spatial prediction of soil moisture, capturing periodic fluctuations in the upper soil layer more effectively by using in-situ, time series soil moisture sensor readings rather than traditional, on field, one time reading approaches. We observed that certain GVs, such as the slope, both type of curvatures and the convergence, were strong predictors of soil moisture variation, enabling the model to produce more accurate irrigation recommendations for agricultural areas with similar geomorphological areas. One of the soil water regime variables was validated during the preliminary validation with mixed results. The main issue was coming from the field use and spatial scarcity of the measurements. Our approach not only provides a different method for spatially extending the current soil water regime data but also offers a framework for improving irrigation decision-making with the help of other value rates and limit related soil regime variables derived from the time series readings from the soil moisture sensors. With its variables, the model allows for forecasts of soil moisture changes, which can inform better irrigation scheduling and water resource management, all based on data from the soil monitoring sensor system.

Luminescent Tactile Sensor System for Robots: Enhancing Human-Computer Interaction in Complex Dark Environments.

In order to achieve interaction and collaboration with humans, robots need to have the ability for tactile perception of simulating human. Traditional methods use electrically connected sensors with complex arrays, leading to intricate wiring, high manufacturing costs, and demanding current environments. A flexible sensor with simple structure, easy preparation process, and low cost based on triboluminescence effect is proposed in this paper, which avoids the complex array and wiring of traditional sensors. The study discusses the relationship between luminescent intensity and factors such as luminescent particle content, luminescent layer thickness, encapsulation layer thickness, and friction layer thickness. It also analyzes the mechanism of luminescence. A micro charge-coupled device is configured for the luminescent unit to collect optical information and is integrated with the robot's manipulator for testing. A simple sensing system is constructed to demonstrate environmental perception, acquiring, and feeding back shape and size information of contact objects in the dark. The system successfully identifies and judges target objects in complex dark environments, offering insights for applications such as unmanned assembly lines. It overcomes the challenge of intricate electrical connections, paving new avenues for intelligent object recognition research in human-computer interaction.

Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap

BackgroundThe age distribution of a mosquito population is a major determinant of its vectorial capacity. To contribute to disease transmission, a competent mosquito vector, carrying a pathogen, must live longer than the extrinsic incubation period of that pathogen to enable transmission to a new host. As such, determining the age of female mosquitoes is of significant interest for vector-borne diseases surveillance and control programs.MethodsIn this contribution, an automated age-grading system was developed to classify the age of female Culex pipiens, which is the primary vector of West Nile virus and other pathogens of medical and veterinary importance in northern latitudes. The system comprises an optical wingbeat sensor coupled to the entrance of a mosquito trap and a machine learning model. Three age classes were used in the study: young (2–4 days), middle (7–9 days) and old (14–16 days). From a balanced dataset of flight data, four features were extracted: wingbeat fundamental frequency, spectrogram, power spectral density and Mel frequency cepstral coefficients. The features were used for training with the XGBoost algorithm to generate a model for age classification.ResultsThe best performing model was trained with the power spectral density feature on two age classes, ≤ 4 days old and ≥ 7 days old, and had an accuracy of 71.8%.ConclusionsAn automated mosquito age-grading system was applied for the first time to our knowledge for automated age classification in mosquitoes; and complements the mosquito genus and sex classification capability of the system reported in our previous work. The system may find use in mosquito-borne disease surveillance and control to help to discriminate young mosquitoes (≤ 4 days old) from older mosquitoes, which may act as vectors of arboviruses.Graphical

How smooth is your ride? Comparison of sensors and methods for surface quality assessment using IMUs

As a major component of riding comfort, surface roughness has a significant impact on peoples' decision to ride bicycles. Riding comfort is most commonly derived from accelerations measured by inertial measurement units (IMUs). However, roughness metrics from different works are not directly comparable as no ‘benchmark data’ exists. This work aims at alleviating this problem by comparing several well-established methods from literature on the same data. Furthermore, to quantify the effect of different sensor systems, for each test run data from both a smartphone and an industrial grade IMU were collected. To compare the derived roughness measurements, the reliability and stability of each sensor-method combination is calculated using non-parametric statistics. The results indicate handlebar mounted smartphones to be sufficient for surface roughness assessment. Furthermore, the selected roughness calculation method has the biggest impact on resulting assessments, above the impacts of both sensor and analyzed segment length. Based on the results, recommendations for surface roughness assessment are provided in the conclusion.

Residual Stress Analysis at the Conductor–Insulator Interface During the Curing Process of Hair-Pin Motors

The curing process of hair-pin motor stator insulation is critical, as residual stress increases the risk of partial discharge and shortens a motor’s lifespan. However, studies on the stress-induced defects during insulation varnish curing remain limited. This research integrates three-dimensional numerical simulations and experimental analysis to develop a curing model based on unsaturated polyester imide resin, aiming to explore the mechanisms of residual stress formation and optimization strategies. A dual fiber Bragg grating (FBG) sensor system is employed for simultaneous temperature and strain monitoring, while curing kinetics tests confirm the self-catalytic nature of the process and yield the corresponding kinetic equations. The multi-physics simulation model demonstrates strong agreement with the experimental data. The results show that optimizing the curing process reduces the maximum stress from 45.1 MPa to 38.6 MPa, effectively alleviating the stress concentration. These findings highlight the significant influence of the post-curing temperature phase on residual stress. The proposed model offers a reliable tool for stress prediction and process optimization in various insulating materials, providing valuable insights for motor insulation system design.

A portable low‐cost incubation chamber for real‐time characterization of soil respiration

AbstractMonitoring CO2 or O2 concentrations within a closed, volume‐defined chamber is widely used to quantify soil respiration during laboratory soil incubation experiments. The standard method of using periodic manual gas sampling is costly, labor‐intensive, and frequently fails to capture the aerobic respiration process. Thus, tools that allow continuous, real‐time tracking of CO2 and O2 concentration changes are needed for soil respiration research. This study presents a new, portable, low‐cost (∼$700), open‐source sensor system to measure CO2 and O2 concentrations in four closed chambers. We provided non‐engineering end‐users with step‐by‐step instructions on how to build the system, enabling replication and customization. System performance was tested by comparing two respiration rates using the same soil—soil with and without glucose added for 1 week. Consistent CO2 production and O2 consumption rates were measured at 1‐min intervals, and the reliability of the system was validated by a trace gas analyzer. Two distinctive continuous apparent respiratory quotient time series between two soil treatments were observed, with higher values of CO2 in glucose soil, demonstrating the ability of the system to capture ongoing respiration processes and sufficient sensitivity to distinguish differences among respiration substrates (i.e., glucose). The tested performance of the system highlights its capabilities for soil respiration research and the potential for further adoption in real‐time gas monitoring applications.

IoRT-Based Middleware for Heterogeneous Multi-Robot Systems

The concurrence of social robots with different functionalities and cyber-physical systems in indoor environments has recently been increasing in many fields, such as medicine, education, and industry. In such scenarios, the collaboration of such heterogeneous robots demands effective communication for task completion. The concept of the Internet of Robotic Things (IoRT) is introduced as a potential solution, leveraging technologies like Artificial Intelligence, Cloud Computing, and Mesh Networks. This paper proposes an IoRT-based middleware that allows the communication of different types of robot operating systems in dynamic environments, using a cloud-based protocol. This middleware facilitates task assignment, training, and planning for heterogeneous robots, while enabling distributed communication via WiFi. The system operates in two control modes: local and cloud-based, for flexible communication and information distribution. This work highlights the challenges of current communication methods, particularly in ensuring information reach, agility, and handling diverse robots. To demonstrate the middleware suitability and applicability, an implementation of a proof-of-concept is shown in a touristic scenario where several guide robots can collaborate by effectively sharing information gathered from their heterogeneous sensor systems, with the aid of cloud processing or even internal communication processes. Results show that the performance of the middleware allows real-time applications for heterogeneous multi-robot systems in different domains.

HIF-2α level in adolescents with chronic inflammatory pathology of the upper gastrointestinal tract

Сhronic inflammatory diseases of the gastrointestinal tract are among the most common pathological conditions in adolescents. A significant role in the pathogenesis of gastrointestinal tract diseases is given to the functioning of the oxygen sensor system, the main mediator of which is hypoxia-inducible factor 2 (HIF-2α). Therefore, the purpose of the study was to determine the level of HIF-2α in the plasma of patients with chronic inflammatory pathology of the gastrointestinal tract, taking into account the endoscopic picture of the lesion, localization of the pathological process, age and gender of the patient. The study involved 70 adolescents aged 8-18 years with chronic gastroduodenitis (CGD), gastroesophageal reflux disease (GERD), gastric or duodenal ulcer and 25 peers who had been classified as healthy. The plasma concentration of HIF-2α was measured by a sandwich-linked ELISA. Helicobacter pylori infection was determined by urease test or by ELISA. According to the data obtained, the HIF-2α plasma level was higher in patients with chronic inflammatory gastrointestinal disease compared to the control group. In boys with chronic gastroduodenal disease, the level of HIF-2α was higher than in girls. CGD and GERD were characterized by a higher HIF-2α level in plasma than gastric and duodenal ulcers. The HIF-2α level did not depend on the age of the patients or the presence of Helicobacter pylori infection. Keywords: adolescents, chronic gastroduodenitis, gastroesophageal reflux disease, gastrointestinal tract, HIF-2α

A High-Precision DOA Estimator for Low-Frequency Signals Using Ultra-Dense Small-Aperture Microphone Arrays

Abstract Accurate Direction of Arrival (DOA) estimation is critical for the effectiveness of Unattended Ground Sensor (UGS) systems, as it enhances sound localization, situational awareness, resource optimization, and facilitates integration with other sensor data for comprehensive monitoring. With the growing demand for lightweight and miniaturized sensors suitable for diverse environments, challenges arise in DOA estimation of low-frequency signals using dense small-aperture microphone arrays, especially under noisy conditions. Despite advancements in deep learning, both conventional and existing neural network methods struggle with this task. In this paper, we present the Multi-Resblock DOA Network (MRDNet), a novel neural network designed for precise DOA estimation of low-frequency sounds in noisy environments using small-aperture arrays. MRDNet was evaluated under simulations involving Brownian and Gaussian noise, representing wind and general background disturbances. The results demonstrate that MRDNet achieves superior accuracy, with a Mean Angular Error (MAE) of 2.797 degrees, significantly outperforming baseline methods by 24.93\%. Furthermore, we show that increasing the number of microphones within a constant array size using MRDNet effectively enhances DOA accuracy in the context of deep learning.

Soft and Integrable Multimodal Artificial Mechanoreceptors Toward Human Sensor of Skin

AbstractHerein, the development and characterization of three distinct artificial mechanoreceptor sensors meticulously engineered is reported to emulate human skin. By mimicking the morphology, structure, and response characteristics (including preferential sensitivity, adaptation profile, and frequency response) of biological mechanoreceptors, artificial Meissner, Merkel, and Ruffini sensors capable of detecting pressure, shear, and tensile deformations with high fidelity are successfully fabricated. In situ experiments, designed to mimic physiological conditions, demonstrate that the integrated sensor array, mimicking human fingertips, can accurately discriminate seven Braille characters, five distinct surface textures, a grating with ridges, and four‐step delivery stages of an object. Furthermore, a woolen glove incorporating 15 multimodal sensors are developed, which exhibits enhanced classification capabilities for eight objects of varying sizes and surface roughness. Notably, the trimodal sensor integration demonstrates superior recognition speed and precision compared to uni‐ or bimodal configurations, while also improving tactile identification intuition. This biomimetic mechanoreceptor sensor system demonstrates comprehensive and synergistic recognition of diverse stimuli and objects, potentially overcoming technological limitations in applications requiring human‐like tactile perception, such as advanced prosthetics, robotics, and immersive augmented and virtual reality interfaces.

Tool pose measurement using shadow imaging sensors

AbstractDue to the high positioning uncertainty and low stiffness of robots, significant geometrical deviations occur in robotic incremental sheet forming (ISF) which requires an optical in-process tool pose measurement. To achieve a measuring uncertainty of less than 50 $$\upmu $$ μ m for the position components and less than 0.05$$^\circ $$ ∘ for the orientation components in the machining volume, a multi-sensor system based on shadow imaging sensors is proposed. To calculate the six-degree-of-freedom tool pose, the sensor system measures the positions of three LEDs attached to the tool. The LEDs emit light with different colors, and the associated shadows are separately detected by using color filters. In addition, each sensor consists of a shadow-casting mask and a monochrome camera. Experimental results show that the systematic error dominates the measurement uncertainty budget and needs to be calibrated, while the random error is one order of magnitude smaller than the required measuring uncertainty. Furthermore, the color filters reduce the cross-sensitivities between the signals to an acceptable level. Final experiments on a robot demonstrate plausible pose measurements indicating the feasibility to apply the multi-sensor system in robotic ISF.

High-Performance All-Inorganic Perovskite Tandem Photodetectors With Bi2TeO6 Layer Enabled by Enhanced Dual Pyro-Phototronic Effect for Triple-Encryption Imaging Sensing System with Programmable Logic Gate.

As the focus on information security continues to intensify, encrypted imaging sensing technology becomes increasingly indispensable. However, the widespread application of encrypted imaging sensing technology is hindered by high manufacturing costs and complex system construction. Herein, an all-inorganic perovskite/perovskite tandem self-powered photodetector (PDs) is reported, incorporated with Bi2TeO6 layer to enahnce dual pyro-phototronic effect, to realize an innovated encryption imaging sensing system with programmable logic gate. The PDs reach responsivity of 1.46 A W-1 and detectivity of 3.44 × 1013 Jones, the corresponding EQE reaches 342%, which is ≈75.48 times greater than that without pyro-phototronic effect. The outstanding performance resulting from dual pyro-phototronic effect in Cs2PbI2Cl2 perovskite and Bi2TeO6/CsSn0.5Pb0.5IBr2 interface, and optimizing band arrangement from the oxidized Bi2TeO6 layer. The photovoltaic and pyro-phototronic signal of PDs can still be clearly distinguished under 8 kHz pulsed laser. More importantly, the encryption imaging sensing system achieves simultaneous outputs of triple image information, and their programmable optoelectronic logic gate can executesix distinct logical functions, including "OR", "AND", "XOR", "NAND", "NOR" and "XNOR". This research not only provides a feasible strategy for realizing the outstanding pyro-phototronic effect of all-inorganic perovskite materials, but also presents novel insights for designingencrypted imaging sensing technology.

Analysis of the Coupling Mechanism of Liquid Cylinder Vibration Based on the Low-Speed Operating Conditions of Neural Network Model

The connection between the friction negative and related damping is on the low-speed hydraulic cylinder, which will generate the vibration and influence of the hydraulic cylinder, which inhibits the accuracy of the hydraulic equipment. In order to effectively solve this problem, this paper creates a neural network model, considering the negative damping and related damping effects under low-speed conditions, and intended the coupling mechanism of negative damping and related damping. Based on the converter of the hydraulic cylinder, the nonlinear stiffness characteristics of the sensor hydraulic cylinder were analyzed, and the requirements of the hydraulic cylinder collection were obtained according to the requirements of the energy star collection of the liquid voltage star, and the equivalent of the sensor liquid pressure cylinder was obtained. At the same time, the sensor system implements feedback control on vibration velocity and displacement based on changes in piston position, in order to analyze the coupling mechanism of hydraulic cylinder vibration under low-speed operating conditions. The results of the study show that the function of the feedback controller is systematic, and the antipity control contains the possibility of minimizing serious damage and can effectively improve the efficiency and stability of the system. In the actual manufacturing process, we studied the vibration coupling effect of the liquid cylinder under low-speed conditions, proved the change characteristics of the vibration amplitude and frequency of the hydraulic cylinder, and revealed the related damping in the vibration.

Toward Stretchable Flexible Integrated Sensor Systems.

Skin-like flexible sensors hold great potential as the next generation of intelligent electronic devices owing to their broad applications in environmental monitoring, human-machine interfaces, the Internet of Things, and artificial intelligence. Flexible electronics inspired by human skin play a vital role in continuous and real-time health monitoring. This review summarizes recent progress in skin-mountable electronics developed by designing flexible electrodes and substrates into different structures, including serpentine, microcrack, wrinkle, and kirigami. Furthermore, this review briefly discusses advances in wearable integrated sensor systems that mimic the flexibility of human skin, as well as multisensing functions. In the future, innovations in stretchable integrated sensor systems will be crucial to develop next-generation intelligent skin-based sensors for practical applications such as medical diagnosis, treatment, and environment monitoring.

Groundwater potential zones delineation in Oued Zdin basin using GIS, RS and hierarchical analysis process

Water scarcity poses a significant challenge, particularly in regions with limited rainfall such as Algeria, where groundwater plays a crucial role in supporting both daily life and economic activities. This research aims to evaluate the groundwater potential in the Oued Zdin basin in northern Algeria by utilizing advanced geomatics methods, particularly the Analytic Hierarchy Process (AHP). Through the integration of Remote Sensing (RS) and Geographic Information Systems (GIS), the study incorporates multiple datasets, including rainfall patterns, topography, geology, drainage networks, land use, and hydrological data to assess areas with high groundwater potential. By applying AHP, the study assigns relative importance to these factors, creating a groundwater potential map that classifies the region into very high, high, low, and poor potential zones. The results indicate that 7% of the basin has very high potential for groundwater recharge, 33% has high potential, while 56% is categorized as low potential, and 4% falls under poor potential. The accuracy of the results is validated through comparison with existing well data, which aligns with the identified high-potential zones. The research demonstrates that combining GIS, RS, and AHP is an effective approach for mapping groundwater potential, offering valuable insights for sustainable water resource management in areas experiencing water scarcity. This methodology presents a scalable model that can be applied to similar regions facing groundwater challenges.

Soft sensor system for in-process eddy current microstructure characterization

Abstract. Non-destructive testing methods can be used for material characterization and quality control during manufacturing processes. For process control applications, a fast transformation of the measured signal to a small set of characteristic quantities is required. In this work, a soft sensor system based on digital data processing of an eddy current sensor system is developed and implemented. The analytical model for the voltage transfer function is based on an equivalent circuit for the sensor system. The data processing is implemented in software, and the resulting soft sensor is tested in experiments to show that it is possible to obtain useful material data for metals using comparatively simple sensor hardware in quasi real time.

Cognitive behavioral therapy for pediatric obsessive-compulsive disorder delivered via internet videoconferencing: a manualized sensor-assisted feasibility approach

BackgroundBetween 1 and 4% of children and adolescents suffer from obsessive-compulsive disorder (OCD) worldwide, but the majority of these young people do not have access to cognitive behavioral therapy (CBT) as a first-line treatment. CBT delivered via online videoconferencing (vCBT) offers a new way to provide young people with therapy, especially in the home environment where symptoms usually occur.MethodsIn this study, we investigated the feasibility of a newly revised vCBT manual, symptom change during treatment, and effects on family life and social functioning. 20 patients with OCD, aged 12–18 years, were treated during 14 weekly sessions while using a multimodal sensor system that assessed their physiological and behavioral responses during therapy. Treatment was delivered in real time via an online videoconferencing platform. Measurements of feasibility, acceptance, and implementation were evaluated descriptively, and clinical measures were assessed with t tests.ResultsThe primary results showed that patients and parents perceived the manual-based vCBT as feasible and easy to understand. According to the therapists’ ratings, all treatment modules and the content could be carried out in accordance with the manual. As a secondary outcome, OCD symptoms improved significantly during treatment (p <.001, d = 1.87), revealed by an average decrease of more than half in the Children’s Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) score. As the psychotherapy could be implemented directly in the patients’ home environment, low barriers to participation were reported, and the majority of participants reported improvements in family life after treatment.ConclusionsIn summary, the results of this feasibility study indicated a successful application of manual-based psychotherapy delivered via videoconferencing for pediatric OCD supported by a sensor system. This method should be further investigated in future randomized controlled trials with larger patient samples.Clinical trial registration: [www.ClinicalTrials.gov], identifier [NCT05291611], first submission: 2021-12-10.

Fully Automated and AI-Assisted Optical Fiber Sensing System for Multiplexed and Continuous Brain Monitoring.

Continuous and comprehensive brain monitoring is crucial for timely identification of changes or deterioration in brain function, enabling prompt intervention and personalized treatments. However, existing brain monitoring systems struggle to offer continuous and accurate monitoring of multiple brain biomarkers simultaneously. This study introduces a multiplexed optical fiber sensing system for continuous and simultaneous monitoring of six cerebrospinal fluid (CSF) biomarkers using tip-functionalized optical fibers and computational algorithms. Optimized machine learning models are developed and integrated for real-time spectra analysis, allowing for precise and continuous readout of biomarker concentrations. The developed machine learning-assisted fiber optic sensing system exhibits high sensitivity (0.04, 0.38, 0.67, 2.62, 0.0064, 0.33 I/I0 change per units of temperature, dissolved oxygen, glucose, pH, Na+, Ca2+, respectively), reversibility, and selectivity toward target biomarkers with a total diameter less than 2.5 mm. By monitoring brain metabolic and ionic dynamics, this system accurately identified brain physiology deterioration and recovery using ex vivo traumatic brain injury models. Additionally, the system successfully tracked biomarker fluctuations in clinical CSF samples with high accuracy (R2 > 0.93), demonstrating excellent sensitivity and selectivity in reflecting disease progression in real time. These findings underscore the enormous potential of automated and multiplexed optical fiber sensing systems for intraoperative and postoperative monitoring of brain physiologies.