Sensor System Research Articles

Cost-effective dynamic sampling in high dimensional online monitoring with advantage actor-critic

Industry 5.0 puts a spotlight on the team effort between people and machines, making the fast-paced development of IoT technology and sensor systems crucial. However, limitations such as the high cost of data acquisition, constrained bandwidth, and computational capacity can often hinder the real-time processing needed for human machine collaboration. To address this issue, we propose an integrated framework using Advantage Actor-Critic (A2C) and statistical process control (SPC) for economically monitoring high-dimensional data streams online. Our method leverages deep reinforcement learning with SPC charts to quickly identify system issues by smartly watching a few essential data streams. This approach not only saves resources but also boosts both the sustainability and efficiency of monitoring systems. Specifically, we construct a nonparametric monitoring statistic for each stream and develop a reinforcement learning framework to automatically identify the most informative and minimal number of data streams for observation at each time epoch. Our strategy is proven effective through simulations and a practical example in smart manufacturing, showcasing its superiority in reducing detection delay and minimising resources use compared to state-of-the-art algorithms.

Compact Quantum Cascade Laser-Based Noninvasive Glucose Sensor Upgraded with Direct Comb Data-Mining.

Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the finger-pricking glucometers in the market, a new sensor must first show that 95% of their glucose measurements have errors below 15% of these glucometers. Although recent innovative exploitations of the well-established Fourier-transform infrared (FTIR) spectroscopy have reached such FDA accuracy benchmarks, an FTIR spectrometer is too bulky. The advancements of quantum cascade lasers (QCLs) can lead to FTIR spectrometers of reduced size, but compact QCL-based noninvasive blood glucose sensors are not yet available. This work reports on two compact sensor system designs, both reaching the FDA accuracy benchmark. Each design commonly comprises a mid-infrared QCL for emission, a multiple attenuation total reflection prism (MATR) for data acquisition, and a computer-controlled infrared detector for data analysis. The first design translates the comb-like signals into conventional spectra, and then data-mines the resultant spectra to yield blood glucose concentrations. When a pressure actuator is employed to press the patient's hypothenar against the MATR, the sensor accuracy is considered to reach the FDA accuracy benchmark. The second design abandons the data processing step of translating combs-to-spectra and directly data-mines the "first-hand" comb signal. Beyond increasing the measurement accuracy to the FDA accuracy benchmark, even without a pressure actuator, direct comb data-mining upgrades the sensor system with speed and data integrity, which can impact the healthcare of diabetic patients. Specifically, the sensor performance is validated with 492 glucose absorption scans in the time domain, each with 20 million datapoints measured from four subjects with glucose concentrations of 3.9-7.9 mM. The sensor data-mines 164 sets of critical singularity strengths, each comprising 4 critical singularity strengths directly from the 9840 million raw signal datapoints, and the 656 critical singularity strengths are subjected to a machine-learning regression model analysis, which yields 164 glucose concentrations. These concentrations are correlated with those measured with a standard finger-pricking glucometer. An accuracy of 99.6% is confirmed from the 164 measurements with errors not more than 15% from the reference of the standard glucometer.

Predictive biomarkers of performance under stress: a two-phase study protocol to develop a wearable monitoring system

Understanding and predicting individual responses to common stressors is essential for optimising performance in high-stress environments. This article outlines a protocol for a study to identify biomarkers that predict performance...

The influence of the carbon nanofiller in the PDMS-based composites on the strain resistive effect

In this study flexible polymer nanocomposite material with different types of carbon nanotubes (CNTs) (single-walled CNT [SWCNT] or multi-walled CNT [MWCNT]) has been obtained for use in stretchable elements of flexible structures. The features of electrically conductive nanostructures and their distribution in the nanocomposite material have been studied using Raman spectrometer and scanning electron microscope, respectively. Setup for studying the functional characteristics of the nanocomposite material based on a testing machine has been developed. The setup ensures simultaneous measurement of the electrical resistance of the nanocomposite material under cyclic tension. The ultimate tensile strength of the polymer composite under 25% tension with different electrically conductive nanostructures has been determined. It has been exposed for a sample with MWCNT, the ultimate tensile strength is significantly lower. It has been revealed that the highest tensile strength had been achieved in composites at about 0.37 MPa. The strain sensitivity (gauge factor [GF]) of polydimethylsiloxane (PDMS)/MWCNT has higher GF value compared to PDMS/SWCNT. This shows a higher potential for using them as fillers for creating sensor systems based on nanocomposite conductive materials.

A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis.

Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations. The relevant papers were subjected to analysis using the PRISMA method, and 72 articles that met the criteria for this research project were identified. A detailing of the most commonly used visual sensor systems, machine learning algorithms, human gait analysis parameters, optimal camera placement, and gait parameter extraction methods is presented in the analysis. The findings of this research indicate that non-invasive depth cameras are gaining increasing popularity within this field. Furthermore, depth learning algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are being employed with increasing frequency. This review seeks to establish the foundations for future innovations that will facilitate the development of more effective, versatile, and user-friendly gait analysis tools, with the potential to significantly enhance human mobility, health, and overall quality of life. This work was supported by [GOBIERNO DE ESPANA/PID2023-150967OB-I00].

Phase Engineering of SnSeX (X = 1,2) Microstructures for High-Performance NO2 Chemiresistive Room-Temperature Sensor Systems: Toward Highly Reliable and Robust Detection Properties under Humidity and Interfering Gas Conditions.

Two-dimensional SnSeX (X = 1, 2) has emerged as a promising candidate for a NO2 chemiresistive sensor due to a remarkable affinity to NO2 gas adsorption. Although their gas sensing mechanism primarily relies on direct charge transfer, the underlying mechanisms of SnSe and SnSe2 remain unclear, despite various reported successes in phase engineering of SnSeX. Here, we investigate phase engineering of SnSeX in a hydrothermal route via 1-dodecanethiol (1-DDT), which served as a phase stabilizer, and comprehensively demonstrate phase-dependent NO2 detection properties. As the 1-DDT concentration increases, we directly confirm that the SnSe structure was gradually transformed to the SnSe2 one. This transformation correlates with a gradual increase in NO2 gas responses from 45 to 1430%, the highest value reported among SnSeX-based NO2 gas sensors. The obtained SnSe2-based sensors also exhibit a good NO2 discrimination without configuration of sensor arrays, under an interfering gas atmosphere in humidity conditions. Our computational calculation also unveils that these outstanding detection performances are attributed to well-constructed SnSe2 coupled with a single Se vacancy to enhance a stronger NO2 adsorption than SnSe. Finally, we demonstrate a sensor module system based on SnSe2, enabling real-time monitoring of NO2 gas.

An ensemble model of machine learning regression techniques and color spaces integrated with a color sensor: application to color-changing biochemical assays.

Non-destructive color sensors are widely applied for rapid analysis of various biological and healthcare point-of-care applications. However, existing red, green, blue (RGB)-based color sensor systems, relying on the conversion to human-perceptible color spaces like hue, saturation, lightness (HSL), hue, saturation, value (HSV), as well as cyan, magenta, yellow, key (CMYK) and the CIE L*a*b* (CIELAB) exhibit limitations compared to spectroscopic methods. The integration of machine learning (ML) techniques presents an opportunity to enhance data analysis and interpretation, enabling insights discovery, prediction, process automation, and decision-making. In this study, we utilized four different regression models integrated with an RGB sensor for colorimetric analysis. Colorimetric protein concentration assays, such as the bicinchoninic acid (BCA) assay and the Bradford assay, were chosen as model studies to evaluate the performance of the ML-based color sensor. Leveraging regression models, the sensor effectively interprets and processes color data, facilitating precision color detection and analysis. Furthermore, the incorporation of diverse color spaces enhances the sensor's adaptability to various color perception models, promising precise measurement, and analysis capabilities for a range of applications.

Design of a Cost-Effective Ultrasound Force Sensor and Force Control System for Robotic Extra-Body Ultrasound Imaging.

Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering more consistent, precise, and faster scans, potentially reducing human error and healthcare costs. Effective force control is crucial in robotic ultrasound scanning to ensure consistent image quality and patient safety. However, existing robotic ultrasound systems rely heavily on expensive commercial force sensors or the integrated sensors of commercial robotic arms, limiting their accessibility. To address these challenges, we developed a cost-effective, lightweight, 3D-printed force sensor and a hybrid position-force control strategy tailored for robotic ultrasound scanning. The system integrates patient-to-robot registration, automated scanning path planning, and multi-sensor data fusion, allowing the robot to autonomously locate the patient, target the region of interest, and maintain optimal contact force during scanning. Validation was conducted using an ultrasound-compatible abdominal aortic aneurysm (AAA) phantom created from patient CT data and healthy volunteer testing. For the volunteer testing, during a 1-min scan, 65% of the forces were within the good image range. Both volunteers reported no discomfort or pain during the whole procedure. These results demonstrate the potential of the system to provide safe, precise, and autonomous robotic ultrasound imaging in real-world conditions.

Analysis of Temperature and Stress Distribution on the Bond Properties of Hybrid Tailored Formed Components

Processing of hybrid materials is particularly challenging due to the different physical properties and requires extensive knowledge to produce defect‐free components. When serially combining steel and aluminum through rotary friction welding and subsequent cup backward extrusion in Tailored Forming, the strongly different yield stresses are a decisive factor. The following study analyzes the temperature and stress distribution after the compensatory heating step with additional cooling and during cup backward extrusion within a preform of a hybrid hollow shaft using experimental heating tests and finite element simulations. The influence on the bond strength is quantified by uniaxial tensile tests along the interface. Compared to uncooled samples, local cooled samples exhibit higher compressive stresses in the joining zone and hence higher forming resistance due to higher temperature gradients. Therefore, delamination and cracks can be prevented. While areas at the edge or in the center indicate reduced strengths overall, areas with high surface enlargement do not fail in the joining zone, but in the base material of the aluminum. To further enhance process stability and process control measures, a preliminary concept for the implicit determination of component temperatures using integrated sensor systems on the handling system is presented.

Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets

Major space agencies such as NASA and the ESA have long reported the growing dangers caused by resident space objects orbiting our planet. These objects continue to grow in number as satellites are imploded and space debris impacts each other, causing fragmentation. As a result, significant efforts by both the public and private sectors are geared towards enhancing space domain awareness capabilities to protect future satellites and astronauts from impact by these orbiting debris. Current approaches and standards implement very large radar arrays, telescopes, and laser ranging systems to detect and track such objects. These systems are very expensive, may take significant amounts of time to develop, and are still only sparingly able to efficiently track debris targets less than 10 cm in diameter. This work proposes a theoretical passive-radar-based method using illuminators of opportunity for detecting space debris while estimating motion direction and Doppler. We show that by using a signal processing chain based on the self-mixing technique and digital filters, Doppler information can be extracted and continuously tracked by a uniform linear receiver array. This can be achieved by a passive sensor system, which has the advantage of lower cost without the need to emit signals that constrain the spectrum sharing issues.

Clinical Characteristics and Outcomes in Heart Failure Patients with Implantable Pulmonary Artery Pressure Monitors: A Single Centre Irish Experience.

Background: Hospitalisation for acute decompensated heart failure (HF) portends a poor prognosis. Fluid retention manifesting in dyspnoea and oedema are important clinical features of decompensated heart failure and drive hospital admissions. Intracardiac and pulmonary artery pressure (PAP) monitoring can help predict heart failure decompensation, as changes in these haemodynamics occur before clinical congestion manifests. Methods: A retrospective single centre analysis of patients who underwent insertion of the Cordella™ PA Sensor System (Endotronix, Inc., Chicago, IL, USA) in University Hospital Galway, Ireland, as part of three separate clinical trials-SIRONA 1, SIRONA 2, PROACTIVE HF, was performed. The primary clinical outcome assessed was the difference between HF hospitalisation pre- and post-sensor implantation. Results: In total, there were 33 patients with symptomatic HF who underwent device insertion between 2018 and 2023. All patients had NYHA class 3 heart failure, and 48.5% (n = 16) of patients had HF with reduced ejection fraction. Only one device-related complication was noted, and no pressure sensor failures occurred. In total, there were 26 admissions for HF decompensation 1-year pre-device insertion and only three admissions post-insertion. The difference in the mean number of HF hospitalisations per patient pre- and post-device insertion was 0.70 (p < 0.0001). The difference in mean NYHA class score pre- and post-insertion was 1.0 (p < 0.001). Conclusions: Data from this single-centre cohort study have shown that the insertion of the Cordella™ PA Sensor System in symptomatic HF patients was safe and resulted in statistically significant improvements in HF hospitalisations and NYHA class.

Review—Carbon-Based Multi-Functional E-Inks for Full Printed Tattoo-Like Sensing Systems

Abstract This review explores the advancements in carbon-based multifunctional electronic inks (CMFEIs) for the development of fully printed, tattoo-like sensing systems. CMFEIs, comprising materials such as carbon nanotubes (CNTs), graphene, and carbon black, offer a unique combination of biocompatibility, mechanical flexibility, and electrical conductivity, making them ideal for wearable electronic applications. We highlight the synthesis, properties, and applications of these inks in creating sensors for monitoring physiological parameters, such as heart rate, temperature, and sweat composition. Notably, the work emphasizes the development of scalable, low-cost production methods that enable the mass production of these sensors without compromising performance. Additionally, it introduces novel fabrication techniques, such as inkjet printing and roll-to-roll processing, that enhance the resolution and flexibility of the sensors, ensuring their seamless integration with the skin. This review also addresses the environmental impact of CMFEIs, emphasizing their potential for sustainable healthcare and flexible electronics. The findings reveal CMFEIs potential to revolutionize wearable technologies by enabling the creation of highly sensitive, low-cost, and flexible sensors that could be widely used in biomedical monitoring, environmental sensing, and smart healthcare systems. This work presents a transformative outlook on the future of wearable electronics and the integration of CMFEIs in next-generation sensing technologies.

Integrated Spectral Sensitivity as Physics-Based Figure of Merit for Spectral Transducers in Optical Sensing.

The design of optical sensors aims at providing, among other things, the highest precision in the determination of the target measurand. Many sensor systems rely on a spectral transducer to map changes in the measurand into spectral shifts of a resonance peak in the reflection or transmission spectrum, which is measured by a readout device (e.g., a spectrometer). For these spectral transducers, figures of merit have been defined which are based on specific assumptions on the readout and the data analysis. In reality, however, different transducers achieve optimal performance with different types of readout. Additionally, some transducers present a more complex spectral response for which existing figures of merit do not apply. In this paper, we investigate an approach to quantifying the potential performance of a given transducer for a more general class of readout methods. Starting from the Cramér-Rao lower bound, we define a new figure of merit, the integrated spectral sensitivity, which is directly related to the physical limit of precision and applicable to a wide variety of sensing systems. We apply this analysis to two different examples of transducers. The results bring useful insights into the design of optical sensors.

Application of Unmanned Aerial Vehicle Remote Sensing System in Low Temperature Rain, Snow and Ice Disaster Monitoring

The unmanned aerial vehicle (UAV) remote sensing system, as a versatile and efficient monitoring tool, exhibits unique advantages in monitoring low-temperature rain, snow, and freezing disasters. Equipped with a variety of sensors, it is capable of collecting high-precision data even under extreme climatic conditions, thus addressing the deficiencies of traditional monitoring methods in terms of timeliness, coverage, and accuracy. Particularly when confronted with large-scale and rapidly evolving disasters such as snowstorms and ice freezes, UAVs can swiftly respond and transmit real-time data, providing disaster response departments with timely and reliable decision-making support. The application of UAV remote sensing systems in disaster data collection, processing, analysis, as well as real-time monitoring and early warning systems, renders it a central tool in contemporary disaster monitoring technology. Looking ahead, with the further advancement of remote sensing technologies and the integration of artificial intelligence and big data analytics, the role of UAVs in disaster monitoring will become even more pronounced. This paper will explore the specific applications of UAV remote sensing systems in low-temperature rain, snow, and freezing disasters, analyze the associated technical and managerial challenges and solutions, and forecast future development directions.

Design, Preparation, Characterization, Density Functional Theory, and HOMO‐LUMO Perspective of Fe3O4@SiO2‐Pr‐NH‐IC as a New Nanomagnetic Chemosensor

ABSTRACTIn this research, the Fe3O4@SiO2‐Pr‐NH‐IC magnetic nanoparticles (MNPs) were synthesized based on Fe3O4 nanoparticles. Initially, Fe3O4 was coated with tetraethylorthosilicate (TEOS) to produce Fe3O4@SiO2, which was functionalized by the reaction with 3‐aminopropyl three‐methoxy silane (APTMS) to yield Fe3O4@SiO2‐Pr‐NH2, followed by the treatment with indole‐3‐carbaldehyde (IC) to obtain Fe3O4@SiO2‐Pr‐NH‐IC as target hybrid organic and inorganic material. The Fe3O4@SiO2‐Pr‐NH‐IC was analyzed using photoluminescence spectroscopy. It was shown that this compound can selectively detect Al3+ ions in aqueous media among various cations, with a limit of detection (LOD) of 2.47 × 10−6 M. Comprehensive DFT calculations were carried out utilizing the B3LYP functional in conjunction with the 6‐311g (d,p) and LANL2DZ basis sets to analyze the ground state of the system. To elucidate the interaction mechanism, the MEP map was generated, and a full geometry optimization was performed. Additionally, the electronic properties and chemical reactivity were examined through a HOMO‐LUMO analysis at the same computational level. The findings revealed that the incorporation of the Al3+ ion significantly enhances the reactivity of the Pr‐NH‐IC + Al3+ complex in comparison to the free Pr‐NH‐IC structure. These findings suggest that Fe3O4@SiO2‐Pr‐NH‐IC has significant potential for the development of advanced sensor systems for the selective detection of Al3+ ions in aqueous environments. Future research could focus on the modification of the nanostructure to enhance its sensitivity and selectivity toward other environmentally and biologically relevant metal ions. Additionally, the integration of this material into portable sensing devices or the development of a real‐time detection system could pave the way for practical applications in environmental monitoring and water quality assessment.

Modeling of Shoulder–Elbow Movement with Exponential Parameter Identification During Walking Gaits for Healthy Subjects and Patients with Parkinson’s Disease

Background: This paper aims to complement the latest contribution in the literature that provides estimates of physiological parameters of a dynamic model for the elbow time profile during walking while linking them to a neurodegenerative disorder (Parkinsons’s disease) characterized by motor symptoms. An upper limb model is here proposed in which an active contractile element is included within a model, viewing the arm as a double pendulum system and muscles as represented by a Kelvin–Voight system. All model parameters characterizing both the shoulder and the elbow of each subject are estimated via a gradient-like identifier whose exponential convergence properties are determined by a non-anticipative Lyapunov function, ensuring robustness features. Methods: Joint angle data from different walking subjects (healthy subjects and patients with Parkinson’s disease) have been recorded using an IMU sensor system and compared with the joint angles obtained by means of the proposed model, which was adapted to each subject using available anthropometric knowledge and relying on the estimated parameters. Results: Experiments show that the reconstruction of shoulder and elbow time profiles can be definitely achieved through the proposed procedure with the estimated stiffness parameters turning out to constitute objective and quantitative indices of muscle stiffness (as a pivotal symptom of the pathology), which are able to track changes due to the therapy. Conclusions: The same dynamic model is actually able to capture the main features of the upper limb movement of both (healthy and pathological) walking subjects, with its parameters, in turn, characterizing the nature and progress of the pathology.

Flexible Site-Specific Labeling-Mediated Self-Assembly Sensor Based on Quantum Dots and LUMinescent AntiBody Sensor for Duplexed Detection of Antibodies.

Over recent years, the LUMinescent AntiBody Sensor (LUMABS) system, utilizing bioluminescence resonance energy transfer (BRET), has emerged as a highly effective method for antibody detection. This system incorporates NanoLuc (Nluc) as the donor and fluorescent protein (FP) as the acceptor. However, the limited Stokes shift of FP poses a challenge, as it leads to significant spectral cross-talk between the excitation and emission spectra. This issue complicates the implementation of multiplexed detection. To address this challenge, we present an innovative enhancement to the LUMABS sensor with quantum dots (QDs) as the acceptor instead of FP. The use of QDs offers several advantages over those of traditional FP-based sensors. The biotin-avidin system facilitates the flexible interchangeability of QDs, allowing for a more convenient multicolor sensor construct. The new QD-LUMABS system overcomes the limitations of spectral cross-talk and provides better spectral separation. This breakthrough enables the successful implementation of multiplexed detection for multiple targets simultaneously. Results demonstrated that the wavelength-tunable QD-LUMABS sensors achieved picomolar-level detection limits for antibodies and that this sensor-construction strategy was generally applicable among various epitopes and their antibodies. Furthermore, this sensor displayed excellent duplexing capabilities. These features underscore its potential for future clinical disease diagnosis applications.

Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems.

A communication network integrating multiple modes can effectively support the sustainable development of next-generation wireless communications. Integrated sensing, communication, and power transfer (ISCPT) represents an emerging technological paradigm that not only facilitates information transmission but also enables environmental sensing and wireless power transfer. To achieve optimal beamforming in transmission, it is crucial to satisfy multiple constraints, including quality of service (QoS), radar sensing accuracy, and power transfer efficiency, while ensuring fundamental system performance. The presence of multiple parametric constraints makes the problem a non-convex optimization challenge, underscoring the need for a solution that balances low computational complexity with high precision. Additionally, the accuracy of channel state information (CSI) is pivotal in determining the achievable rate, as imperfect or incomplete CSI can significantly degrade system performance and beamforming efficiency. Deep reinforcement learning (DRL), a machine learning technique where an agent learns by interacting with its environment, offers a promising approach that can dynamically optimize system performance through adaptive decision-making strategies. In this paper, we propose a DRL-based ISCPT framework, which effectively manages complex environmental states and continuously adjusts variables related to sensing, communication, and energy harvesting to enhance overall system efficiency and reliability. The achievable rate upper bound can be inferred through robust, learnable beamforming in the ISCPT system. Our results demonstrate that DRL-based algorithms significantly improve resource allocation, power management, and information transmission, particularly in dynamic and uncertain environments with imperfect CSI.

Assessing the validity of a wearable shoulder motion tracking system through comparison with dartfish in patients undergoing shoulder joint replacement surgery

Objective assessments of shoulder motion are paramount for effective rehabilitation and evaluation of surgical outcomes. Inertial Measurement Units (IMU) have demonstrated promise in providing unbiased movement data. This study is dedicated to evaluating the concurrent construct validity and accuracy of a wearable IMU-based sensor system, called 'Motion Shirt', for the assessment of humero-thoracic motion arcs in patients awaiting shoulder replacement surgery. This evaluation was conducted by comparing Motion Shirt data with the Dartfish Motion Analyzer software during the Functional Impairment Test-Hand and Neck/Shoulder/Arm (FIT-HaNSA) test. Thirteen patients (age > 50), who were awaiting shoulder replacement surgery, were recruited. The Motion Shirt was employed to measure angular humero-thoracic movements in two planes during the FIT-HaNSA test. Simultaneously, two cameras recorded the participants' movements to provide reference data. Bland-Altman plots were generated to visualize agreement between the Motion Shirt and the reference data obtained from the Dartfish Motion Analyzer software. The data analysis on Bland-Altman plots revealed a substantial level of agreement between the Motion Shirt and Dartfish analysis in measuring humero-thoracic motion. In Task-1, no significant systematic errors were exhibited, with only 3.27% and 2.18% of points exceeding the limits of agreement (LOA) in both elevation and the Plane of Elevation (POE), signifying a high level of concordance. In Task-2, a high level of agreement was also observed in Elevation, with only 3.8% of points exceeding the LOA. However, 5.98% of points exceeded LOA in POE for Task-2. In Task-3, focused on sustained overhead activity, the Motion Shirt showed strong agreement with Dartfish in Elevation (2.44% points exceeded LOA), but in POE, 7.32% points exceeded LOA. The Motion Shirt demonstrated a robust concordance with Dartfish Motion Analyzer system in assessing humerothoracic motion during the FIT-HaNSA test. These results affirm the Motion Shirt's suitability for objective motion analysis in patients awaiting shoulder replacement surgery.

Review of advanced sensor system applications in grinding operations.

Today, in a wide variety of industries, grinding operations are an extremely important finishing process for obtaining precise dimensions and meeting strict requirements for roughness and shape accuracy. However, the constant wear of abrasive tools during grinding negatively affects the dimensional and surface conditions of the workpiece. Therefore, effective monitoring of the wear process during grinding operations helps to predict tool life, plan maintenance and ensure consistent product quality. The objective of this review is to examine current tool condition monitoring techniques, both direct and indirect, in various sensor systems and their application in both traditional and AI-driven grinding processes. By examining these techniques, the review provides insight into how different monitoring techniques can improve process efficiency, reduce downtime, and improve finished product quality, as well as the application of intelligent and adaptive processes to traditional grinding operations. Key Scientific Concepts of Review: The review discusses the critical role of sensor systems in monitoring tool condition, including technologies such as imaging, vibration analysis, acoustic emission, and force measurement. These systems are vital for detecting wear and predicting failures, allowing for timely interventions and preventing unplanned downtimes. The integration of artificial intelligence into these monitoring systems greatly enhances their capabilities, as they enable more proactive strategies and adapt to changing conditions during the grinding process.