Proximity Detection Research Articles

Spatial localisation and sensing in two dimensions via metasurfaces

In this study, we introduce a two-dimensional metasurface sensor designed to detect, locate and distinguish between different objects placed in its near field. When an object is placed on the metasurface, local changes can be detected in one or more of the structure’s meta-atoms. This interaction generally modifies the inductance of the meta-atom, resulting in changes to the overall input impedance of the surface. We derive the properties of the structure and its behaviour in terms of superposition and demonstrate that observing the meta-surface from a single point is sufficient for unambiguous localisation and identification. To model these changes effectively and identify the position of an object, we employ a neural network machine learning algorithm. Our approach enables accurate localisation of all studied objects, with a precision exceeding 98%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98\\%$$\\end{document}. Additionally, the distinct signatures of the objects allow for separation between them with an accuracy of over 97%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$97\\%$$\\end{document}. The potential applications of this platform extend to foreign object detection on metasurfaces for wireless power transfer, providing proximity detection for many surfaces such as clothing, car bodies and robotic carapaces. Furthermore, our research suggests the feasibility of implementing a touchscreen type interface requiring only a single waveguide connection.

Impact of advanced endoscopy training on colonoscopy quality and efficiency.

Few reports have detailed improvements in the quality of colonoscopies with continuous training post-fellowship completion. We examined the changes in colonoscopy performance among trainees during our advanced endoscopy training program. Screening or surveillance colonoscopies performed by 11 trainees who participated in our 3-year advanced endoscopy training program between April 2015 and March 2020 were retrospectively analyzed. Quality and efficiency metrics of colonoscopies were evaluated annually. Altogether, 297, 385, and 438 colonoscopies were enrolled in the first, second, and third training years, respectively. The mean insertion times were 8.6, 7.6, and 6.9 min in the first, second, and third training years, respectively, with significant improvement from the first to second year (p = 0.03) and from the first to third year (p < 0.01). The adenoma detection rate, proximal adenoma detection rate, and mean number of adenomas per patient exhibited a tendency to improve annually; however, the difference was not significant. Polypectomy efficiency was 10.5%, 11.2%, and 13.0%, with significant improvements from the first to third year (p < 0.01) and from the second to third year (p = 0.02). Insertion time and polypectomy efficiency showed significant improvements, especially among trainees experienced with <500 colonoscopies. Through our advanced endoscopy training program, there has been an improvement in the quality and efficiency of colonoscopy for trainees who have completed their fellowships, particularly those with <500 colonoscopies.

In vitro early proximal caries detection using trilateral short-wave infrared reflection at 1050 and 1550 nm.

This in vitro study evaluated the diagnostic potential of short-wave infrared reflection (SWIRR) at 1050 and 1550 nm for proximal caries detection from the occlusal, buccal and lingual surfaces of posterior teeth under clinically relevant conditions. Bitewing radiography (BWR) was the alternative index test and micro-computed tomography (μCT) the reference standard. 250 proximal surfaces of extracted human teeth were examined using SWIRR at 1050 and 1550 nm and BWR. SWIRR, BWR and μCT findings were evaluated twice by two trained examiners. SWIRR images were evaluated from occlusal and trilateral (occlusal, buccal and lingual combined) views. Sensitivity, specificity and AUC were calculated. Reliability assessment was performed using κ statistics. SWIRR (1050 nm) showed sensitivity of 0.44 for occlusal and 0.55 for trilateral assessment, paired with specificity of 0.96 and 0.90, whereas SWIRR (1550 nm) showed sensitivity of 0.73 and 0.85 paired with specificity of 0.76 and 0.59. Compared to occlusal view, trilateral SWIRR view revealed ≈10% higher sensitivity and lower specificity. BWR revealed lowest sensitivity (0.30) and highest specificity (0.99). Over-and underestimation of caries demonstrated opposite trends: from 1050-1550 nm, overestimation of trilateral SWIRR increased (0.08-0.29), while underestimation decreased (0.15-0.06). Trilateral SWIRR has higher sensitivity and lower specificity for proximal caries, than occlusal SWIRR. 1050 nm are more suitable for trilateral SWIRR and 1550 nm for occlusal examinations. A combination of SWIRR at 1050 and 1550 nm may exhibit a balanced sensitivity and specificity for proximal caries.

Enhanced CRISPR/Cas-Based Immunoassay through Magnetic Proximity Extension and Detection.

Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas-associated systems have recently emerged as a focal point for developing next-generation molecular diagnosis, particularly for nucleic acid detection. However, the detection of proteins is equally critical across diverse applications in biology, medicine, and the food industry, especially for diagnosing and prognosing diseases like cancer, Alzheimer's and cardiovascular conditions. Despite recent efforts to adapt CRISPR/Cas systems for protein detection with immunoassays, these methods typically achieved sensitivity only in the femtomolar to picomolar range, underscoring the need for enhanced detection capabilities. To address this, we developed CRISPR-AMPED, an innovative CRISPR/Cas-based immunoassay enhanced by magnetic proximity extension and detection. This approach combines proximity extension assay (PEA) with magnetic beads that converts protein into DNA barcodes for quantification with effective washing steps to minimize non-specific binding and hybridization, therefore reducing background noise and increasing detection sensitivity. The resulting DNA barcodes are then detected through isothermal nucleic acid amplification testing (NAAT) using recombinase polymerase amplification (RPA) coupled with the CRISPR/Cas12a system, replacing the traditional PCR. This integration eliminates the need for thermocycling and bulky equipment, reduces amplification time, and provides simultaneous target and signal amplification, thereby significantly boosting detection sensitivity. CRISPR-AMPED achieves attomolar level sensitivity, surpassing ELISA by over three orders of magnitude and outperforming existing CRISPR/Cas-based detection systems. Additionally, our smartphone-based detection device demonstrates potential for point-of-care applications, and the digital format extends dynamic range and enhances quantitation precision. We believe CRISPR-AMPED represents a significant advancement in the field of protein detection.

Nanocomposite Multimodal Sensor Array Integrated with Auxetic Structure for an Intelligent Biometrics System.

A multimodal sensor array, combining pressure and proximity sensing, has attracted considerable interest due to its importance in ubiquitous monitoring of cardiopulmonary health- and sleep-related biometrics. However, the sensitivity and dynamic range of prevalent sensors are often insufficient to detect subtle body signals. This study introduces a novel capacitive nanocomposite proximity-pressure sensor (NPPS) for detecting multiple human biometrics. NPPS consists of a carbon nanotube-paper composite (CPC) electrode and a percolating multiwalled carbon nanotube (MWCNT) foam enclosed in a MWCNT-coated auxetic frame. The fractured fibers in the CPC electrode intensify an electric field, enabling highly sensitive detection of proximity and pressure. When pressure is applied to the sensor, the synergic effect of MWCNT foam and auxetic deformation amplifies the sensitivity. The simple and mass-producible fabrication protocol allows for building an array of highly sensitive sensors to monitor human presence, sleep posture, and vital signs, including ballistocardiography (BCG). With the aid of a machine learning algorithm, the sensor array accurately detects blood pressure (BP) without intervention. This advancement holds promise for unrestricted vital sign monitoring during sleep or driving.

New parameters for the capacitive accelerometer to reduce its measurement error and power consumption

Capacitive accelerometers are essential components in a wide range of electronic devices, enabling crucial functionalities such as touch sensitivity and proximity detection. Ensuring optimal accuracy is crucial for their effective performance in various applications. A key factor in this accuracy is the frequency margin, a parameter that significantly influences the sensor's ability to detect and respond to changes in capacitance.In this article, we will delve deeply into strategies aimed at optimizing capacitive sensors with a focus on improving their frequency margin. By exploring the methodologies and techniques that enhance the sensor's ability to operate within an ideal frequency range, we aim to improve the measurement accuracy of capacitive accelerometers by reducing measurement errors and power consumption. This optimization process involves meticulous calibration of sensor parameters such as sensitivity, resonance frequency, and damping factors to maximize performance under various environmental conditions. The new capacitive accelerometer structure improves sensitivity, linearity, and accuracy through advanced measurement setups and design, offering high-performance acceleration measurements suitable for various applications and reliable data collection and calibration.

‘Barely keeping the wheels on the trolley’: A qualitative study of the New Zealand COVID Tracer App

Digital contact tracing apps were developed to help control the spread of COVID-19 but research exploring these apps has underrepresented both ‘at-risk’ communities and contact tracers. Our study examines perspectives of the New Zealand COVID Tracer app among 53 participants, comprising policy advisors, contact tracers, and Māori, Pacific, and disability stakeholders, underpinned by the theory of social construction of which positions technology within the social context in which it evolves, operates, and is negotiated. Although community stakeholders believed the app helped safeguard communities from COVID-19, the health officials' views on the app's usefulness in contact tracing varied. Participants who oversaw the app's technical development generally perceived it as being more useful, particularly regarding Bluetooth proximity detection, in contrast with contact tracers' perceptions. The findings highlight a disconnection between public sentiment and operational reality in the use of the app and the need for improved collaboration and consultation in future contact tracing responses.

Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios

Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios

Echo characteristics of pulsed lasers in non-uniform smoke environments.

This work establishes a pulsed laser backscattering echo signal model based on an improved semi-analytical Monte Carlo method. The developed model is applied in non-uniform smoke environments to mitigate the smoke interference of laser fuzes for ground proximity detection. The model considers variations of the photon step size according to the concentration of the smoke environment, and the computational speed is accelerated by implementing an improved semi-analytical reception method. The proposed echo signal model is employed to investigate the effects of smoke-related parameters and laser detection system parameters on the backscattering echo waveform of smoke. Finally, the model is validated based on experiments. The results show that the intensity of the smoke backscattering echo and the emission pulse width approximately conform to a logarithmic relationship. Specifically, the echo intensity is positively correlated with the emission pulse width, and the ratio of the time between the rising edge and the falling edge of the echo signal is positively correlated with the pulse width. Meanwhile, the intensity of the smoke backscattering echo and the distance between the transmitter and receiver approximately adopt an exponential relationship. Herein, we describe the characteristics of a laser backscattering echo in a non-uniform smoke environment. The results can guide future research regarding laser fuze detection methods and strategies relevant for ground targets in smoke environments.

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection.

Persuasive technologies, in connection with human factor engineering requirements for healthy workplaces, have played a significant role in ensuring a change in human behavior. Healthy workplaces suggest different best practices applicable to body posture, proximity to the computer system, movement, lighting conditions, computer system layout, and other significant psychological and cognitive aspects. Most importantly, body posture suggests how users should sit or stand in workplaces in line with best and healthy practices. In this study, we developed two study phases (pilot and main) using two deep learning models: convolutional neural networks (CNN) and Yolo-V3. To train the two models, we collected posture datasets from creative common license YouTube videos and Kaggle. We classified the dataset into comfortable and uncomfortable postures. Results show that our YOLO-V3 model outperformed CNN model with a mean average precision of 92%. Based on this finding, we recommend that YOLO-V3 model be integrated in the design of persuasive technologies for a healthy workplace. Additionally, we provide future implications for integrating proximity detection taking into consideration the ideal number of centimeters users should maintain in a healthy workplace.

Flexible Capacitive Pressure Sensor Enhanced by Black Phosphorus-Au Nanocomposites/Ecoflex Sponge for Pressure and Proximity Detection

Flexible Capacitive Pressure Sensor Enhanced by Black Phosphorus-Au Nanocomposites/Ecoflex Sponge for Pressure and Proximity Detection

Visualising cancer in 3D: 3-Dimensional Tissue Imaging for management of cutaneous basal cell carcinoma.

Surgical management of basal cell carcinoma (BCC) typically involves surgical excision with post-operative margin assessment using the bread-loafing technique; or gold-standard Mohs micrographic surgery (MMS), where margins are iteratively examined for residual cancer after tumour removal, with additional excisions performed upon detecting residual tumour at margins. There is limited sampling of resection margins with bread loafing, with detection of positive margins 44% of the time using 2 mm intervals. To resolve this, we have developed three-dimensional (3D) Tissue Imaging for: (1) complete examination of cancer margins and (2) detection of tumour proximity to nerves and blood vessels. 3D Tissue optical clearing with a light sheet imaging protocol was developed for margin assessment in two datasets assessed by two independent evaluators: (1) 48 samples from 29 patients with varied BCC subtypes, sizes and pigmentation levels; (2) 32 samples with matching Mohs' surgeon reading of tumour margins using two-dimensional haematoxylin & eosin-stained sections. The 3D Tissue Imaging protocol permits a complete examination of deeper and peripheral margins. Two independent evaluators achieved negative predictive values of 92.3% and 88.24% with 3D Tissue Imaging. Images obtained from 3D Tissue Imaging recapitulates histological features of BCC, such as nuclear crowding, palisading and retraction clefting and provides a 3D context for recognising normal skin adnexal structures. Concurrent immunofluorescence labelling of nerves and blood vessels allows visualisation of structures closer to tumour-positive regions, which may have a higher risk for neural and vascular infiltration. Together, this method provides more information in a 3D spatial context, enabling better cancer management by clinicians.

Bioinspired electronic-skin for proximity and pressure detection in robot active sensing

Active sensing technology plays an essential role in environment–robot interactions. Inspired by the proximity sensing approach of weakly electric fish, which relies on distributed electroreceptors capable of detecting electric fields, we propose a flexible electronic skin (e-skin) for proximity and pressure detection. Conductive thermoplastic polyurethane and dielectric polyurethane are employed for fabricating flexible electrodes and substrates, respectively. An Ecoflex-based elastic layer enables proximity and pressure information to be decoupled from the electric field. The proposed e-skin can detect objects up to 160 mm away while performing real-time proximity and pressure sensing. Finally, we demonstrate that robots equipped with the e-skin can easily explore their surroundings and perform specific tasks such as recognition, avoidance, and grasping. Because of its proximity and pressure sensing capabilities and low-cost fabrication process, the e-skin has broad application potential for robot active sensing.

GARBAGE MANAGEMENT SYSTEM FOR SMART CITY “

This project presents an IoT-based smart dustbin using NodeMCU, MQ-135 gas sensor, ultrasonic sensors, a servo motor, and the Blynk platform. It features automatic lid operation via proximity detection and monitors fill level and gas concentration in real-time, displayed on the Blynk app. A custom PCB ensures reliable connections. If harmful gas levels are detected, the system triggers a buzzer and opens the lid for ventilation. This smart dustbin enhances hygiene and safety, demonstrating IoT's potential in improving waste management for households and public spaces Key Words: NodeMCU (ESP12E), Ultrasonic Sensor [2 units],7805 Voltage Regulator, MQ-135 Gas Sensor, Servo Motor 9g,Buzzer,DC Adapter (9 Volt),Soldering Iron, Solder Wire.

Proximal femur fracture detection on plain radiography via feature pyramid networks.

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics. We used a dataset of 823 hip radiographs of 150 subjects with proximal femur fractures and 362 controls to develop and evaluate the deep learning model. Our model attained 0.94 specificity and 0.95 sensitivity in fracture detection over the diverse imaging dataset. We compared the performance of our model against five benchmark FPN models, demonstrating 6-14% sensitivity and 1-9% accuracy improvement. In addition, we demonstrated that our model outperforms a state-of-the-art transformer model based on DINO network by 17% sensitivity and 5% accuracy, while taking half the time on average to process a radiograph. The developed model can aid radiologists and support on-premise integration with hospital cloud services to enable automatic, opportunistic screening for hip fractures.

Evaluation of near-infrared digital imaging transillumination compared with bitewing radiography for proximal caries detection in children.

Limitations in traditional caries detection tools have driven the development of alternatives methods, focused on the early lesion detection such as near-infrared digital imaging transillumination (NIDIT). The aim of this study was to evaluate the performance of NIDIT compared with bitewing radiography (BWR) in the detection of interproximal carious lesions in children. A retrospective audit of data from children who had NIDIT, BWR and intraoral photographs was conducted. Carious lesions were scored on a tooth surface level with BWR acting as the primary reference for comparison. Accuracy was determined using multi-class area under the curve (AUC), and correlation was determined using Fleiss' Kappa. Data from 499 tooth surfaces involving 44 children were included in this study. The average age across the participants was 86 months (~7 years) with an average dmft (decayed, missing and filled teeth in primary dentition) of 5.29. Multi-class AUC comparing NIDIT to BWR was 0.70. The correlation between NIDIT and BWR was moderate (0.43), whereas the correlation between photographic examination and BWR was 0.30, which is fair. When compared to BWR, NIDIT showed a high specificity but a low sensitivity for proximal caries detection in primary teeth.

Evaluation of the effects of postprocessing settings in digital bitewing radiographs on proximal caries detection.

Radiographs are an integral part of detecting proximal caries. The aim of this study was to evaluate the effect of contrast, brightness, noise, sharpness, and γ adjustment of digital intraoral radiographs on the diagnosis of proximal caries. In this in vitro study, 40 extracted teeth including 20 premolars and 20 molars with enamel lesions (white spot or dentin discoloration seen through the enamel) were mounted together in groups of eight inside the skull. Bitewing radiographic images of each dental group were obtained by a photostimulable phosphor plate sensor with exposure conditions of 8 mA, 70 kV, and 0.2 s. The images were reconstructed by the built-in software and examined by two oral and maxillofacial radiologists in various settings of contrast, brightness, sharpness, noise, and γ. The teeth were then cut mesiodistally and the presence or absence of caries was confirmed by an oral and maxillofacial pathologist using a stereomicroscope. The data were then analyzed using the κ agreement coefficient, sensitivity, specificity, and accuracy (α = .05). Adjustment of brightness and contrast led to higher diagnostic performance with an accuracy of 82.5% and 83.8 (for observers 1 and 2, respectively) and 82.5% (for both observers), respectively. Noise adjustment was the least helpful approach for diagnosis of proximal dental caries among other adjustments, with an accuracy of 78.8% and 77.5% for observers 1 and 2, respectively. Brightness and contrast setting was more efficient in improving the diagnostic potential of bitewing radiographs compared to other adjustments.

Comparison of near-infrared imaging with cone-beam computed tomography for proximal caries detection in permanent dentition: An in vivo study

ObjectivesThis study aimed to evaluate the diagnostic performance of near-infrared imaging (NIRI) and unaided visual examination (UVE) in detecting proximal caries in permanent dentition in comparison with cone-beam computed tomography (CBCT). MethodsPatients who underwent NIRI, UVE, and CBCT imaging within 1 week were enrolled. Using CBCT as the reference test, the positive percent agreement (PPA), negative percent agreement (NPA), and overall percent agreement (OPA) of NIRI, UVE, and a combination of the two for detecting proximal caries at different depths and in different tooth locations were assessed. Additionally, the consistency of these diagnostic methods with CBCT was evaluated. ResultsWe evaluated 6,084 proximal surfaces and identified 177 CBCT-positive sites. NIRI had a PPA, NPA, and OPA of 68.93 %, 99.09 %, and 98.21 %, respectively, with a substantial agreement with CBCT. When combined with UVE, the PPA increased by approximately 50 % compared with that of UVE alone. Regarding caries at different depths, NIRI outperformed UVE in detecting initial caries (ICDAS 1–2) over moderate-to-advanced caries (ICDAS 3–6). However, the combined use of NIRI and UVE improved the detection of moderate-to-advanced caries. In the anterior teeth region, NIRI exhibited excellent agreement with CBCT, surpassing its performance in the posterior region. ConclusionsAlthough NIRI cannot fully replace radiographic methods, the substantial agreement of NIRI with CBCT in detecting proximal caries highlights its potential as a complementary tool in routine caries screening, especially when combined with UVE. Clinical SignificanceThis study highlights the potential of NIRI as a radiation-free method for detecting proximal caries in permanent teeth. Early detection through regular NIRI scanning can lead to timely intervention, improved patient outcomes, and reduced overall disease burden.

An IoT Intelligent Approach for Safety and Efficiency of Robotic Medicine Delivery in Hospitals

In this paper, we introduce an IoT-based robotic medicine delivery system designed to boost safety and efficiency in hospital settings. This IoT-embedded robotic system integrates advanced sensors such as proximity, temperature, pressure, pulse rate, etc., sensors for ensuring real-time health and environmental monitoring behind this system. The robot is connected to a centralised controller, enabling healthcare workers to monitor patients using a cloud-based system. . Our robotic system moves about with the support of both line follower and LiDAR scanning technology for environmental safety and proximity detection. Experimental results show that the robotic system was able to navigate static obstacles and dynamically move away from the mobile entities within its reach without touching them. Besides, the proposed robotic system was able to reach the bed at the right time under normal operating conditions, which shows the efficiency of this approach in real-time settings. Patient feedback suggests that patients are very happy about the system, in terms of its usefulness, trustworthiness, and to their suprise to be taken care of by a robot. Overall, our study shows the potential of the proposed robotic system in streamlining the existing medicine delivery process in hospitals. It can potentially improve various aspects including safety, efficiency, patient compliance, and quality of care delivery. The promising results suggest further studies for incorporating advanced features and for ensuring compliance with regulatory requirements. Additional studies are to be conducted to further improve the proposed sensor-based robotic medicine delivery system, to facilitate its adoption in hospitals.

AI-Dentify: deep learning for proximal caries detection on bitewing x-ray - HUNT4 Oral Health Study.

Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has the potential to aid in the diagnosis by providing a quick and informative analysis of the bitewing images. A dataset of 13,887 bitewings from the HUNT4 Oral Health Study were annotated individually by six different experts, and used to train three different object detection deep-learning architectures: RetinaNet (ResNet50), YOLOv5 (M size), and EfficientDet (D0 and D1 sizes). A consensus dataset of 197 images, annotated jointly by the same six dental clinicians, was used for evaluation. A five-fold cross validation scheme was used to evaluate the performance of the AI models. The trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians. When compared against the dental clinicians, the YOLOv5 model shows the largest improvement, reporting 0.647 mean average precision, 0.548 mean F1-score, and 0.149 mean false negative rate. Whereas the best annotators on each of these metrics reported 0.299, 0.495, and 0.164 respectively. Deep-learning models have shown the potential to assist dental professionals in the diagnosis of caries. Yet, the task remains challenging due to the artifacts natural to the bitewing images.