Sensors

23 pages, 368 KiB

Open AccessArticle

Specification of Self-Adaptive Privacy-Related Requirements within Cloud Computing Environments (CCE)

by Angeliki Kitsiou, Maria Sideri, Michail Pantelelis, Stavros Simou, Aikaterini-Georgia Mavroeidi, Katerina Vgena, Eleni Tzortzaki and Christos Kalloniatis

Sensors 2024, 24(10), 3227; https://doi.org/10.3390/s24103227 (registering DOI) - 19 May 2024

Abstract

This paper presents a novel approach to address the challenges of self-adaptive privacy in cloud computing environments (CCE). Under the Cloud-InSPiRe project, the aim is to provide an interdisciplinary framework and a beta-version tool for self-adaptive privacy design, effectively focusing on the integration [...] Read more.

This paper presents a novel approach to address the challenges of self-adaptive privacy in cloud computing environments (CCE). Under the Cloud-InSPiRe project, the aim is to provide an interdisciplinary framework and a beta-version tool for self-adaptive privacy design, effectively focusing on the integration of technical measures with social needs. To address that, a pilot taxonomy that aligns technical, infrastructural, and social requirements is proposed after two supplementary surveys that have been conducted, focusing on users’ privacy needs and developers’ perspectives on self-adaptive privacy. Through the integration of users’ social identity-based practices and developers’ insights, the taxonomy aims to provide clear guidance for developers, ensuring compliance with regulatory standards and fostering a user-centric approach to self-adaptive privacy design tailored to diverse user groups, ultimately enhancing satisfaction and confidence in cloud services. Full article

(This article belongs to the Section Sensor Networks)

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20 pages, 8443 KiB

Open AccessArticle

A Rapid Localization Method Based on Super Resolution Magnetic Array Information for Unknown Number Magnetic Sources

by Linliang Miao, Tianyi Zhang, Chao Zuo, Zijie Chen, Xiaofei Yang and Jun Ouyang

Sensors 2024, 24(10), 3226; https://doi.org/10.3390/s24103226 (registering DOI) - 19 May 2024

Abstract

A rapid method that uses super-resolution magnetic array data is proposed to localize an unknown number of magnets in a magnetic array. A magnetic data super-resolution (SR) neural network was developed to improve the resolution of a magnetic sensor array. The approximate 3D [...] Read more.

A rapid method that uses super-resolution magnetic array data is proposed to localize an unknown number of magnets in a magnetic array. A magnetic data super-resolution (SR) neural network was developed to improve the resolution of a magnetic sensor array. The approximate 3D positions of multiple targets were then obtained based on the normalized source strength (NSS) and magnetic gradient tensor (MGT) inversion. Finally, refined inversion of the position and magnetic moment was performed using a trust region reflective algorithm (TRR). The effectiveness of the proposed method was examined using experimental field data collected from a magnetic sensor array. The experimental results showed that all the targets were successfully captured in multiple trials with three to five targets with an average positioning error of less than 3 mm and an average time of less than 300 ms. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Object Tracking—2nd Edition)

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15 pages, 5458 KiB

Open AccessArticle

Muscle Synergy during Wrist Movements Based on Non-Negative Tucker Decomposition

by Xiaoling Chen, Yange Feng, Qingya Chang, Jinxu Yu, Jie Chen and Ping Xie

Sensors 2024, 24(10), 3225; https://doi.org/10.3390/s24103225 (registering DOI) - 19 May 2024

Abstract

Modular control of the muscle, which is called muscle synergy, simplifies control of the movement by the central nervous system. The purpose of this study was to explore the synergy in both the frequency and movement domains based on the non-negative Tucker decomposition [...] Read more.

Modular control of the muscle, which is called muscle synergy, simplifies control of the movement by the central nervous system. The purpose of this study was to explore the synergy in both the frequency and movement domains based on the non-negative Tucker decomposition (NTD) method. Surface electromyography (sEMG) data of 8 upper limb muscles in 10 healthy subjects under wrist flexion (WF) and wrist extension (WE) were recorded. NTD was selected for exploring the multi-domain muscle synergy from the sEMG data. The results showed two synergistic flexor pairs, Palmaris longus–Flexor Digitorum Superficialis (PL-FDS) and Extensor Carpi Radialis–Flexor Carpi Radialis (ECR-FCR), in the WF stage. Their spectral components are mainly in the respective bands 0–20 Hz and 25–50 Hz. And the spectral components of two extensor pairs, Extensor Digitorum–Extensor Carpi Ulnar (ED-ECU) and Extensor Carpi Radialis–Brachioradialis (ECR-B), are mainly in the respective bands 0–20 Hz and 7–45 Hz in the WE stage. Additionally, further analysis showed that the Biceps Brachii (BB) muscle was a shared muscle synergy module of the WE and WF stage, while the flexor muscles FCR, PL and FDS were the specific synergy modules of the WF stage, and the extensor muscles ED, ECU, ECR and B were the specific synergy modules of the WE stage. This study showed that NTD is a meaningful method to explore the multi-domain synergistic characteristics of multi-channel sEMG signals. The results can help us to better understand the frequency features of muscle synergy and shared and specific synergies, and expand the study perspective related to motor control in the nervous system. Full article

(This article belongs to the Section Biomedical Sensors)

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18 pages, 7382 KiB

Open AccessArticle

Sensor Fault Reconstruction Using Robustly Adaptive Unknown-Input Observers

by Qiang Huang, Zhi-Wei Gao and Yuanhong Liu

Sensors 2024, 24(10), 3224; https://doi.org/10.3390/s24103224 (registering DOI) - 19 May 2024

Abstract

Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a [...] Read more.

Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a novel estimation technique, called adaptive unknown-input observer, is proposed to simultaneously reconstruct sensor faults as well as system states. Specifically, the unknown input observer is used to decouple partial disturbances, the un-decoupled disturbances are attenuated by the optimization using linear matrix inequalities, and the adaptive technique is explored to track sensor faults. As a result, a robust reconstruction of the sensor fault as well as system states is then achieved. Furthermore, the proposed robustly adaptive fault reconstruction technique is extended to Lipschitz nonlinear systems subjected to sensor faults and unknown input uncertainties. Finally, the effectiveness of the algorithms is demonstrated using an aircraft system model and robotic arm and comparison studies. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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20 pages, 4529 KiB

Open AccessArticle

Risk Evaluation and Attack Detection in Heterogeneous IoMT Devices Using Hybrid Fuzzy Logic Analytical Approach

by Pritika, Bharanidharan Shanmugam and Sami Azam

Sensors 2024, 24(10), 3223; https://doi.org/10.3390/s24103223 (registering DOI) - 19 May 2024

Abstract

The rapidly expanding Internet of Medical Things (IoMT) landscape fosters enormous opportunities for personalized healthcare, yet it also exposes patients and healthcare systems to diverse security threats. Heterogeneous IoMT devices present challenges that need comprehensive risk assessment due to their varying functionality, protocols, [...] Read more.

The rapidly expanding Internet of Medical Things (IoMT) landscape fosters enormous opportunities for personalized healthcare, yet it also exposes patients and healthcare systems to diverse security threats. Heterogeneous IoMT devices present challenges that need comprehensive risk assessment due to their varying functionality, protocols, and vulnerabilities. Hence, to achieve the goal of having risk-free IoMT devices, the authors used a hybrid approach using fuzzy logic and the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate risks, providing effective and useful results for developers and researchers. The presented approach specifies qualitative descriptors such as the frequency of occurrence, consequence severity, weight factor, and risk level. A case study with risk events in three different IoMT devices was carried out to illustrate the proposed method. We performed a Bluetooth Low Energy (BLE) attack on an oximeter, smartwatch, and smart peak flow meter to discover their vulnerabilities. Using the FAHP method, we calculated fuzzy weights and risk levels, which helped us to prioritize criteria and alternatives in decision-making. Smartwatches were found to have a risk level of 8.57 for injection attacks, which is of extreme importance and needs immediate attention. Conversely, jamming attacks registered the lowest risk level of 1, with 9 being the maximum risk level and 1 the minimum. Based on this risk assessment, appropriate security measures can be implemented to address the severity of potential threats. The findings will assist healthcare industry decision-makers in evaluating the relative importance of risk factors, aiding informed decisions through weight comparison. Full article

(This article belongs to the Section Internet of Things)

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14 pages, 2478 KiB

Open AccessArticle

MRD-YOLO: A Multispectral Object Detection Algorithm for Complex Road Scenes

by Chaoyue Sun, Yajun Chen, Xiaoyang Qiu, Rongzhen Li and Longxiang You

Sensors 2024, 24(10), 3222; https://doi.org/10.3390/s24103222 (registering DOI) - 18 May 2024

Abstract

Object detection is one of the core technologies for autonomous driving. Current road object detection mainly relies on visible light, which is prone to missed detections and false alarms in rainy, night-time, and foggy scenes. Multispectral object detection based on the fusion of [...] Read more.

Object detection is one of the core technologies for autonomous driving. Current road object detection mainly relies on visible light, which is prone to missed detections and false alarms in rainy, night-time, and foggy scenes. Multispectral object detection based on the fusion of RGB and infrared images can effectively address the challenges of complex and changing road scenes, improving the detection performance of current algorithms in complex scenarios. However, previous multispectral detection algorithms suffer from issues such as poor fusion of dual-mode information, poor detection performance for multi-scale objects, and inadequate utilization of semantic information. To address these challenges and enhance the detection performance in complex road scenes, this paper proposes a novel multispectral object detection algorithm called MRD-YOLO. In MRD-YOLO, we utilize interaction-based feature extraction to effectively fuse information and introduce the BIC-Fusion module with attention guidance to fuse different modal information. We also incorporate the SAConv module to improve the model’s detection performance for multi-scale objects and utilize the AIFI structure to enhance the utilization of semantic information. Finally, we conduct experiments on two major public datasets, FLIR_Aligned and M³FD. The experimental results demonstrate that compared to other algorithms, the proposed algorithm achieves superior detection performance in complex road scenes. Full article

(This article belongs to the Section Remote Sensors)

40 pages, 970 KiB

Open AccessReview

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

by Marco Bolpagni, Susanna Pardini, Marco Dianti and Silvia Gabrielli

Sensors 2024, 24(10), 3221; https://doi.org/10.3390/s24103221 (registering DOI) - 18 May 2024

Abstract

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed [...] Read more.

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios. Full article

(This article belongs to the Section Wearables)

21 pages, 4116 KiB

Open AccessArticle

Design and Modeling of a Terahertz Transceiver for Intra- and Inter-Chip Communications in Wireless Network-on-Chip Architectures

by Biswash Paudel, Xue Jun Li and Boon-Chong Seet

Sensors 2024, 24(10), 3220; https://doi.org/10.3390/s24103220 (registering DOI) - 18 May 2024

Viewed by 55

Abstract

This paper addresses the increasing demand for computing power and the challenges associated with adding more core units to a computer processor. It explores the utilization of System-on-Chip (SoC) technology, which integrates Terahertz (THz) wave communication capabilities for intra- and inter-chip communication, using [...] Read more.

This paper addresses the increasing demand for computing power and the challenges associated with adding more core units to a computer processor. It explores the utilization of System-on-Chip (SoC) technology, which integrates Terahertz (THz) wave communication capabilities for intra- and inter-chip communication, using the concept of Wireless Network-on-Chips (WNoCs). Various types of network topologies are discussed, along with the disadvantages of wired networks. We explore the idea of applying wireless connections among cores and across the chip. Additionally, we describe the WNoC architecture, the flip-chip package, and the THz antenna. Electromagnetic fields are analyzed using a full-wave simulation software, Ansys High Frequency Structure Simulator (HFSS). The simulation is conducted with dipole and zigzag antennas communicating within the chip at resonant frequencies of 446 GHz and 462.5 GHz, with transmission coefficients of around −28 dB and −33 to −41 dB, respectively. Transmission coefficient characterization, path loss analysis, a study of electric field distribution, and a basic link budget for transmission are provided. Furthermore, the feasibility of calculated transmission power is validated in cases of high insertion loss, ensuring that the achieved energy expenditure is less than 1 pJ/bit. Finally, employing a similar setup, we study intra-chip communication using the same antennas. Simulation results indicate that the zigzag antenna exhibits a higher electric field magnitude compared with the dipole antenna across the simulated chip structure. We conclude that transmission occurs through reflection from the ground plane of a printed circuit board (PCB), as evidenced by the electric field distribution. Full article

(This article belongs to the Special Issue Integrated Sensing and Communication)

18 pages, 8167 KiB

Open AccessArticle

Designing a Novel Hybrid Technique Based on Enhanced Performance Wideband Millimeter-Wave Antenna for Short-Range Communication

by Tanvir Islam, Dildar Hussain, Fahad N. Alsunaydih, Fahd Alsaleem and Khaled Alhassoon

Sensors 2024, 24(10), 3219; https://doi.org/10.3390/s24103219 (registering DOI) - 18 May 2024

Viewed by 57

Abstract

This paper presents the design of a performance-improved 4-port multiple-input–multiple-output (MIMO) antenna proposed for millimeter-wave applications, especially for short-range communication systems. The antenna exhibits compact size, simplified geometry, and low profile along with wide bandwidth, high gain, low coupling, and a low Envelope [...] Read more.

This paper presents the design of a performance-improved 4-port multiple-input–multiple-output (MIMO) antenna proposed for millimeter-wave applications, especially for short-range communication systems. The antenna exhibits compact size, simplified geometry, and low profile along with wide bandwidth, high gain, low coupling, and a low Envelope Correlation Coefficient (ECC). Initially, a single-element antenna was designed by the integration of rectangular and circular patch antennas with slots. The antenna is superimposed on a Roger RT/Duroid 6002 with total dimensions of 17 × 12 × 1.52 mm³. Afterward, a MIMO configuration is formed along with a novel decoupling structure comprising a parasitic patch and a Defected Ground Structure (DGS). The parasitic patch is made up of strip lines with a rectangular box in the center, which is filled with circular rings. On the other side, the DGS is made by a combination of etched slots, resulting in separate ground areas behind each MIMO element. The proposed structure not only reduces coupling from −17.25 to −44 dB but also improves gain from 9.25 to 11.9 dBi while improving the bandwidth from 26.5–30.5 GHz to 25.5–30.5 GHz. Moreover, the MIMO antenna offers good performance while offering strong MIMO performance parameters, including ECC, diversity gain (DG), channel capacity loss (CCL), and mean effective gain (MEG). Furthermore, a state-of-the-art comparison is provided that results in the overperforming results of the proposed antenna system as compared to already published work. The antenna prototype is also fabricated and tested to verify software-generated results obtained from the electromagnetic (EM) tool HFSS. Full article

(This article belongs to the Special Issue Antenna Design and Sensors for Internet of Things - 2nd Edition)

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16 pages, 488 KiB

Open AccessArticle

IDAC: Federated Learning-Based Intrusion Detection Using Autonomously Extracted Anomalies in IoT

by Takahiro Ohtani, Ryo Yamamoto and Satoshi Ohzahata

Sensors 2024, 24(10), 3218; https://doi.org/10.3390/s24103218 (registering DOI) - 18 May 2024

Abstract

The recent rapid growth in Internet of Things (IoT) technologies is enriching our daily lives but significant information security risks in IoT fields have become apparent. In fact, there have been large-scale botnet attacks that exploit undiscovered vulnerabilities, known as zero-day attacks. Several [...] Read more.

The recent rapid growth in Internet of Things (IoT) technologies is enriching our daily lives but significant information security risks in IoT fields have become apparent. In fact, there have been large-scale botnet attacks that exploit undiscovered vulnerabilities, known as zero-day attacks. Several intrusion detection methods based on network traffic monitoring have been proposed to address this issue. These methods employ federated learning to share learned attack information among multiple IoT networks, aiming to improve collective detection capabilities against attacks including zero-day attacks. Although their ability to detect zero-day attacks with high precision has been confirmed, challenges such as autonomous labeling of attacks from traffic information and attack information sharing between different device types still remain. To resolve the issues, this paper proposes IDAC, a novel intrusion detection method with autonomous attack candidate labeling and federated learning-based attack candidate sharing. The labeling of attack candidates in IDAC is executed using information autonomously extracted from traffic information, and the labeling can also be applied to zero-day attacks. The federated learning-based attack candidate sharing enables candidate aggregation from multiple networks, and it executes attack determination based on the aggregated similar candidates. Performance evaluations demonstrated that IDS with IDAC within networks based on attack candidates is feasible and achieved comparable detection performance against multiple attacks including zero-day attacks compared to the existing methods while suppressing false positives in the extraction of attack candidates. In addition, the sharing of autonomously extracted attack candidates from multiple networks improves both detection performance and the required time for attack detection. Full article

(This article belongs to the Section Sensor Networks)

17 pages, 6850 KiB

Open AccessArticle

Development of an NO₂ Gas Sensor Based on Laser-Induced Graphene Operating at Room Temperature

by Gizem Soydan, Ali Fuat Ergenc, Ahmet T. Alpas and Nuri Solak

Sensors 2024, 24(10), 3217; https://doi.org/10.3390/s24103217 (registering DOI) - 18 May 2024

Viewed by 129

Abstract

A novel, in situ, low-cost and facile method has been developed to fabricate flexible NO₂ sensors capable of operating at ambient temperature, addressing the urgent need for monitoring this toxic gas. This technique involves the synthesis of highly porous structures, as well [...] Read more.

A novel, in situ, low-cost and facile method has been developed to fabricate flexible NO₂ sensors capable of operating at ambient temperature, addressing the urgent need for monitoring this toxic gas. This technique involves the synthesis of highly porous structures, as well as the specific development of laser-induced graphene (LIG) and its heterostructures with SnO₂, all through laser scribing. The morphology, phases, and compositions of the sensors were analyzed using scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy and Raman spectroscopy. The effects of SnO₂ addition on structural and sensor properties were investigated. Gas-sensing measurements were conducted at room temperature with NO₂ concentrations ranging from 50 to 10 ppm. LIG and LIG/SnO₂ sensors exhibited distinct trends in response to NO₂, and the gas-sensing mechanism was elucidated. Overall, this study demonstrates the feasibility of utilizing LIG and LIG/SnO₂ heterostructures in gas-sensing applications at ambient temperatures, underscoring their broad potential across diverse fields. Full article

(This article belongs to the Special Issue Gas Sensors’ Microstructure, Fabrication, Performance and Application)

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23 pages, 1784 KiB

Open AccessArticle

ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method

by Shuai You, Shijun Lin, Yujian Feng, Jianhua Fan, Zhenzheng Yan, Shangdong Liu and Yimu Ji

Sensors 2024, 24(10), 3216; https://doi.org/10.3390/s24103216 (registering DOI) - 18 May 2024

Viewed by 87

Abstract

The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage’s foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage [...] Read more.

The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage’s foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. Full article

(This article belongs to the Special Issue Digital Imaging Processing, Sensing, and Object Recognition)

25 pages, 8038 KiB

Open AccessArticle

Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders

by Xanthi Bampoula, Nikolaos Nikolakis and Kosmas Alexopoulos

Sensors 2024, 24(10), 3215; https://doi.org/10.3390/s24103215 (registering DOI) - 18 May 2024

Viewed by 112

Abstract

The production of multivariate time-series data facilitates the continuous monitoring of production assets. The modelling approach of multivariate time series can reveal the ways in which parameters evolve as well as the influences amongst themselves. These data can be used in tandem with [...] Read more.

The production of multivariate time-series data facilitates the continuous monitoring of production assets. The modelling approach of multivariate time series can reveal the ways in which parameters evolve as well as the influences amongst themselves. These data can be used in tandem with artificial intelligence methods to create insight on the condition of production equipment, hence potentially increasing the sustainability of existing manufacturing and production systems, by optimizing resource utilization, waste, and production downtime. In this context, a predictive maintenance method is proposed based on the combination of LSTM-Autoencoders and a Transformer encoder in order to enable the forecasting of asset failures through spatial and temporal time series. These neural networks are implemented into a software prototype. The dataset used for training and testing the models is derived from a metal processing industry case study. Ultimately, the goal is to train a remaining useful life (RUL) estimation model. Full article

(This article belongs to the Special Issue AI-Assisted Condition Monitoring and Fault Diagnosis)

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13 pages, 2941 KiB

Open AccessPerspective

Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges

by Aisling O’Leary, Timothy Lahey, Juniper Lovato, Bryn Loftness, Antranig Douglas, Joseph Skelton, Jenna G. Cohen, William E. Copeland, Ryan S. McGinnis and Ellen W. McGinnis

Sensors 2024, 24(10), 3214; https://doi.org/10.3390/s24103214 (registering DOI) - 18 May 2024

Viewed by 137

Abstract

In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports [...] Read more.

In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports given by caregivers, who tend to unintentionally under-report, and in some cases over-report, child symptomology. Digital phenotype screening tools (DPSTs), currently being developed in research settings, may help overcome reporting bias by providing objective measures of physiology and behavior to supplement child mental health screening. Prior to their implementation in pediatric practice, however, the ethical dimensions of DPSTs should be explored. Herein, we consider some promises and challenges of DPSTs under three broad categories: accuracy and bias, privacy, and accessibility and implementation. We find that DPSTs have demonstrated accuracy, may eliminate concerns regarding under- and over-reporting, and may be more accessible than gold-standard surveys. However, we also find that if DPSTs are not responsibly developed and deployed, they may be biased, raise privacy concerns, and be cost-prohibitive. To counteract these potential shortcomings, we identify ways to support the responsible and ethical development of DPSTs for clinical practice to improve mental health screening in children. Full article

(This article belongs to the Section Wearables)

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20 pages, 38903 KiB

Open AccessArticle

Three-Dimensional ERT Advanced Detection Method with Source-Position Electrode Excitation for Tunnel-Boring Machines

by Shuanfeng Zhao, Bo Liu, Bowen Ren, Li Wang, Zhijian Luo, Jian Yao and Yunrui Bai

Sensors 2024, 24(10), 3213; https://doi.org/10.3390/s24103213 (registering DOI) - 18 May 2024

Viewed by 93

Abstract

Tunnel-boring machines (TBMs) are widely used in urban underground tunnel construction due to their fast and efficient features. However, shield-tunnel construction faces increasingly complex geological environments and may encounter geological hazards such as faults, fracture zones, water surges, and collapses, which can cause [...] Read more.

Tunnel-boring machines (TBMs) are widely used in urban underground tunnel construction due to their fast and efficient features. However, shield-tunnel construction faces increasingly complex geological environments and may encounter geological hazards such as faults, fracture zones, water surges, and collapses, which can cause significant property damage and casualties. Existing geophysical methods are subject to many limitations in the shield-tunnel environment, where the detection space is extremely small, and a variety of advanced detection methods are unable to meet the required detection requirements. Therefore, it is crucial to accurately detect the geological conditions in front of the tunnel face in real time during the tunnel boring process of TBM tunnels. In this paper, a 3D-ERT advanced detection method using source-position electrode excitation is proposed. First, a source-position electrode array integrated into the TBM cutterhead is designed for the shield-tunnel construction environment, which provides data security for the inverse imaging of the anomalous bodies. Secondly, a 3D finite element tunnel model containing high- and low-resistance anomalous bodies is established, and the GREIT reconstruction algorithm is utilized to reconstruct 3D images of the anomalous body in front of the tunnel face. Finally, a physical simulation experiment platform is built, and the effectiveness of the method is verified by laboratory physical modeling experiments with two different anomalous bodies. The results show that the position and shape of the anomalous body in front of the tunnel face can be well reconstructed, and the method provides a new idea for the continuous detection of shield construction tunnels with boring. Full article

(This article belongs to the Section Electronic Sensors)

26 pages, 18344 KiB

Open AccessArticle

Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation

by Yongju Lee, Sungjun Jang, Han Byeol Bae, Taejae Jeon and Sangyoun Lee

Sensors 2024, 24(10), 3212; https://doi.org/10.3390/s24103212 (registering DOI) - 18 May 2024

Viewed by 92

Abstract

Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and [...] Read more.

Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise. The use of our pseudo-labeled training dataset can help to overcome the lack of diversity among the people in the dataset. Our integrated framework is constructed using complementary multitask learning methods to extract robust features for each task. Furthermore, introducing pseudo-labeling and multitask learning improves the face recognition performance by enabling the learning of pose-invariant features. Our method achieves state-of-the-art (SOTA) or near-SOTA performance on the AFLW2000-3D and BIWI datasets for facial landmark detection and head pose estimation, with competitive face verification performance on the IJB-C test dataset for face recognition. We demonstrate this through a novel testing methodology that categorizes cases as soft, medium, and hard based on the pose values of IJB-C. The proposed method achieves stable performance even when the dataset lacks diverse face identifications. Full article

(This article belongs to the Special Issue Deep Learning Based Face Recognition and Feature Extraction)

30 pages, 6907 KiB

Open AccessArticle

Research on the Multiple Small Target Detection Methodology in Remote Sensing

by Changman Zou, Wang-Su Jeon and Sang-Yong Rhee

Sensors 2024, 24(10), 3211; https://doi.org/10.3390/s24103211 (registering DOI) - 18 May 2024

Viewed by 104

Abstract

This study focuses on advancing the field of remote sensing image target detection, addressing challenges such as small target detection, complex background handling, and dense target distribution. We propose solutions based on enhancing the YOLOv7 algorithm. Firstly, we improve the multi-scale feature enhancement [...] Read more.

This study focuses on advancing the field of remote sensing image target detection, addressing challenges such as small target detection, complex background handling, and dense target distribution. We propose solutions based on enhancing the YOLOv7 algorithm. Firstly, we improve the multi-scale feature enhancement (MFE) method of YOLOv7, enhancing its adaptability and precision in detecting small targets and complex backgrounds. Secondly, we design a modified YOLOv7 global information DP-MLP module to effectively capture and integrate global information, thereby improving target detection accuracy and robustness, especially in handling large-scale variations and complex scenes. Lastly, we explore a semi-supervised learning model (SSLM) target detection algorithm incorporating unlabeled data, leveraging information from unlabeled data to enhance the model’s generalization ability and performance. Experimental results demonstrate that despite the outstanding performance of YOLOv7, the mean average precision (MAP) can still be improved by 1.9%. Specifically, under testing on the TGRS-HRRSD-Dataset, the MFE and DP-MLP models achieve MAP values of 93.4% and 93.1%, respectively. Across the NWPU VHR-10 dataset, the three models achieve MAP values of 93.1%, 92.1%, and 92.2%, respectively. Significant improvements are observed across various metrics compared to the original model. This study enhances the adaptability, accuracy, and generalization of remote sensing image object detection. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies and Intelligent Systems: Selected Papers From the 24th International Symposium on Advanced Intelligent Systems (ISIS)

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21 pages, 613 KiB

Open AccessArticle

Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features

by Mariam Bahameish, Tony Stockman and Jesús Requena Carrión

Sensors 2024, 24(10), 3210; https://doi.org/10.3390/s24103210 (registering DOI) - 18 May 2024

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Abstract

Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lead to inaccurate conclusions regarding the ML model performance. This study [...] Read more.

Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lead to inaccurate conclusions regarding the ML model performance. This study employed supervised learning algorithms to classify stress and relaxation states using HRV measures. To account for limitations associated with small datasets, robust strategies were implemented based on methodological recommendations for ML with a limited dataset, including data segmentation, feature selection, and model evaluation. Our findings highlight that the random forest model achieved the best performance in distinguishing stress from non-stress states. Notably, it showed higher performance in identifying stress from relaxation (F1-score: 86.3%) compared to neutral states (F1-score: 65.8%). Additionally, the model demonstrated generalizability when tested on independent secondary datasets, showcasing its ability to distinguish between stress and relaxation states. While our performance metrics might be lower than some previous studies, this likely reflects our focus on robust methodologies to enhance the generalizability and interpretability of ML models, which are crucial for real-world applications with limited datasets. Full article

(This article belongs to the Special Issue Advanced-Sensors-Based Emotion Sensing and Recognition)

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11 pages, 3918 KiB

Open AccessArticle

Improvement of Qualitative Analyses of Aliphatic Alcohols Using Direct Catalytic Fuel Cell and Chemometric Analysis Format

by Mauro Tomassetti, Federico Marini, Riccardo Pezzilli, Mauro Castrucci, Corrado Di Natale and Luigi Campanella

Sensors 2024, 24(10), 3209; https://doi.org/10.3390/s24103209 (registering DOI) - 18 May 2024

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Abstract

Direct catalytic methanol fuel cells (DCMFCs) have been studied for several years for energy conversion. Less extensive is the investigation of their analytical properties. In this paper, we demonstrate that the behavior of both the discharge and charger curves of DCMFCs depends on [...] Read more.

Direct catalytic methanol fuel cells (DCMFCs) have been studied for several years for energy conversion. Less extensive is the investigation of their analytical properties. In this paper, we demonstrate that the behavior of both the discharge and charger curves of DCMFCs depends on the chemical composition of the solution injected in the fuel cell. Their discharge and charge curves, analyzed using a chemometric data fusion method named ComDim, enable the identification of various types of aliphatic alcohols diluted in water. The results also show that the identification of alcohols can be obtained from the first portion of the discharge and charge curves. To this end, the curves have been described by a set of features related to the slope and intercept of the initial portion of the curves. The ComDim analysis of this set of features shows that the identification of alcohols can be obtained in a time that is about thirty times shorter than the time taken to achieve steady-state voltage. Full article

(This article belongs to the Section Chemical Sensors)

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21 pages, 1647 KiB

Open AccessArticle

Artificial Intelligence Approach for Classifying Images of Upper-Atmospheric Transient Luminous Events

by Axi Aguilera and Vidya Manian

Sensors 2024, 24(10), 3208; https://doi.org/10.3390/s24103208 (registering DOI) - 18 May 2024

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Abstract

Transient Luminous Events (TLEs) are short-lived, upper-atmospheric optical phenomena associated with thunderstorms. Their rapid and random occurrence makes manual classification laborious and time-consuming. This study presents an effective approach to automating the classification of TLEs using state-of-the-art Convolutional Neural Networks (CNNs) and a [...] Read more.

Transient Luminous Events (TLEs) are short-lived, upper-atmospheric optical phenomena associated with thunderstorms. Their rapid and random occurrence makes manual classification laborious and time-consuming. This study presents an effective approach to automating the classification of TLEs using state-of-the-art Convolutional Neural Networks (CNNs) and a Vision Transformer (ViT). The ViT architecture and four different CNN architectures, namely, ResNet50, ResNet18, GoogLeNet, and SqueezeNet, are employed and their performance is evaluated based on their accuracy and execution time. The models are trained on a dataset that was augmented using rotation, translation, and flipping techniques to increase its size and diversity. Additionally, the images are preprocessed using bilateral filtering to enhance their quality. The results show high classification accuracy across all models, with ResNet50 achieving the highest accuracy. However, a trade-off is observed between accuracy and execution time, which should be considered based on the specific requirements of the task. This study demonstrates the feasibility and effectiveness of using transfer learning and pre-trained CNNs for the automated classification of TLEs. Full article

(This article belongs to the Special Issue Applications of Video Processing and Computer Vision Sensor II)

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13 pages, 3931 KiB

Open AccessArticle

A Visual–Inertial Pressure Fusion-Based Underwater Simultaneous Localization and Mapping System

by Zhufei Lu, Xing Xu, Yihao Luo, Lianghui Ding, Chao Zhou and Jiarong Wang

Sensors 2024, 24(10), 3207; https://doi.org/10.3390/s24103207 (registering DOI) - 18 May 2024

Viewed by 128

Abstract

Detecting objects, particularly naval mines, on the seafloor is a complex task. In naval mine countermeasures (MCM) operations, sidescan or synthetic aperture sonars have been used to search large areas. However, a single sensor cannot meet the requirements of high-precision autonomous navigation. Based [...] Read more.

Detecting objects, particularly naval mines, on the seafloor is a complex task. In naval mine countermeasures (MCM) operations, sidescan or synthetic aperture sonars have been used to search large areas. However, a single sensor cannot meet the requirements of high-precision autonomous navigation. Based on the ORB-SLAM3-VI framework, we propose ORB-SLAM3-VIP, which integrates a depth sensor, an IMU sensor and an optical sensor. This method integrates the measurements of depth sensors and an IMU sensor into the visual SLAM algorithm through tight coupling, and establishes a multi-sensor fusion SLAM model. Depth constraints are introduced into the process of initialization, scale fine-tuning, tracking and mapping to constrain the position of the sensor in the z-axis and improve the accuracy of pose estimation and map scale estimate. The test on seven sets of underwater multi-sensor sequence data in the AQUALOC dataset shows that, compared with ORB-SLAM3-VI, the ORB-SLAM3-VIP system proposed in this paper reduces the scale error in all sequences by up to 41.2%, and reduces the trajectory error by up to 41.2%. The square root has also been reduced by up to 41.6%. Full article

(This article belongs to the Special Issue Development and Implementation of the Underwater Robot Enhanced by AI Methods)

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12 pages, 1779 KiB

Open AccessArticle

Efficacy of a Single-Bout of Auditory Feedback Training on Gait Performance and Kinematics in Healthy Young Adults

by Yosuke Tomita, Yoshihiro Sekiguchi and Nancy E. Mayo

Sensors 2024, 24(10), 3206; https://doi.org/10.3390/s24103206 (registering DOI) - 18 May 2024

Viewed by 122

Abstract

This study investigated the immediate effects of auditory feedback training on gait performance and kinematics in 19 healthy young adults, focusing on bilateral changes, despite unilateral training. Baseline and post-training kinematic measurements, as well as the feedback training were performed on a treadmill [...] Read more.

This study investigated the immediate effects of auditory feedback training on gait performance and kinematics in 19 healthy young adults, focusing on bilateral changes, despite unilateral training. Baseline and post-training kinematic measurements, as well as the feedback training were performed on a treadmill with a constant velocity. Significant improvements were seen in step length (trained: 590.7 mm to 611.1 mm, 95%CI [7.609, 24.373]; untrained: 591.1 mm to 628.7 mm, 95%CI [10.698, 30.835]), toe clearance (trained: 13.9 mm to 16.5 mm, 95%CI [1.284, 3.503]; untrained: 11.8 mm to 13.7 mm, 95%CI [1.763, 3.612]), ankle dorsiflexion angle at terminal stance (trained: 8.3 deg to 10.5 deg, 95%CI [1.092, 3.319]; untrained: 9.2 deg to 12.0 deg, 95%CI [1.676, 3.573]), hip flexion angular velocity, (trained: −126.5 deg/s to −131.0 deg/s, 95%CI [−9.054, −2.623]; untrained: −130.2 deg/s to −135.3 deg/s, 95%CI [−10.536, −1.675]), ankle angular velocity at terminal stance (trained: −344.7 deg/s to −359.1 deg/s, 95%CI [−47.540, −14.924]; untrained: −340.3 deg/s to −376.9 deg/s, 95%CI [−37.280, −13.166s]), and gastrocnemius EMG activity (trained: 0.60 to 0.66, 95%CI [0.014, 0.258]; untrained: 0.55 to 0.65, 95%CI [0.049, 0.214]). These findings demonstrate the efficacy of auditory feedback training in enhancing key gait parameters, highlighting the bilateral benefits from unilateral training. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring)

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22 pages, 2812 KiB

Open AccessArticle

Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings

by Michele Zanoletti, Pasquale Bufano, Francesco Bossi, Francesco Di Rienzo, Carlotta Marinai, Gianluca Rho, Carlo Vallati, Nicola Carbonaro, Alberto Greco, Marco Laurino and Alessandro Tognetti

Sensors 2024, 24(10), 3205; https://doi.org/10.3390/s24103205 - 17 May 2024

Viewed by 145

Abstract

Assessing mobility in daily life can provide significant insights into several clinical conditions, such as Chronic Obstructive Pulmonary Disease (COPD). In this paper, we present a comprehensive analysis of wearable devices’ performance in gait speed estimation and explore optimal device combinations for everyday [...] Read more.

Assessing mobility in daily life can provide significant insights into several clinical conditions, such as Chronic Obstructive Pulmonary Disease (COPD). In this paper, we present a comprehensive analysis of wearable devices’ performance in gait speed estimation and explore optimal device combinations for everyday use. Using data collected from smartphones, smartwatches, and smart shoes, we evaluated the individual capabilities of each device and explored their synergistic effects when combined, thereby accommodating the preferences and possibilities of individuals for wearing different types of devices. Our study involved 20 healthy subjects performing a modified Six-Minute Walking Test (6MWT) under various conditions. The results revealed only little performance differences among devices, with the combination of smartwatches and smart shoes exhibiting superior estimation accuracy. Particularly, smartwatches captured additional health-related information and demonstrated enhanced accuracy when paired with other devices. Surprisingly, wearing all devices concurrently did not yield optimal results, suggesting a potential redundancy in feature extraction. Feature importance analysis highlighted key variables contributing to gait speed estimation, providing valuable insights for model refinement. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing—2nd Edition)

10 pages, 1234 KiB

Open AccessArticle

A Förster Resonance Energy Transfer (FRET)-Based Immune Assay for the Detection of Microcystin-LR in Drinking Water

by Alessandro Capo, Angela Pennacchio, Concetta Montagnese, Antonis Hadjiantonis, Panayiota Demosthenous, Alessandro Giusti, Maria Staiano, Sabato D’Auria and Antonio Varriale

Sensors 2024, 24(10), 3204; https://doi.org/10.3390/s24103204 - 17 May 2024

Viewed by 129

Abstract

Cyanobacteria bloom is the term used to describe an abnormal and rapid growth of cyanobacteria in aquatic ecosystems such as lakes, rivers, and oceans as a consequence of anthropic factors, ecosystem degradation, or climate change. Cyanobacteria belonging to the genera Microcystis, Anabaena [...] Read more.

Cyanobacteria bloom is the term used to describe an abnormal and rapid growth of cyanobacteria in aquatic ecosystems such as lakes, rivers, and oceans as a consequence of anthropic factors, ecosystem degradation, or climate change. Cyanobacteria belonging to the genera Microcystis, Anabaena, Planktothrix, and Nostoc produce and release toxins called microcystins (MCs) into the water. MCs can have severe effects on human and animal health following their ingestion and inhalation. The MC structure is composed of a constant region (composed of five amino acid residues) and a variable region (composed of two amino acid residues). When the MC variable region is composed of arginine and leucine, it is named MC-LR. The most-common methods used to detect the presence of MC-LR in water are chromatographic-based methods (HPLC, LC/MS, GC/MS) and immunological-based methods (ELISA). In this work, we developed a new competitive Förster resonance energy transfer (FRET) assay to detect the presence of traces of MC-LR in water. Monoclonal antibody anti-MC-LR and MC-LR conjugated with bovine serum albumin (BSA) were labeled with the near-infrared fluorophores CF568 and CF647, respectively. Steady-state fluorescence measurements were performed to investigate the energy transfer process between anti-MC-LR 568 and MC-LR BSA 647 upon their interaction. Since the presence of unlabeled MC-LR competes with the labeled one, a lower efficiency of FRET process can be observed in the presence of an increasing amount of unlabeled MC-LR. The limit of detection (LoD) of the FRET assay is found to be 0.245 nM (0.245 µg/L). This value is lower than the provisional limit established by the World Health Organization (WHO) for quantifying the presence of MC-LR in drinking water. Full article

(This article belongs to the Special Issue Design and Application of Sensors Based on Nanomaterials in Clinical, Food and Environmental Analysis)

17 pages, 1418 KiB

Open AccessArticle

Location-Aware Range-Error Correction for Improved UWB Localization

by Sander Coene, Chenglong Li, Sebastian Kram, Emmeric Tanghe, Wout Joseph and David Plets

Sensors 2024, 24(10), 3203; https://doi.org/10.3390/s24103203 - 17 May 2024

Viewed by 139

Abstract

In this paper, we present a novel localization scheme, location-aware ranging correction (LARC), to correct ranging estimates from ultra wideband (UWB) signals. Existing solutions to calculate ranging corrections rely solely on channel information features (e.g., signal energy, maximum amplitude, estimated range). We propose [...] Read more.

In this paper, we present a novel localization scheme, location-aware ranging correction (LARC), to correct ranging estimates from ultra wideband (UWB) signals. Existing solutions to calculate ranging corrections rely solely on channel information features (e.g., signal energy, maximum amplitude, estimated range). We propose to incorporate a preliminary location estimate into a localization chain, such that location-based features can be calculated as inputs to a range-error prediction model. This way, we can add information to range-only measurements without relying on additional hardware such as an inertial measurement unit (IMU). This improves performance and reduces overfitting behavior. We demonstrate our LARC method using an open-access measurement dataset with distances up to 20 m, using a simple regression model that can run purely on the CPU in real-time. The inclusion of the proposed features for range-error mitigation decreases the ranging error 90th percentile (P90) by 58% to 15 cm (compared to the uncorrected range error), for an unseen trajectory. The 2D localization P90 error is improved by 21% to 18 cm. We show the robustness of our approach by comparing results to a changed environment, where metallic objects have been moved around the room. In this modified environment, we obtain a 56% better P90 ranging performance of 16 cm. The 2D localization P90 error improves as much as for the unchanged environment, by 17% to 18 cm, showing the robustness of our method. This method evolved from the first-ranking solution of the 2021 and 2022 International Conference on Indoor Position and Indoor Navigation (IPIN) Competition. Full article

(This article belongs to the Special Issue Enhancing Indoor LBS with Emerging Sensor Technologies)

28 pages, 4388 KiB

Open AccessArticle

Regional Pulmonary Ventilation Assessment Method and System Based on Impedance Sensing Information from the Pentapulmonary Lobes

by Yapeng Zhang, Chengxin Song, Wei He, Qian Zhang, Pengcheng Zhao and Jingang Wang

Sensors 2024, 24(10), 3202; https://doi.org/10.3390/s24103202 - 17 May 2024

Viewed by 140

Abstract

Regional lung ventilation assessment is a critical tool for the early detection of lung diseases and postoperative evaluation. Biosensor-based impedance measurements, known for their non-invasive nature, among other benefits, have garnered significant attention compared to traditional detection methods that utilize pressure sensors. However, [...] Read more.

Regional lung ventilation assessment is a critical tool for the early detection of lung diseases and postoperative evaluation. Biosensor-based impedance measurements, known for their non-invasive nature, among other benefits, have garnered significant attention compared to traditional detection methods that utilize pressure sensors. However, solely utilizing overall thoracic impedance fails to accurately capture changes in regional lung air volume. This study introduces an assessment method for lung ventilation that utilizes impedance data from the five lobes, develops a nonlinear model correlating regional impedance with lung air volume, and formulates an approach to identify regional ventilation obstructions based on impedance variations in affected areas. The electrode configuration for the five lung lobes was established through numerical simulations, revealing a power–function nonlinear relationship between regional impedance and air volume changes. An analysis of 389 pulmonary function tests refined the equations for calculating pulmonary function parameters, taking into account individual differences. Validation tests on 30 cases indicated maximum relative errors of 0.82% for FVC and 0.98% for FEV1, all within the 95% confidence intervals. The index for assessing regional ventilation impairment was corroborated by CT scans in 50 critical care cases, with 10 validation trials showing agreement with CT lesion localization results. Full article

(This article belongs to the Special Issue Advanced Flexible/Stretchable Electronics: Materials, Technologies and (Bio)-Sensing Application)

14 pages, 13914 KiB

Open AccessArticle

Detecting of Barely Visible Impact Damage on Carbon Fiber Reinforced Polymer Using Diffusion Ultrasonic Improved by Time-Frequency Domain Disturbance Sensitive Zone

by Yuqi Ma, Fangyuan Li, Jianbo Wu, Zhaoting Liu, Hui Xia and Zhaoyuan Xu

Sensors 2024, 24(10), 3201; https://doi.org/10.3390/s24103201 - 17 May 2024

Viewed by 138

Abstract

Based on the decorrelation calculation of diffusion ultrasound in time-frequency domain, this paper discusses the repeatability and potential significance of Disturbance Sensitive Zone (DSZ) in time-frequency domain. The experimental study of Barely Visible Impact Damage (BVID) on Carbon Fiber Reinforced Polymer (CFRP) is [...] Read more.

Based on the decorrelation calculation of diffusion ultrasound in time-frequency domain, this paper discusses the repeatability and potential significance of Disturbance Sensitive Zone (DSZ) in time-frequency domain. The experimental study of Barely Visible Impact Damage (BVID) on Carbon Fiber Reinforced Polymer (CFRP) is carried out. The decorrelation coefficients of time, frequency, and time-frequency domains and DSZ are calculated and compared. It has been observed that the sensitivity of the scattered wave disturbance caused by impact damage is non-uniformly distributed in both the time and frequency domains. This is evident from the non-uniform distribution of the decorrelation coefficient in time-domain and frequency-domain decorrelation calculations. Further, the decorrelation calculation in the time-frequency domain can show the distribution of the sensitivity of the scattered wave disturbance in the time domain and frequency domain. The decorrelation coefficients in time, frequency, and time-frequency domains increase monotonically with the number of impacts. In addition, in the time-frequency domain decorrelation calculation results, stable and repetitive DSZ are observed, which means that the specific frequency component of the scattered wave is extremely sensitive to the damage evolution of the impact region at a specific time. Finally, the DSZ obtained from the first 15 impacts is used to improve the decorrelation calculation in the 16-th to 20-th impact. The results show that the increment rate of the improved decorrelation coefficient is 10.22%. This study reveals that the diffusion ultrasonic decorrelation calculation improved by DSZ makes it feasible to evaluate early-stage damage caused by BVID. Full article

(This article belongs to the Special Issue Sensors in Nondestructive Testing)

14 pages, 5826 KiB

Open AccessArticle

Direct Measurement of Dissolved Gas Using a Tapered Single-Mode Silica Fiber

by Panpan Sun, Mengpeng Hu, Licai Zhu, Hui Zhang, Jinguang Lv, Yu Liu, Jingqiu Liang and Qiang Wang

Sensors 2024, 24(10), 3200; https://doi.org/10.3390/s24103200 - 17 May 2024

Viewed by 193

Abstract

Dissolved gases in the aquatic environment are critical to understanding the population of aquatic organisms and the ocean. Currently, laser absorption techniques based on membrane separation technology have made great strides in dissolved gas detection. However, the prolonged water–gas separation time of permeable [...] Read more.

Dissolved gases in the aquatic environment are critical to understanding the population of aquatic organisms and the ocean. Currently, laser absorption techniques based on membrane separation technology have made great strides in dissolved gas detection. However, the prolonged water–gas separation time of permeable membranes remains a key obstacle to the efficiency of dissolved gas analysis. To mitigate these limitations, we demonstrated direct measurement of dissolved gas using the evanescent-wave absorption spectroscopy of a tapered silica micro-fiber. It enhanced the analysis efficiency of dissolved gases without water–gas separation or sample preparation. The feasibility of this sensor for direct measurement of dissolved gases was verified by taking the detection of dissolved ammonia as an example. With a sensing length of 5 mm and a consumption of ~50 µL, this sensor achieves a system response time of ~11 min and a minimum detection limit (MDL) of 0.015%. Possible strategies are discussed for further performance improvement in in-situ applications requiring fast and highly sensitive dissolved gas sensing. Full article

(This article belongs to the Special Issue Applications of Optical Sensing and Laser Spectroscopy in Gas Detection)

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20 pages, 2846 KiB

Open AccessReview

Scientometric Research and Critical Analysis of Gait and Balance in Older Adults

by Qian Mao, Wei Zheng, Menghan Shi and Fan Yang

Sensors 2024, 24(10), 3199; https://doi.org/10.3390/s24103199 - 17 May 2024

Viewed by 149

Abstract

Gait and balance have emerged as a critical area of research in health technology. Gait and balance studies have been affected by the researchers’ slow follow-up of research advances due to the absence of visual inspection of the study literature across decades. This [...] Read more.

Gait and balance have emerged as a critical area of research in health technology. Gait and balance studies have been affected by the researchers’ slow follow-up of research advances due to the absence of visual inspection of the study literature across decades. This study uses advanced search methods to analyse the literature on gait and balance in older adults from 1993 to 2022 in the Web of Science (WoS) database to gain a better understanding of the current status and trends in the field for the first time. The study analysed 4484 academic publications including journal articles and conference proceedings on gait and balance in older adults. Bibliometric analysis methods were applied to examine the publication year, number of publications, discipline distribution, journal distribution, research institutions, application fields, test methods, analysis theories, and influencing factors in the field of gait and balance. The results indicate that the publication of relevant research documents has been steadily increasing from 1993 to 2022. The United States (US) exhibits the highest number of publications with 1742 articles. The keyword “elderly person” exhibits a strong citation burst strength of 18.04, indicating a significant focus on research related to the health of older adults. With a burst factor of 20.46, Harvard University has made impressive strides in the subject. The University of Pittsburgh displayed high research skills in the area of gait and balance with a burst factor of 7.7 and a publication count of 103. The research on gait and balance mainly focuses on physical performance evaluation approaches, and the primary study methods include experimental investigations, computational modelling, and observational studies. The field of gait and balance research is increasingly intertwined with computer science and artificial intelligence (AI), paving the way for intelligent monitoring of gait and balance in the elderly. Moving forward, the future of gait and balance research is anticipated to highlight the importance of multidisciplinary collaboration, intelligence-driven approaches, and advanced visualization techniques. Full article

(This article belongs to the Section Wearables)

17 pages, 1179 KiB

Open AccessArticle

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders

by Krzysztof Przybył

Sensors 2024, 24(10), 3198; https://doi.org/10.3390/s24103198 - 17 May 2024

Viewed by 147

Abstract

Recently, explainability in machine and deep learning has become an important area in the field of research as well as interest, both due to the increasing use of artificial intelligence (AI) methods and understanding of the decisions made by models. The explainability of [...] Read more.

Recently, explainability in machine and deep learning has become an important area in the field of research as well as interest, both due to the increasing use of artificial intelligence (AI) methods and understanding of the decisions made by models. The explainability of artificial intelligence (XAI) is due to the increasing consciousness in, among other things, data mining, error elimination, and learning performance by various AI algorithms. Moreover, XAI will allow the decisions made by models in problems to be more transparent as well as effective. In this study, models from the ‘glass box’ group of Decision Tree, among others, and the ‘black box’ group of Random Forest, among others, were proposed to understand the identification of selected types of currant powders. The learning process of these models was carried out to determine accuracy indicators such as accuracy, precision, recall, and F1-score. It was visualized using Local Interpretable Model Agnostic Explanations (LIMEs) to predict the effectiveness of identifying specific types of blackcurrant powders based on texture descriptors such as entropy, contrast, correlation, dissimilarity, and homogeneity. Bagging (Bagging_100), Decision Tree (DT0), and Random Forest (RF7_gini) proved to be the most effective models in the framework of currant powder interpretability. The measures of classifier performance in terms of accuracy, precision, recall, and F1-score for Bagging_100, respectively, reached values of approximately 0.979. In comparison, DT0 reached values of 0.968, 0.972, 0.968, and 0.969, and RF7_gini reached values of 0.963, 0.964, 0.963, and 0.963. These models achieved classifier performance measures of greater than 96%. In the future, XAI using agnostic models can be an additional important tool to help analyze data, including food products, even online. Full article

(This article belongs to the Section Intelligent Sensors)

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