Sensors

17 pages, 425 KiB

Open AccessEditor’s ChoiceArticle

Strengthening Privacy and Data Security in Biomedical Microelectromechanical Systems by IoT Communication Security and Protection in Smart Healthcare

by Francisco J. Jaime, Antonio Muñoz, Francisco Rodríguez-Gómez and Antonio Jerez-Calero

Sensors 2023, 23(21), 8944; https://doi.org/10.3390/s23218944 - 3 Nov 2023

Cited by 14 | Viewed by 2806

Abstract

Biomedical Microelectromechanical Systems (BioMEMS) serve as a crucial catalyst in enhancing IoT communication security and safeguarding smart healthcare systems. Situated at the nexus of advanced technology and healthcare, BioMEMS are instrumental in pioneering personalized diagnostics, monitoring, and therapeutic applications. Nonetheless, this integration brings [...] Read more.

Biomedical Microelectromechanical Systems (BioMEMS) serve as a crucial catalyst in enhancing IoT communication security and safeguarding smart healthcare systems. Situated at the nexus of advanced technology and healthcare, BioMEMS are instrumental in pioneering personalized diagnostics, monitoring, and therapeutic applications. Nonetheless, this integration brings forth a complex array of security and privacy challenges intrinsic to IoT communications within smart healthcare ecosystems, demanding comprehensive scrutiny. In this manuscript, we embark on an extensive analysis of the intricate security terrain associated with IoT communications in the realm of BioMEMS, addressing a spectrum of vulnerabilities that spans cyber threats, data manipulation, and interception of communications. The integration of real-world case studies serves to illuminate the direct repercussions of security breaches within smart healthcare systems, highlighting the imperative to safeguard both patient safety and the integrity of medical data. We delve into a suite of security solutions, encompassing rigorous authentication processes, data encryption, designs resistant to attacks, and continuous monitoring mechanisms, all tailored to fortify BioMEMS in the face of ever-evolving threats within smart healthcare environments. Furthermore, the paper underscores the vital role of ethical and regulatory considerations, emphasizing the need to uphold patient autonomy, ensure the confidentiality of data, and maintain equitable access to healthcare in the context of IoT communication security. Looking forward, we explore the impending landscape of BioMEMS security as it intertwines with emerging technologies such as AI-driven diagnostics, quantum computing, and genomic integration, anticipating potential challenges and strategizing for the future. In doing so, this paper highlights the paramount importance of adopting an integrated approach that seamlessly blends technological innovation, ethical foresight, and collaborative ingenuity, thereby steering BioMEMS towards a secure and resilient future within smart healthcare systems, in the ambit of IoT communication security and protection. Full article

(This article belongs to the Special Issue IoT Cybersecurity)

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19 pages, 6979 KiB

Open AccessEditor’s ChoiceReview

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

by Xavier Fernando and George Lăzăroiu

Sensors 2023, 23(18), 7792; https://doi.org/10.3390/s23187792 - 11 Sep 2023

Cited by 34 | Viewed by 3909

Abstract

The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the [...] Read more.

The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the use of a web-based Shiny app in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow design. AMSTAR, DistillerSR, Eppi-Reviewer, PICO Portal, Rayyan, and ROBIS were the review software systems harnessed for screening and quality assessment, while bibliometric mapping (dimensions) and layout algorithms (VOSviewer) configured data visualization and analysis. Cognitive radio is pivotal in the utilization of an adequate radio spectrum source, with spectrum sensing optimizing cognitive radio network operations, opportunistic spectrum access and sensing able to boost the efficiency of cognitive radio networks, and cooperative spectrum sharing together with simultaneous wireless information and power transfer able increase spectrum and energy efficiency in 6G wireless communication networks and across IoT devices for efficient data exchange. Full article

(This article belongs to the Special Issue Spectrum Sensing for Wireless Communication Systems)

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16 pages, 9326 KiB

Open AccessEditor’s ChoiceArticle

3D-Printed Graphene Nanoplatelets/Polymer Foams for Low/Medium-Pressure Sensors

by Marco Fortunato, Luca Pacitto, Nicola Pesce and Alessio Tamburrano

Sensors 2023, 23(16), 7054; https://doi.org/10.3390/s23167054 - 9 Aug 2023

Cited by 1 | Viewed by 1100

Abstract

The increasing interest in wearable devices for health monitoring, illness prevention, and human motion detection has driven research towards developing novel and cost-effective solutions for highly sensitive flexible sensors. The objective of this work is to develop innovative piezoresistive pressure sensors utilizing two [...] Read more.

The increasing interest in wearable devices for health monitoring, illness prevention, and human motion detection has driven research towards developing novel and cost-effective solutions for highly sensitive flexible sensors. The objective of this work is to develop innovative piezoresistive pressure sensors utilizing two types of 3D porous flexible open-cell foams: Grid and triply periodic minimal surface structures. These foams will be produced through a procedure involving the 3D printing of sacrificial templates, followed by infiltration with various low-viscosity polymers, leaching, and ultimately coating the pores with graphene nanoplatelets (GNPs). Additive manufacturing enables precise control over the shape and dimensions of the structure by manipulating geometric parameters during the design phase. This control extends to the piezoresistive response of the sensors, which is achieved by infiltrating the foams with varying concentrations of a colloidal suspension of GNPs. To examine the morphology of the produced materials, field emission scanning electron microscopy (FE-SEM) is employed, while mechanical and piezoresistive behavior are investigated through quasi-static uniaxial compression tests. The results obtained indicate that the optimized grid-based structure sensors, manufactured using the commercial polymer Solaris, exhibit the highest sensitivity compared to other tested samples. These sensors demonstrate a maximum sensitivity of 0.088 kPa⁻¹ for pressures below 10 kPa, increasing to 0.24 kPa⁻¹ for pressures of 80 kPa. Furthermore, the developed sensors are successfully applied to measure heartbeats both before and after aerobic activity, showcasing their excellent sensitivity within the typical pressure range exerted by the heartbeat, which typically falls between 10 and 20 kPa. Full article

(This article belongs to the Special Issue Graphene-Based Strain and Pressure Sensors)

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30 pages, 10953 KiB

Open AccessEditor’s ChoiceReview

A Review on Acoustic Emission Testing for Structural Health Monitoring of Polymer-Based Composites

by Noor Ghadarah and David Ayre

Sensors 2023, 23(15), 6945; https://doi.org/10.3390/s23156945 - 4 Aug 2023

Cited by 13 | Viewed by 3717

Abstract

Acoustic emission (AE) has received increased interest as a structural health monitoring (SHM) technique for various materials, including laminated polymer composites. Piezoelectric sensors, including PZT (piezoelectric ceramic) and PVDF (piezoelectric polymer), can monitor AE in materials. The thickness of the piezoelectric sensors (as [...] Read more.

Acoustic emission (AE) has received increased interest as a structural health monitoring (SHM) technique for various materials, including laminated polymer composites. Piezoelectric sensors, including PZT (piezoelectric ceramic) and PVDF (piezoelectric polymer), can monitor AE in materials. The thickness of the piezoelectric sensors (as low as 28 µm—PVDF) allows embedding the sensors within the laminated composite, creating a smart material. Incorporating piezoelectric sensors within composites has several benefits but presents numerous difficulties and challenges. This paper provides an overview of acoustic emission testing, concluding with a discussion on embedding piezoelectric AE sensors within fibre-polymer composites. Various aspects are covered, including the underlying AE principles in fibre-based composites, factors that influence the reliability and accuracy of AE measurements, methods to artificially induce acoustic emission, and the correlation between AE events and damage in polymer composites. Full article

(This article belongs to the Section Electronic Sensors)

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29 pages, 12357 KiB

Open AccessEditor’s ChoiceReview

A Review on Electrospun Nanofiber Composites for an Efficient Electrochemical Sensor Applications

by Ramkumar Vanaraj, Bharathi Arumugam, Gopiraman Mayakrishnan, Ick Soo Kim and Seong Cheol Kim

Sensors 2023, 23(15), 6705; https://doi.org/10.3390/s23156705 - 26 Jul 2023

Cited by 2 | Viewed by 1554

Abstract

The present review article discusses the elementary concepts of the sensor mechanism and various types of materials used for sensor applications. The electrospinning method is the most comfortable method to prepare the device-like structure by means of forming from the fiber structure. Though [...] Read more.

The present review article discusses the elementary concepts of the sensor mechanism and various types of materials used for sensor applications. The electrospinning method is the most comfortable method to prepare the device-like structure by means of forming from the fiber structure. Though there are various materials available for sensors, the important factor is to incorporate the functional group on the surface of the materials. The post-modification sanction enhances the efficiency of the sensor materials. This article also describes the various types of materials applied to chemical and biosensor applications. The chemical sensor parts include acetone, ethanol, ammonia, and CO₂, H₂O₂, and NO₂ molecules; meanwhile, the biosensor takes on glucose, uric acid, and cholesterol molecules. The above materials have to be sensed for a healthier lifestyle for humans and other living organisms. The prescribed review articles give a detailed report on the Electrospun materials for sensor applications. Full article

(This article belongs to the Special Issue Electrospun Composite Nanofibers: Sensing and Biosensing Applications)

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27 pages, 8928 KiB

Open AccessEditor’s ChoiceReview

Current Sensor Integration Issues with Wide-Bandgap Power Converters

by Ali Parsa Sirat and Babak Parkhideh

Sensors 2023, 23(14), 6481; https://doi.org/10.3390/s23146481 - 18 Jul 2023

Cited by 10 | Viewed by 3115

Abstract

Precise current sensing is essential for several power electronics’ protection, control, and reliability mechanisms. Even so, WBG power converters will likely struggle to develop a single current-sensing scheme to measure various types of currents due to the limited space and size of these [...] Read more.

Precise current sensing is essential for several power electronics’ protection, control, and reliability mechanisms. Even so, WBG power converters will likely struggle to develop a single current-sensing scheme to measure various types of currents due to the limited space and size of these devices, the required high sensing speed, and the high electromagnetic interference (EMI) emissions they cause. Analysis of existing current sensors was conducted in such terms with the objective of understanding the challenges associated with their integration into WBG power converters. Since each of these requirements has different design tradeoffs, it is challenging to consider one specific method of current sensing to be perfect for all situations; thus, the possibility of developing novel methods to improve the performance of these single-scheme current sensors is further explored. Full article

(This article belongs to the Special Issue Wide Bandgap Power Integrated Circuits and Sensors)

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34 pages, 4264 KiB

Open AccessEditor’s ChoiceReview

Advances in MIMO Antenna Design for 5G: A Comprehensive Review

by Tej Raj, Ranjan Mishra, Pradeep Kumar and Ankush Kapoor

Sensors 2023, 23(14), 6329; https://doi.org/10.3390/s23146329 - 12 Jul 2023

Cited by 21 | Viewed by 9205

Abstract

Multiple-input multiple-output (MIMO) technology has emerged as a highly promising solution for wireless communication, offering an opportunity to overcome the limitations of traffic capacity in high-speed broadband wireless network access. By utilizing multiple antennas at both the transmitting and receiving ends, the MIMO [...] Read more.

Multiple-input multiple-output (MIMO) technology has emerged as a highly promising solution for wireless communication, offering an opportunity to overcome the limitations of traffic capacity in high-speed broadband wireless network access. By utilizing multiple antennas at both the transmitting and receiving ends, the MIMO system enhances the efficiency and performance of wireless communication systems. This manuscript specifies a comprehensive review of MIMO antenna design approaches for fifth generation (5G) and beyond. With an introductory glimpse of cellular generation and the frequency spectrum for 5G, profound key enabling technologies for 5G mobile communication are presented. A detailed analysis of MIMO performance parameters in terms of envelope correlation coefficient (ECC), total active reflection coefficient (TARC), mean effective gain (MEG), and isolation is presented along with the advantages of MIMO technology over conventional SISO systems. MIMO is characterized and the performance is compared based on wideband/ultra-wideband, multiband/reconfigurable, circular polarized wideband/circular polarized ultra-wideband/circular polarized multiband, and reconfigurable categories. The design approaches of MIMO antennas for various 5G bands are discussed. It is subsequently enriched with the detailed studies of wideband (WB)/ultra-wideband (UWB), multiband, and circular polarized MIMO antennas with different design techniques. A good MIMO antenna system should be well decoupled among different ports to enhance its performance, and hence isolation among different ports is a crucial factor in designing high-performance MIMO antennas. A summary of design approaches with improved isolation is presented. The manuscript summarizes the various MIMO antenna design aspects for NR FR-1 (new radio frequency range) and NR FR-2, which will benefit researchers in the field of 5G and forthcoming cellular generations. Full article

(This article belongs to the Section Physical Sensors)

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28 pages, 11710 KiB

Open AccessEditor’s ChoiceArticle

A Wireless Sensor Network for Residential Building Energy and Indoor Environmental Quality Monitoring: Design, Instrumentation, Data Analysis and Feedback

by Mathieu Bourdeau, Julien Waeytens, Nedia Aouani, Philippe Basset and Elyes Nefzaoui

Sensors 2023, 23(12), 5580; https://doi.org/10.3390/s23125580 - 14 Jun 2023

Cited by 6 | Viewed by 2192

Abstract

This article outlines the implementation and use of a large wireless instrumentation solution to collect data over a long time period of a few years for three collective residential buildings. The sensor network consists of a variety of 179 sensors deployed in building [...] Read more.

This article outlines the implementation and use of a large wireless instrumentation solution to collect data over a long time period of a few years for three collective residential buildings. The sensor network consists of a variety of 179 sensors deployed in building common areas and in apartments to monitor energy consumption, indoor environmental quality, and local meteorological conditions. The collected data are used and analyzed to assess the building performance in terms of energy consumption and indoor environmental quality following major renovation operations on the buildings. Observations from the collected data show energy consumption of the renovated buildings in agreement with expected energy savings calculated by an engineering office, many different occupancy patterns mainly related to the professional situation of the households, and seasonal variation in window opening rates. The monitoring was also able to detect some deficiencies in the energy management. Indeed, the data reveal the absence of time-of-day-dependent heating load control and higher than expected indoor temperatures because of a lack of occupant awareness on energy savings, thermal comfort, and the new technologies installed during the renovation such as thermostatic valves on the heaters. Lastly, we also provide feedback on the performed sensor network from the experiment design and choice of measured quantities to data communication, through the sensors’ technological choices, implementation, calibration, and maintenance. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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38 pages, 24263 KiB

Open AccessEditor’s ChoiceReview

Recent Progress in Micro- and Nanotechnology-Enabled Sensors for Biomedical and Environmental Challenges

by Francisco J. Tovar-Lopez

Sensors 2023, 23(12), 5406; https://doi.org/10.3390/s23125406 - 7 Jun 2023

Cited by 23 | Viewed by 9210

Abstract

Micro- and nanotechnology-enabled sensors have made remarkable advancements in the fields of biomedicine and the environment, enabling the sensitive and selective detection and quantification of diverse analytes. In biomedicine, these sensors have facilitated disease diagnosis, drug discovery, and point-of-care devices. In environmental monitoring, [...] Read more.

Micro- and nanotechnology-enabled sensors have made remarkable advancements in the fields of biomedicine and the environment, enabling the sensitive and selective detection and quantification of diverse analytes. In biomedicine, these sensors have facilitated disease diagnosis, drug discovery, and point-of-care devices. In environmental monitoring, they have played a crucial role in assessing air, water, and soil quality, as well as ensured food safety. Despite notable progress, numerous challenges persist. This review article addresses recent developments in micro- and nanotechnology-enabled sensors for biomedical and environmental challenges, focusing on enhancing basic sensing techniques through micro/nanotechnology. Additionally, it explores the applications of these sensors in addressing current challenges in both biomedical and environmental domains. The article concludes by emphasizing the need for further research to expand the detection capabilities of sensors/devices, enhance sensitivity and selectivity, integrate wireless communication and energy-harvesting technologies, and optimize sample preparation, material selection, and automated components for sensor design, fabrication, and characterization. Full article

(This article belongs to the Special Issue Advances in Micro- and Nano- Sensors/Devices for Environmental and Biomedical Applications)

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33 pages, 4823 KiB

Open AccessEditor’s ChoiceArticle

NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio

by Panagiotis T. Karfakis, Micael S. Couceiro and David Portugal

Sensors 2023, 23(11), 5354; https://doi.org/10.3390/s23115354 - 5 Jun 2023

Cited by 6 | Viewed by 3521

Abstract

Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited [...] Read more.

Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited availability in dense urban and rural environments. Light Detection and Ranging (LiDAR), inertial and visual methods are also prone to drift and can be susceptible to outliers due to environmental changes and illumination conditions. In this work, we propose a cellular Simultaneous Localization and Mapping (SLAM) framework based on 5G New Radio (NR) signals and inertial measurements for mobile robot localization with several gNodeB stations. The method outputs the pose of the robot along with a radio signal map based on the Received Signal Strength Indicator (RSSI) measurements for correction purposes. We then perform benchmarking against LiDAR-Inertial Odometry Smoothing and Mapping (LIO-SAM), a state-of-the-art LiDAR SLAM method, comparing performance via a simulator ground truth reference. Two experimental setups are presented and discussed using the sub-6 GHz and mmWave frequency bands for communication, while the transmission is based on down-link (DL) signals. Our results show that 5G positioning can be utilized for radio SLAM, providing increased robustness in outdoor environments and demonstrating its potential to assist in robot localization, as an additional absolute source of information when LiDAR methods fail and GNSS data is unreliable. Full article

(This article belongs to the Special Issue Sensor Based Perception for Field Robotics)

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15 pages, 4845 KiB

Open AccessEditor’s ChoiceArticle

Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection

by Shenglin Li and Hwan-Sik Yoon

Sensors 2023, 23(10), 4888; https://doi.org/10.3390/s23104888 - 19 May 2023

Cited by 6 | Viewed by 5743

Abstract

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to [...] Read more.

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors. Full article

(This article belongs to the Special Issue Advances in Intelligent Transportation Systems Based on Sensor Fusion)

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29 pages, 2565 KiB

Open AccessEditor’s ChoiceReview

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

by Luca Neri, Matt T. Oberdier, Kirsten C. J. van Abeelen, Luca Menghini, Ethan Tumarkin, Hemantkumar Tripathi, Sujai Jaipalli, Alessandro Orro, Nazareno Paolocci, Ilaria Gallelli, Massimo Dall’Olio, Amir Beker, Richard T. Carrick, Claudio Borghi and Henry R. Halperin

Sensors 2023, 23(10), 4805; https://doi.org/10.3390/s23104805 - 16 May 2023

Cited by 14 | Viewed by 5519

Abstract

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices [...] Read more.

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data. Full article

(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)

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35 pages, 3792 KiB

Open AccessEditor’s ChoiceReview

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

by Inyeong Bae and Jungpyo Hong

Sensors 2023, 23(10), 4643; https://doi.org/10.3390/s23104643 - 10 May 2023

Cited by 14 | Viewed by 9801

Abstract

With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including [...] Read more.

With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), have the potential to complete maritime tasks that are unachievable for manned vehicles, lower the risk of man power, raise the power required to carry out military missions, and reap huge economic benefits. The aim of this review is to identify past and current trends in UMV development and present insights into future UMV development. The review discusses the potential benefits of UMVs, including completing maritime tasks that are unachievable for manned vehicles, lowering the risk of human intervention, and increasing power for military missions and economic benefits. However, the development of UMVs has been relatively tardy compared to that of UVs used on the ground and in the air due to adverse environments for UMV operation. This review highlights the challenges in developing UMVs, particularly in adverse environments, and the need for continued advancements in communication and networking technologies, navigation and sound exploration technologies, and multivehicle mission planning technologies to improve UMV cooperation and intelligence. Furthermore, the review identifies the importance of incorporating AI and machine learning technologies in UMVs to enhance their autonomy and ability to perform complex tasks. Overall, this review provides insights into the current state and future directions for UMV development. Full article

(This article belongs to the Special Issue Intelligent Sound Measurement Sensor and System 2022)

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26 pages, 1275 KiB

Open AccessEditor’s ChoiceReview

Oxygen Sensor-Based Respirometry and the Landscape of Microbial Testing Methods as Applicable to Food and Beverage Matrices

by Dmitri B. Papkovsky and Joseph P. Kerry

Sensors 2023, 23(9), 4519; https://doi.org/10.3390/s23094519 - 6 May 2023

Cited by 8 | Viewed by 2573

Abstract

The current status of microbiological testing methods for the determination of viable bacteria in complex sample matrices, such as food samples, is the focus of this review. Established methods for the enumeration of microorganisms, particularly, the ‘gold standard’ agar plating method for the [...] Read more.

The current status of microbiological testing methods for the determination of viable bacteria in complex sample matrices, such as food samples, is the focus of this review. Established methods for the enumeration of microorganisms, particularly, the ‘gold standard’ agar plating method for the determination of total aerobic viable counts (TVC), bioluminescent detection of total ATP, selective molecular methods (immunoassays, DNA/RNA amplification, sequencing) and instrumental methods (flow cytometry, Raman spectroscopy, mass spectrometry, calorimetry), are analyzed and compared with emerging oxygen sensor-based respirometry techniques. The basic principles of optical O₂ sensing and respirometry and the primary materials, detection modes and assay formats employed are described. The existing platforms for bacterial cell respirometry are then described, and examples of particular assays are provided, including the use of rapid TVC tests of food samples and swabs, the toxicological screening and profiling of cells and antimicrobial sterility testing. Overall, O₂ sensor-based respirometry and TVC assays have high application potential in the food industry and related areas. They detect viable bacteria via their growth and respiration; the assay is fast (time to result is 2–8 h and dependent on TVC load), operates with complex samples (crude homogenates of food samples) in a simple mix-and-measure format, has low set-up and instrumentation costs and is inexpensive and portable. Full article

(This article belongs to the Special Issue Optical Sensing Methods for Microorganism Identification)

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22 pages, 4697 KiB

Open AccessEditor’s ChoiceReview

Advances in Electrochemical Biosensor Technologies for the Detection of Nucleic Acid Breast Cancer Biomarkers

by Ana-Maria Chiorcea-Paquim

Sensors 2023, 23(8), 4128; https://doi.org/10.3390/s23084128 - 20 Apr 2023

Cited by 11 | Viewed by 3457

Abstract

Breast cancer is the second leading cause of cancer deaths in women worldwide; therefore, there is an increased need for the discovery, development, optimization, and quantification of diagnostic biomarkers that can improve the disease diagnosis, prognosis, and therapeutic outcome. Circulating cell-free nucleic acids [...] Read more.

Breast cancer is the second leading cause of cancer deaths in women worldwide; therefore, there is an increased need for the discovery, development, optimization, and quantification of diagnostic biomarkers that can improve the disease diagnosis, prognosis, and therapeutic outcome. Circulating cell-free nucleic acids biomarkers such as microRNAs (miRNAs) and breast cancer susceptibility gene 1 (BRCA1) allow the characterization of the genetic features and screening breast cancer patients. Electrochemical biosensors offer excellent platforms for the detection of breast cancer biomarkers due to their high sensitivity and selectivity, low cost, use of small analyte volumes, and easy miniaturization. In this context, this article provides an exhaustive review concerning the electrochemical methods of characterization and quantification of different miRNAs and BRCA1 breast cancer biomarkers using electrochemical DNA biosensors based on the detection of hybridization events between a DNA or peptide nucleic acid probe and the target nucleic acid sequence. The fabrication approaches, the biosensors architectures, the signal amplification strategies, the detection techniques, and the key performance parameters, such as the linearity range and the limit of detection, were discussed. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Electrochemical Sensors)

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36 pages, 17376 KiB

Open AccessEditor’s ChoiceArticle

Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study

by Nastassia Vysotskaya, Christoph Will, Lorenzo Servadei, Noah Maul, Christian Mandl, Merlin Nau, Jens Harnisch and Andreas Maier

Sensors 2023, 23(8), 4111; https://doi.org/10.3390/s23084111 - 19 Apr 2023

Cited by 7 | Viewed by 4746

Abstract

Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is incapable [...] Read more.

Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used—together with the calibration parameters of age, gender, height, and weight—as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach’s predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of

9.2 \pm 8.3

mmHg (mean error ± standard deviation) and a diastolic error of

7.7 \pm 5.7

mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)

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18 pages, 5780 KiB

Open AccessEditor’s ChoiceArticle

Crowdsourced Indoor Positioning with Scalable WiFi Augmentation

by Yinhuan Dong, Guoxiong He, Tughrul Arslan, Yunjie Yang and Yingda Ma

Sensors 2023, 23(8), 4095; https://doi.org/10.3390/s23084095 - 19 Apr 2023

Cited by 7 | Viewed by 1664

Abstract

In recent years, crowdsourcing approaches have been proposed to record the WiFi signals annotated with the location of the reference points (RPs) extracted from the trajectories of common users to reduce the burden of constructing a fingerprint (FP) database for indoor positioning. However, [...] Read more.

In recent years, crowdsourcing approaches have been proposed to record the WiFi signals annotated with the location of the reference points (RPs) extracted from the trajectories of common users to reduce the burden of constructing a fingerprint (FP) database for indoor positioning. However, crowdsourced data is usually sensitive to crowd density. The positioning accuracy degrades in some areas due to a lack of FPs or visitors. To improve the positioning performance, this paper proposes a scalable WiFi FP augmentation method with two major modules: virtual reference point generation (VRPG) and spatial WiFi signal modeling (SWSM). A globally self-adaptive (GS) and a locally self-adaptive (LS) approach are proposed in VRPG to determine the potential unsurveyed RPs. A multivariate Gaussian process regression (MGPR) model is designed to estimate the joint distribution of all WiFi signals and predicts the signals on unsurveyed RPs to generate more FPs. Evaluations are conducted on an open-source crowdsourced WiFi FP dataset based on a multi-floor building. The results show that combining GS and MGPR can improve the positioning accuracy by 5% to 20% from the benchmark, but with halved computation complexity compared to the conventional augmentation approach. Moreover, combining LS and MGPR can sharply reduce 90% of the computation complexity against the conventional approach while still providing moderate improvement in positioning accuracy from the benchmark. Full article

(This article belongs to the Special Issue Multi-Sensor Positioning for Navigation in Smart Cities)

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16 pages, 2865 KiB

Open AccessEditor’s ChoiceArticle

A New Design to Rayleigh Wave EMAT Based on Spatial Pulse Compression

by Chuanliu Jiang, Zhichao Li, Zeyang Zhang and Shujuan Wang

Sensors 2023, 23(8), 3943; https://doi.org/10.3390/s23083943 - 13 Apr 2023

Cited by 9 | Viewed by 2163

Abstract

The main disadvantage of the electromagnetic acoustic transducer (EMAT) is low energy-conversion efficiency and low signal-to-noise ratio (SNR). This problem can be improved by pulse compression technology in the time domain. In this paper, a new coil structure with unequal spacing was proposed [...] Read more.

The main disadvantage of the electromagnetic acoustic transducer (EMAT) is low energy-conversion efficiency and low signal-to-noise ratio (SNR). This problem can be improved by pulse compression technology in the time domain. In this paper, a new coil structure with unequal spacing was proposed for a Rayleigh wave EMAT (RW-EMAT) to replace the conventional meander line coil with equal spacing, which allows the signal to be compressed in the spatial domain. Linear and nonlinear wavelength modulations were analyzed to design the unequal spacing coil. Based on this, the performance of the new coil structure was analyzed by the autocorrelation function. Finite element simulation and experiments proved the feasibility of the spatial pulse compression coil. The experimental results show that the received signal amplitude is increased by 2.3~2.6 times, the signal with a width of 20 μs could be compressed into a δ-like pulse of less than 0.25 μs and the SNR is increased by 7.1–10.1 dB. These indicate that the proposed new RW-EMAT can effectively enhance the strength, time resolution and SNR of the received signal. Full article

(This article belongs to the Special Issue Development, Investigation and Application of Acoustic Sensors: Part II)

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28 pages, 1981 KiB

Open AccessEditor’s ChoiceReview

Biomimetic Approaches for Human Arm Motion Generation: Literature Review and Future Directions

by Urvish Trivedi, Dimitrios Menychtas, Redwan Alqasemi and Rajiv Dubey

Sensors 2023, 23(8), 3912; https://doi.org/10.3390/s23083912 - 12 Apr 2023

Cited by 4 | Viewed by 3803

Abstract

In recent years, numerous studies have been conducted to analyze how humans subconsciously optimize various performance criteria while performing a particular task, which has led to the development of robots that are capable of performing tasks with a similar level of efficiency as [...] Read more.

In recent years, numerous studies have been conducted to analyze how humans subconsciously optimize various performance criteria while performing a particular task, which has led to the development of robots that are capable of performing tasks with a similar level of efficiency as humans. The complexity of the human body has led researchers to create a framework for robot motion planning to recreate those motions in robotic systems using various redundancy resolution methods. This study conducts a thorough analysis of the relevant literature to provide a detailed exploration of the different redundancy resolution methodologies used in motion generation for mimicking human motion. The studies are investigated and categorized according to the study methodology and various redundancy resolution methods. An examination of the literature revealed a strong trend toward formulating intrinsic strategies that govern human movement through machine learning and artificial intelligence. Subsequently, the paper critically evaluates the existing approaches and highlights their limitations. It also identifies the potential research areas that hold promise for future investigations. Full article

(This article belongs to the Section Sensors and Robotics)

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17 pages, 4248 KiB

Open AccessEditor’s ChoiceArticle

An Automotive LiDAR Performance Test Method in Dynamic Driving Conditions

by Jewoo Park, Jihyuk Cho, Seungjoo Lee, Seokhwan Bak and Yonghwi Kim

Sensors 2023, 23(8), 3892; https://doi.org/10.3390/s23083892 - 11 Apr 2023

Cited by 8 | Viewed by 4736

Abstract

The Light Detection and Ranging (LiDAR) sensor has become essential to achieving a high level of autonomous driving functions, as well as a standard Advanced Driver Assistance System (ADAS). LiDAR capabilities and signal repeatabilities under extreme weather conditions are of utmost concern in [...] Read more.

The Light Detection and Ranging (LiDAR) sensor has become essential to achieving a high level of autonomous driving functions, as well as a standard Advanced Driver Assistance System (ADAS). LiDAR capabilities and signal repeatabilities under extreme weather conditions are of utmost concern in terms of the redundancy design of automotive sensor systems. In this paper, we demonstrate a performance test method for automotive LiDAR sensors that can be utilized in dynamic test scenarios. In order to measure the performance of a LiDAR sensor in a dynamic test scenario, we propose a spatio-temporal point segmentation algorithm that can separate a LiDAR signal of moving reference targets (car, square target, etc.), using an unsupervised clustering method. An automotive-graded LiDAR sensor is evaluated in four harsh environmental simulations, based on time-series environmental data of real road fleets in the USA, and four vehicle-level tests with dynamic test cases are conducted. Our test results showed that the performance of LiDAR sensors may be degraded, due to several environmental factors, such as sunlight, reflectivity of an object, cover contamination, and so on. Full article

(This article belongs to the Section Vehicular Sensing)

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23 pages, 3078 KiB

Open AccessEditor’s ChoiceReview

Review of Zinc Oxide Piezoelectric Nanogenerators: Piezoelectric Properties, Composite Structures and Power Output

by Neelesh Bhadwal, Ridha Ben Mrad and Kamran Behdinan

Sensors 2023, 23(8), 3859; https://doi.org/10.3390/s23083859 - 10 Apr 2023

Cited by 23 | Viewed by 6315

Abstract

Lead-containing piezoelectric materials typically show the highest energy conversion efficiencies, but due to their toxicity they will be limited in future applications. In their bulk form, the piezoelectric properties of lead-free piezoelectric materials are significantly lower than lead-containing materials. However, the piezoelectric properties [...] Read more.

Lead-containing piezoelectric materials typically show the highest energy conversion efficiencies, but due to their toxicity they will be limited in future applications. In their bulk form, the piezoelectric properties of lead-free piezoelectric materials are significantly lower than lead-containing materials. However, the piezoelectric properties of lead-free piezoelectric materials at the nano scale can be significantly larger than the bulk scale. This review looks at the suitability of ZnO nanostructures as candidate lead-free piezoelectric materials for use in piezoelectric nanogenerators (PENGs) based on their piezoelectric properties. Of the papers reviewed, Neodymium-doped ZnO nanorods (NRs) have a comparable piezoelectric strain constant to bulk lead-based piezoelectric materials and hence are good candidates for PENGs. Piezoelectric energy harvesters typically have low power outputs and an improvement in their power density is needed. This review systematically reviews the different composite structures of ZnO PENGs to determine the effect of composite structure on power output. State-of-the-art techniques to increase the power output of PENGs are presented. Of the PENGs reviewed, the highest power output belonged to a vertically aligned ZnO nanowire (NWs) PENG (1-3 nanowire composite) with a power output of 45.87 μW/cm² under finger tapping. Future directions of research and challenges are discussed. Full article

(This article belongs to the Special Issue MEMS Sensors and Actuators 2022–2023)

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31 pages, 6976 KiB

Open AccessEditor’s ChoiceReview

Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning

by Chiranjivi Neupane, Maisa Pereira, Anand Koirala and Kerry B. Walsh

Sensors 2023, 23(8), 3868; https://doi.org/10.3390/s23083868 - 10 Apr 2023

Cited by 13 | Viewed by 5347

Abstract

Forward estimates of harvest load require information on fruit size as well as number. The task of sizing fruit and vegetables has been automated in the packhouse, progressing from mechanical methods to machine vision over the last three decades. This shift is now [...] Read more.

Forward estimates of harvest load require information on fruit size as well as number. The task of sizing fruit and vegetables has been automated in the packhouse, progressing from mechanical methods to machine vision over the last three decades. This shift is now occurring for size assessment of fruit on trees, i.e., in the orchard. This review focuses on: (i) allometric relationships between fruit weight and lineal dimensions; (ii) measurement of fruit lineal dimensions with traditional tools; (iii) measurement of fruit lineal dimensions with machine vision, with attention to the issues of depth measurement and recognition of occluded fruit; (iv) sampling strategies; and (v) forward prediction of fruit size (at harvest). Commercially available capability for in-orchard fruit sizing is summarized, and further developments of in-orchard fruit sizing by machine vision are anticipated. Full article

(This article belongs to the Section Smart Agriculture)

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25 pages, 4252 KiB

Open AccessEditor’s ChoiceReview

Towards an Evolved Immersive Experience: Exploring 5G- and Beyond-Enabled Ultra-Low-Latency Communications for Augmented and Virtual Reality

by Ananya Hazarika and Mehdi Rahmati

Sensors 2023, 23(7), 3682; https://doi.org/10.3390/s23073682 - 2 Apr 2023

Cited by 18 | Viewed by 8291

Abstract

Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of [...] Read more.

Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of low-latency connectivity, which is defined as the end-to-end delay between the action and the reaction, is very crucial to leverage these technologies for a high-quality immersive experience. This paper provides a comprehensive survey and detailed insight into various advantageous approaches from the hardware and software perspectives, as well as the integration of 5G technology, towards 5GB, in enabling a low-latency environment for AR and VR applications. The contribution of 5GB systems as an outcome of several cutting-edge technologies, such as massive multiple-input, multiple-output (mMIMO) and millimeter wave (mmWave), along with the utilization of artificial intelligence (AI) and machine learning (ML) techniques towards an ultra-low-latency communication system, is also discussed in this paper. The potential of using a visible-light communications (VLC)-guided beam through a learning algorithm for a futuristic, evolved immersive experience of augmented and virtual reality with the ultra-low-latency transmission of multi-sensory tracking information with an optimal scheduling policy is discussed in this paper. Full article

(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)

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37 pages, 5055 KiB

Open AccessEditor’s ChoiceReview

A Review of Skin-Wearable Sensors for Non-Invasive Health Monitoring Applications

by Pengsu Mao, Haoran Li and Zhibin Yu

Sensors 2023, 23(7), 3673; https://doi.org/10.3390/s23073673 - 31 Mar 2023

Cited by 17 | Viewed by 5841

Abstract

The early detection of fatal diseases is crucial for medical diagnostics and treatment, both of which benefit the individual and society. Portable devices, such as thermometers and blood pressure monitors, and large instruments, such as computed tomography (CT) and X-ray scanners, have already [...] Read more.

The early detection of fatal diseases is crucial for medical diagnostics and treatment, both of which benefit the individual and society. Portable devices, such as thermometers and blood pressure monitors, and large instruments, such as computed tomography (CT) and X-ray scanners, have already been implemented to collect health-related information. However, collecting health information using conventional medical equipment at home or in a hospital can be inefficient and can potentially affect the timeliness of treatment. Therefore, on-time vital signal collection via healthcare monitoring has received increasing attention. As the largest organ of the human body, skin delivers significant signals reflecting our health condition; thus, receiving vital signals directly from the skin offers the opportunity for accessible and versatile non-invasive monitoring. In particular, emerging flexible and stretchable electronics demonstrate the capability of skin-like devices for on-time and continuous long-term health monitoring. Compared to traditional electronic devices, this type of device has better mechanical properties, such as skin conformal attachment, and maintains compatible detectability. This review divides the health information that can be obtained from skin using the sensor aspect’s input energy forms into five categories: thermoelectrical signals, neural electrical signals, photoelectrical signals, electrochemical signals, and mechanical pressure signals. We then summarize current skin-wearable health monitoring devices and provide outlooks on future development. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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15 pages, 3868 KiB

Open AccessEditor’s ChoiceArticle

Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection

by Shenglin Li and Hwan-Sik Yoon

Sensors 2023, 23(7), 3661; https://doi.org/10.3390/s23073661 - 31 Mar 2023

Cited by 3 | Viewed by 4287

Abstract

Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors [...] Read more.

Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors such as cameras, radars, and LiDARs. Among these sensors, cameras can provide a cost-effective way to determine the number, location, type, and speed of the vehicles for better-informed decision-making at traffic intersections. In this research, a new approach for accurately determining vehicle locations near traffic intersections using a single camera is presented. For that purpose, a well-known object detection algorithm called YOLO is used to determine vehicle locations in video images captured by a traffic camera. YOLO draws a bounding box around each detected vehicle, and the vehicle location in the image coordinates is converted to the world coordinates using camera calibration data. During this process, a significant error between the center of a vehicle’s bounding box and the real center of the vehicle in the world coordinates is generated due to the angled view of the vehicles by a camera installed on a traffic light pole. As a means of mitigating this vehicle localization error, two different types of regression models are trained and applied to the centers of the bounding boxes of the camera-detected vehicles. The accuracy of the proposed approach is validated using both static camera images and live-streamed traffic video. Based on the improved vehicle localization, it is expected that more accurate traffic signal control can be made to improve the overall network-wide energy efficiency and traffic flow at traffic intersections. Full article

(This article belongs to the Special Issue Applications of Machine Learning in Automotive Engineering)

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12 pages, 2256 KiB

Open AccessEditor’s ChoiceArticle

Towards the Use of Individual Fluorescent Nanoparticles as Ratiometric Sensors: Spectral Robustness of Ultrabright Nanoporous Silica Nanoparticles

by Mahshid Iraniparast, Berney Peng and Igor Sokolov

Sensors 2023, 23(7), 3471; https://doi.org/10.3390/s23073471 - 26 Mar 2023

Cited by 2 | Viewed by 1634

Abstract

Here we address an important roadblock that prevents the use of bright fluorescent nanoparticles as individual ratiometric sensors: the possible variation of fluorescence spectra between individual nanoparticles. Ratiometric measurements using florescent dyes have shown their utility in measuring the spatial distribution of temperature, [...] Read more.

Here we address an important roadblock that prevents the use of bright fluorescent nanoparticles as individual ratiometric sensors: the possible variation of fluorescence spectra between individual nanoparticles. Ratiometric measurements using florescent dyes have shown their utility in measuring the spatial distribution of temperature, acidity, and concentration of various ions. However, the dyes have a serious limitation in their use as sensors; namely, their fluorescent spectra can change due to interactions with the surrounding dye. Encapsulation of the d, e in a porous material can solve this issue. Recently, we demonstrated the use of ultrabright nanoporous silica nanoparticles (UNSNP) to measure temperature and acidity. The particles have at least two kinds of encapsulated dyes. Ultrahigh brightness of the particles allows measuring of the signal of interest at the single particle level. However, it raises the problem of spectral variation between particles, which is impossible to control at the nanoscale. Here, we study spectral variations between the UNSNP which have two different encapsulated dyes: rhodamine R6G and RB. The dyes can be used to measure temperature. We synthesized these particles using three different ratios of the dyes. We measured the spectra of individual nanoparticles and compared them with simulations. We observed a rather small variation of fluorescence spectra between individual UNSNP, and the spectra were in very good agreement with the results of our simulations. Thus, one can conclude that individual UNSNP can be used as effective ratiometric sensors. Full article

(This article belongs to the Section Sensor Materials)

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23 pages, 18382 KiB

Open AccessEditor’s ChoiceArticle

A Concurrent Framework for Constrained Inverse Kinematics of Minimally Invasive Surgical Robots

by Jacinto Colan, Ana Davila, Khusniddin Fozilov and Yasuhisa Hasegawa

Sensors 2023, 23(6), 3328; https://doi.org/10.3390/s23063328 - 22 Mar 2023

Cited by 13 | Viewed by 2838

Abstract

Minimally invasive surgery has undergone significant advancements in recent years, transforming various surgical procedures by minimizing patient trauma, postoperative pain, and recovery time. However, the use of robotic systems in minimally invasive surgery introduces significant challenges related to the control of the robot’s [...] Read more.

Minimally invasive surgery has undergone significant advancements in recent years, transforming various surgical procedures by minimizing patient trauma, postoperative pain, and recovery time. However, the use of robotic systems in minimally invasive surgery introduces significant challenges related to the control of the robot’s motion and the accuracy of its movements. In particular, the inverse kinematics (IK) problem is critical for robot-assisted minimally invasive surgery (RMIS), where satisfying the remote center of motion (RCM) constraint is essential to prevent tissue damage at the incision point. Several IK strategies have been proposed for RMIS, including classical inverse Jacobian IK and optimization-based approaches. However, these methods have limitations and perform differently depending on the kinematic configuration. To address these challenges, we propose a novel concurrent IK framework that combines the strengths of both approaches and explicitly incorporates RCM constraints and joint limits into the optimization process. In this paper, we present the design and implementation of concurrent inverse kinematics solvers, as well as experimental validation in both simulation and real-world scenarios. Concurrent IK solvers outperform single-method solvers, achieving a 100% solve rate and reducing the IK solving time by up to 85% for an endoscope positioning task and 37% for a tool pose control task. In particular, the combination of an iterative inverse Jacobian method with a hierarchical quadratic programming method showed the highest average solve rate and lowest computation time in real-world experiments. Our results demonstrate that concurrent IK solving provides a novel and effective solution to the constrained IK problem in RMIS applications. Full article

(This article belongs to the Special Issue Sensor-Based Surgical and Medical Robotics: Challenges and Opportunities)

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14 pages, 8451 KiB

Open AccessEditor’s ChoiceArticle

An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study

by Ghena Hammour and Danilo P. Mandic

Sensors 2023, 23(6), 3319; https://doi.org/10.3390/s23063319 - 21 Mar 2023

Cited by 10 | Viewed by 11021

Abstract

Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 [...] Read more.

Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring. Full article

(This article belongs to the Section Wearables)

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15 pages, 9351 KiB

Open AccessEditor’s ChoiceArticle

A Comparative Study on the Effects of Spray Coating Methods and Substrates on Polyurethane/Carbon Nanofiber Sensors

by Mounika Chowdary Karlapudi, Mostafa Vahdani, Sheyda Mirjalali Bandari, Shuhua Peng and Shuying Wu

Sensors 2023, 23(6), 3245; https://doi.org/10.3390/s23063245 - 19 Mar 2023

Cited by 8 | Viewed by 2363

Abstract

Thermoplastic polyurethane (TPU) has been widely used as the elastic polymer substrate to be combined with conductive nanomaterials to develop stretchable strain sensors for a variety of applications such as health monitoring, smart robotics, and e-skins. However, little research has been reported on [...] Read more.

Thermoplastic polyurethane (TPU) has been widely used as the elastic polymer substrate to be combined with conductive nanomaterials to develop stretchable strain sensors for a variety of applications such as health monitoring, smart robotics, and e-skins. However, little research has been reported on the effects of deposition methods and the form of TPU on their sensing performance. This study intends to design and fabricate a durable, stretchable sensor based on composites of thermoplastic polyurethane and carbon nanofibers (CNFs) by systematically investigating the influences of TPU substrates (i.e., either electrospun nanofibers or solid thin film) and spray coating methods (i.e., either air-spray or electro-spray). It is found that the sensors with electro-sprayed CNFs conductive sensing layers generally show a higher sensitivity, while the influence of the substrate is not significant and there is no clear and consistent trend. The sensor composed of a TPU solid thin film with electro-sprayed CNFs exhibits an optimal performance with a high sensitivity (gauge factor ~28.2) in a strain range of 0–80%, a high stretchability of up to 184%, and excellent durability. The potential application of these sensors in detecting body motions has been demonstrated, including finger and wrist-joint movements, by using a wooden hand. Full article

(This article belongs to the Special Issue Use of Smart Wearable Sensors and AI Methods in Providing P4 Medicine)

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15 pages, 3960 KiB

Open AccessEditor’s ChoiceArticle

A Wearable Insole System to Measure Plantar Pressure and Shear for People with Diabetes

by Jinghua Tang, Dan L. Bader, David Moser, Daniel J. Parker, Saeed Forghany, Christopher J. Nester and Liudi Jiang

Sensors 2023, 23(6), 3126; https://doi.org/10.3390/s23063126 - 15 Mar 2023

Cited by 14 | Viewed by 5910

Abstract

Pressure coupled with shear stresses are the critical external factors for diabetic foot ulceration assessment and prevention. To date, a wearable system capable of measuring in-shoe multi-directional stresses for out-of-lab analysis has been elusive. The lack of an insole system capable of measuring [...] Read more.

Pressure coupled with shear stresses are the critical external factors for diabetic foot ulceration assessment and prevention. To date, a wearable system capable of measuring in-shoe multi-directional stresses for out-of-lab analysis has been elusive. The lack of an insole system capable of measuring plantar pressure and shear hinders the development of an effective foot ulcer prevention solution that could be potentially used in a daily living environment. This study reports the development of a first-of-its-kind sensorised insole system and its evaluation in laboratory settings and on human participants, indicating its potential as a wearable technology to be used in real-world applications. Laboratory evaluation revealed that the linearity error and accuracy error of the sensorised insole system were up to 3% and 5%, respectively. When evaluated on a healthy participant, change in footwear resulted in approximately 20%, 75% and 82% change in pressure, medial–lateral and anterior–posterior shear stress, respectively. When evaluated on diabetic participants, no notable difference in peak plantar pressure, as a result of wearing the sensorised insole, was measured. The preliminary results showed that the performance of the sensorised insole system is comparable to previously reported research devices. The system has adequate sensitivity to assist footwear assessment relevant to foot ulcer prevention and is safe to use for people with diabetes. The reported insole system presents the potential to help assess diabetic foot ulceration risk in a daily living environment underpinned by wearable pressure and shear sensing technologies. Full article

(This article belongs to the Special Issue Saving Limbs and Lives-Sensing and Monitoring Technologies for the Diabetic Foot)

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22 pages, 2419 KiB

Open AccessEditor’s ChoiceReview

An Extended AI-Experience: Industry 5.0 in Creative Product Innovation

by Amy Grech, Jörn Mehnen and Andrew Wodehouse

Sensors 2023, 23(6), 3009; https://doi.org/10.3390/s23063009 - 10 Mar 2023

Cited by 10 | Viewed by 3950

Abstract

Creativity plays a significant role in competitive product ideation. With the increasing emergence of Virtual Reality (VR) and Artificial Intelligence (AI) technologies, the link between such technologies and product ideation is explored in this research to assist and augment creative scenarios in the [...] Read more.

Creativity plays a significant role in competitive product ideation. With the increasing emergence of Virtual Reality (VR) and Artificial Intelligence (AI) technologies, the link between such technologies and product ideation is explored in this research to assist and augment creative scenarios in the engineering field. A bibliographic analysis is performed to review relevant fields and their relationships. This is followed by a review of current challenges in group ideation and state-of-the-art technologies with the aim of addressing them in this study. This knowledge is applied to the transformation of current ideation scenarios into a virtual environment using AI. The aim is to augment designers’ creative experiences, a core value of Industry 5.0 that focuses on human-centricity, social and ecological benefits. For the first time, this research reclaims brainstorming as a challenging and inspiring activity where participants are fully engaged through a combination of AI and VR technologies. This activity is enhanced through three key areas: facilitation, stimulation, and immersion. These areas are integrated through intelligent team moderation, enhanced communication techniques, and access to multi-sensory stimuli during the collaborative creative process, therefore providing a platform for future research into Industry 5.0 and smart product development. Full article

(This article belongs to the Special Issue Human-Centred Smart Manufacturing - Industry 5.0)

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15 pages, 32729 KiB

Open AccessEditor’s ChoiceArticle

Colorimetric and Fluorescent Sensing of Copper Ions in Water through o-Phenylenediamine-Derived Carbon Dots

by Roberto Pizzoferrato, Ramanand Bisauriya, Simonetta Antonaroli, Marcello Cabibbo and Artur J. Moro

Sensors 2023, 23(6), 3029; https://doi.org/10.3390/s23063029 - 10 Mar 2023

Cited by 11 | Viewed by 2181

Abstract

Fluorescent nitrogen and sulfur co-doped carbon dots (NSCDs) were synthesized using a simple one-step hydrothermal method starting from o-phenylenediamine (OPD) and ammonium sulfide. The prepared NSCDs presented a selective dual optical response to Cu(II) in water through the arising of an absorption band [...] Read more.

Fluorescent nitrogen and sulfur co-doped carbon dots (NSCDs) were synthesized using a simple one-step hydrothermal method starting from o-phenylenediamine (OPD) and ammonium sulfide. The prepared NSCDs presented a selective dual optical response to Cu(II) in water through the arising of an absorption band at 660 nm and simultaneous fluorescence enhancement at 564 nm. The first effect was attributed to formation of cuprammonium complexes through coordination with amino functional groups of NSCDs. Alternatively, fluorescence enhancement can be explained by the oxidation of residual OPD bound to NSCDs. Both absorbance and fluorescence showed a linear increase with an increase of Cu(II) concentration in the range 1–100 µM, with the lowest detection limit of 100 nM and 1 µM, respectively. NSCDs were successfully incorporated in a hydrogel agarose matrix for easier handling and application to sensing. The formation of cuprammonium complexes was strongly hampered in an agarose matrix while oxidation of OPD was still effective. As a result, color variations could be perceived both under white light and UV light for concentrations as low as 10 µM. Since these color changes were similarly perceived in tap and lake water samples, the present method could be a promising candidate for simple, cost-effective visual monitoring of copper onsite. Full article

(This article belongs to the Collection Optical Chemical Sensors: Design and Applications)

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18 pages, 3601 KiB

Open AccessEditor’s ChoiceArticle

Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction

by Bach-Tung Pham, Phuong Thi Le, Tzu-Chiang Tai, Yi-Chiung Hsu, Yung-Hui Li and Jia-Ching Wang

Sensors 2023, 23(6), 2993; https://doi.org/10.3390/s23062993 - 9 Mar 2023

Cited by 8 | Viewed by 5186

Abstract

An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on [...] Read more.

An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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29 pages, 947 KiB

Open AccessEditor’s ChoiceReview

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review

by Marianne Boyer, Laurent Bouyer, Jean-Sébastien Roy and Alexandre Campeau-Lecours

Sensors 2023, 23(6), 2927; https://doi.org/10.3390/s23062927 - 8 Mar 2023

Cited by 21 | Viewed by 9107

Abstract

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and [...] Read more.

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and artifacts, leading to potential data misinterpretation. Even assuming best practices, the acquired signal may still contain contaminants. The aim of this paper is to review methods employed to reduce the contamination of single channel EMG signals. Specifically, we focus on methods which enable a full reconstruction of the EMG signal without loss of information. This includes subtraction methods used in the time domain, denoising methods performed after the signal decomposition and hybrid approaches that combine multiple methods. Finally, this paper provides a discussion on the suitability of the individual methods based on the type of contaminant(s) present in the signal and the specific requirements of the application. Full article

(This article belongs to the Special Issue EMG Sensors and Signal Processing Technologies)

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24 pages, 783 KiB

Open AccessEditor’s ChoiceReview

Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions

by Mohammed Ayalew Belay, Sindre Stenen Blakseth, Adil Rasheed and Pierluigi Salvo Rossi

Sensors 2023, 23(5), 2844; https://doi.org/10.3390/s23052844 - 6 Mar 2023

Cited by 15 | Viewed by 14029

Abstract

The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, e.g., multi-sensor systems represent a critical enabling technology towards full autonomy in industrial scenarios. Sensors usually produce vast amounts of unlabeled data in the form of [...] Read more.

The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, e.g., multi-sensor systems represent a critical enabling technology towards full autonomy in industrial scenarios. Sensors usually produce vast amounts of unlabeled data in the form of multivariate time series that may capture normal conditions or anomalies. Multivariate Time Series Anomaly Detection (MTSAD), i.e., the ability to identify normal or irregular operative conditions of a system through the analysis of data from multiple sensors, is crucial in many fields. However, MTSAD is challenging due to the need for simultaneous analysis of temporal (intra-sensor) patterns and spatial (inter-sensor) dependencies. Unfortunately, labeling massive amounts of data is practically impossible in many real-world situations of interest (e.g., the reference ground truth may not be available or the amount of data may exceed labeling capabilities); therefore, robust unsupervised MTSAD is desirable. Recently, advanced techniques in machine learning and signal processing, including deep learning methods, have been developed for unsupervised MTSAD. In this article, we provide an extensive review of the current state of the art with a theoretical background about multivariate time-series anomaly detection. A detailed numerical evaluation of 13 promising algorithms on two publicly available multivariate time-series datasets is presented, with advantages and shortcomings highlighted. Full article

(This article belongs to the Special Issue Signal Processing and AI in Sensor Networks and IoT)

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26 pages, 4081 KiB

Open AccessEditor’s ChoiceArticle

AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring

by Nikos Mitro, Katerina Argyri, Lampros Pavlopoulos, Dimitrios Kosyvas, Lazaros Karagiannidis, Margarita Kostovasili, Fay Misichroni, Eleftherios Ouzounoglou and Angelos Amditis

Sensors 2023, 23(5), 2821; https://doi.org/10.3390/s23052821 - 4 Mar 2023

Cited by 8 | Viewed by 7102

Abstract

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring [...] Read more.

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers’ physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%. Full article

(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Measuring Human Vital Signs)

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31 pages, 7945 KiB

Open AccessEditor’s ChoiceReview

State-of-the-Art Review on Wearable Obstacle Detection Systems Developed for Assistive Technologies and Footwear

by Anna M. Joseph, Azadeh Kian and Rezaul Begg

Sensors 2023, 23(5), 2802; https://doi.org/10.3390/s23052802 - 3 Mar 2023

Cited by 4 | Viewed by 5887

Abstract

Walking independently is essential to maintaining our quality of life but safe locomotion depends on perceiving hazards in the everyday environment. To address this problem, there is an increasing focus on developing assistive technologies that can alert the user to the risk destabilizing [...] Read more.

Walking independently is essential to maintaining our quality of life but safe locomotion depends on perceiving hazards in the everyday environment. To address this problem, there is an increasing focus on developing assistive technologies that can alert the user to the risk destabilizing foot contact with either the ground or obstacles, leading to a fall. Shoe-mounted sensor systems designed to monitor foot-obstacle interaction are being employed to identify tripping risk and provide corrective feedback. Advances in smart wearable technologies, integrating motion sensors with machine learning algorithms, has led to developments in shoe-mounted obstacle detection. The focus of this review is gait-assisting wearable sensors and hazard detection for pedestrians. This literature represents a research front that is critically important in paving the way towards practical, low-cost, wearable devices that can make walking safer and reduce the increasing financial and human costs of fall injuries. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

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20 pages, 5942 KiB

Open AccessEditor’s ChoiceArticle

A New Generation of OPM for High Dynamic and Large Bandwidth MEG: The ⁴He OPMs—First Applications in Healthy Volunteers

by Tjerk P. Gutteling, Mathilde Bonnefond, Tommy Clausner, Sébastien Daligault, Rudy Romain, Sergey Mitryukovskiy, William Fourcault, Vincent Josselin, Matthieu Le Prado, Agustin Palacios-Laloy, Etienne Labyt, Julien Jung and Denis Schwartz

Sensors 2023, 23(5), 2801; https://doi.org/10.3390/s23052801 - 3 Mar 2023

Cited by 10 | Viewed by 4176

Abstract

MagnetoEncephaloGraphy (MEG) provides a measure of electrical activity in the brain at a millisecond time scale. From these signals, one can non-invasively derive the dynamics of brain activity. Conventional MEG systems (SQUID-MEG) use very low temperatures to achieve the necessary sensitivity. This leads [...] Read more.

MagnetoEncephaloGraphy (MEG) provides a measure of electrical activity in the brain at a millisecond time scale. From these signals, one can non-invasively derive the dynamics of brain activity. Conventional MEG systems (SQUID-MEG) use very low temperatures to achieve the necessary sensitivity. This leads to severe experimental and economical limitations. A new generation of MEG sensors is emerging: the optically pumped magnetometers (OPM). In OPM, an atomic gas enclosed in a glass cell is traversed by a laser beam whose modulation depends on the local magnetic field. MAG⁴Health is developing OPMs using Helium gas (⁴He-OPM). They operate at room temperature with a large dynamic range and a large frequency bandwidth and output natively a 3D vectorial measure of the magnetic field. In this study, five ⁴He-OPMs were compared to a classical SQUID-MEG system in a group of 18 volunteers to evaluate their experimental performances. Considering that the ⁴He-OPMs operate at real room temperature and can be placed directly on the head, our assumption was that ⁴He-OPMs would provide a reliable recording of physiological magnetic brain activity. Indeed, the results showed that the ⁴He-OPMs showed very similar results to the classical SQUID-MEG system by taking advantage of a shorter distance to the brain, despite having a lower sensitivity. Full article

(This article belongs to the Section Physical Sensors)

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20 pages, 8179 KiB

Open AccessEditor’s ChoiceArticle

An Interface ASIC Design of MEMS Gyroscope with Analog Closed Loop Driving

by Huan Zhang, Weiping Chen, Liang Yin and Qiang Fu

Sensors 2023, 23(5), 2615; https://doi.org/10.3390/s23052615 - 27 Feb 2023

Cited by 3 | Viewed by 4191

Abstract

This paper introduces a digital interface application-specific integrated circuit (ASIC) for a micro-electromechanical systems (MEMS) vibratory gyroscope. The driving circuit of the interface ASIC uses an automatic gain circuit (AGC) module instead of a phase-locked loop to realize a self-excited vibration, which gives [...] Read more.

This paper introduces a digital interface application-specific integrated circuit (ASIC) for a micro-electromechanical systems (MEMS) vibratory gyroscope. The driving circuit of the interface ASIC uses an automatic gain circuit (AGC) module instead of a phase-locked loop to realize a self-excited vibration, which gives the gyroscope system good robustness. In order to realize the co-simulation of the mechanically sensitive structure and interface circuit of the gyroscope, the equivalent electrical model analysis and modeling of the mechanically sensitive structure of the gyro are carried out by Verilog-A. According to the design scheme of the MEMS gyroscope interface circuit, a system-level simulation model including mechanically sensitive structure and measurement and control circuit is established by SIMULINK. A digital-to-analog converter (ADC) is designed for the digital processing and temperature compensation of the angular velocity in the MEMS gyroscope digital circuit system. Using the positive and negative diode temperature characteristics, the function of the on-chip temperature sensor is realized, and the temperature compensation and zero bias correction are carried out simultaneously. The MEMS interface ASIC is designed using a standard 0.18 μM CMOS BCD process. The experimental results show that the signal-to-noise ratio (SNR) of sigma-delta (ΣΔ) ADC is 111.56 dB. The nonlinearity of the MEMS gyroscope system is 0.03% over the full-scale range. Full article

(This article belongs to the Special Issue Advanced Sensors in MEMS)

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11 pages, 1479 KiB

Open AccessEditor’s ChoiceArticle

Generation of Mixed-OAM-Carrying Waves Using Huygens’ Metasurface for Mm-Wave Applications

by Hassan Naseri, Peyman PourMohammadi, Nouredddine Melouki, Fahad Ahmed, Amjad Iqbal and Tayeb A. Denidni

Sensors 2023, 23(5), 2590; https://doi.org/10.3390/s23052590 - 26 Feb 2023

Cited by 9 | Viewed by 1988

Abstract

Antennas that generate orbital angular momentum (OAM) have the potential to significantly enhance the channel capacity of upcoming wireless systems. This is because different OAM modes that are excited from a shared aperture are orthogonal, which means that each mode can carry a [...] Read more.

Antennas that generate orbital angular momentum (OAM) have the potential to significantly enhance the channel capacity of upcoming wireless systems. This is because different OAM modes that are excited from a shared aperture are orthogonal, which means that each mode can carry a distinct stream of data. As a result, it is possible to transmit multiple data streams at the same time and frequency using a single OAM antenna system. To achieve this, there is a need to develop antennas that can create several OAM modes. This study employs an ultrathin dual-polarized Huygens’ metasurface to design a transmit array (TA) that can generate mixed-OAM modes. Two concentrically-embedded TAs are used to excite the desired modes by achieving the required phase difference according to the coordinate position of each unit cell. The prototype of the TA, which operates at 28 GHz and has a size of 11 × 11 cm

^{2}

, generates mixed OAM modes of −1 and −2 using dual-band Huygens’ metasurfaces. To the best of the authors’ knowledge, this is the first time that such a low-profile and dual-polarized OAM carrying mixed vortex beams has been designed using TAs. The maximum gain of the structure is 16 dBi. Full article

(This article belongs to the Special Issue Recent Trends and Developments in Antennas)

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12 pages, 2540 KiB

Open AccessEditor’s ChoiceArticle

Potentiometric Chloride Ion Biosensor for Cystic Fibrosis Diagnosis and Management: Modeling and Design

by Annabella la Grasta, Martino De Carlo, Attilio Di Nisio, Francesco Dell’Olio and Vittorio M. N. Passaro

Sensors 2023, 23(5), 2491; https://doi.org/10.3390/s23052491 - 23 Feb 2023

Cited by 6 | Viewed by 2151

Abstract

The ion-sensitive field-effect transistor is a well-established electronic device typically used for pH sensing. The usability of the device for detecting other biomarkers in easily accessible biologic fluids, with dynamic range and resolution compliant with high-impact medical applications, is still an open research [...] Read more.

The ion-sensitive field-effect transistor is a well-established electronic device typically used for pH sensing. The usability of the device for detecting other biomarkers in easily accessible biologic fluids, with dynamic range and resolution compliant with high-impact medical applications, is still an open research topic. Here, we report on an ion-sensitive field-effect transistor that is able to detect the presence of chloride ions in sweat with a limit-of-detection of 0.004 mol/m³. The device is intended for supporting the diagnosis of cystic fibrosis, and it has been designed considering two adjacent domains, namely the semiconductor and the electrolyte containing the ions of interest, by using the finite element method, which models the experimental reality with great accuracy. According to the literature explaining the chemical reactions that take place between the gate oxide and the electrolytic solution, we have concluded that anions directly interact with the hydroxyl surface groups and replace protons previously adsorbed from the surface. The achieved results confirm that such a device can be used to replace the traditional sweat test in the diagnosis and management of cystic fibrosis. In fact, the reported technology is easy-to-use, cost-effective, and non-invasive, leading to earlier and more accurate diagnoses. Full article

(This article belongs to the Special Issue Novel Field-Effect Transistor Gas/Chem/Bio Sensing)

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46 pages, 10529 KiB

Open AccessEditor’s ChoiceReview

Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects

by Halid Abdulrahim Kadi and Kasim Terzić

Sensors 2023, 23(5), 2389; https://doi.org/10.3390/s23052389 - 21 Feb 2023

Cited by 2 | Viewed by 4352

Abstract

Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such [...] Read more.

Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state–action dynamics as significant obstacles to perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth shaping, knot tying/untying, dressing and bag manipulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms. Full article

(This article belongs to the Section Sensors and Robotics)

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25 pages, 2878 KiB

Open AccessEditor’s ChoiceReview

LoRa Technology in Flying Ad Hoc Networks: A Survey of Challenges and Open Issues

by William David Paredes, Hemani Kaushal, Iman Vakilinia and Zornitza Prodanoff

Sensors 2023, 23(5), 2403; https://doi.org/10.3390/s23052403 - 21 Feb 2023

Cited by 11 | Viewed by 3415

Abstract

The Internet of Things (IoT) and Flying Ad Hoc Networks (FANETs) have become hot topics among researchers because of the increased availability of Unmanned Aerial Vehicles (UAVs) and the electronic components required to control and connect them (e.g., microcontrollers, single board computers, and [...] Read more.

The Internet of Things (IoT) and Flying Ad Hoc Networks (FANETs) have become hot topics among researchers because of the increased availability of Unmanned Aerial Vehicles (UAVs) and the electronic components required to control and connect them (e.g., microcontrollers, single board computers, and radios). LoRa is a wireless technology, intended for the IoT, that requires low power and provides long-range communications, which can be useful for ground and aerial applications. This paper explores the role that LoRa plays in FANET design by presenting a technical overview of both, and by performing a systematic literature review based on a breakdown of the communications, mobility and energy topics involved in a FANET implementation. Furthermore, open issues in protocol design are discussed, as well as other challenges associated with the use of LoRa in the deployment of FANETs. Full article

(This article belongs to the Section Sensor Networks)

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20 pages, 8858 KiB

Open AccessEditor’s ChoiceArticle

Smart Cementitious Sensors with Nano-, Micro-, and Hybrid-Modified Reinforcement: Mechanical and Electrical Properties

by Athanasia K. Thomoglou, Maria G. Falara, Fani I. Gkountakou, Anaxagoras Elenas and Constantin E. Chalioris

Sensors 2023, 23(5), 2405; https://doi.org/10.3390/s23052405 - 21 Feb 2023

Cited by 18 | Viewed by 1877

Abstract

The current paper presents the results of an experimental study of carbon nano-, micro-, and hybrid-modified cementitious mortar to evaluate mechanical performance, energy absorption, electrical conductivity, and piezoresistive sensibility. Three amounts of single-walled carbon nanotubes (SWCNTs), namely 0.05 wt.%, 0.1 wt.%, 0.2 wt.%, [...] Read more.

The current paper presents the results of an experimental study of carbon nano-, micro-, and hybrid-modified cementitious mortar to evaluate mechanical performance, energy absorption, electrical conductivity, and piezoresistive sensibility. Three amounts of single-walled carbon nanotubes (SWCNTs), namely 0.05 wt.%, 0.1 wt.%, 0.2 wt.%, and 0.3 wt.% of the cement mass, were used to prepare nano-modified cement-based specimens. In the microscale modification, 0.05 wt.%, 0.5 wt.%, 1.0 wt.% carbon fibers (CFs) were incorporated in the matrix. The hybrid-modified cementitious specimens were enhanced by adding optimized amounts of CFs and SWCNTs. The smartness of modified mortars, indicated by their piezoresistive behavior, was investigated by measuring the changes in electrical resistivity. The effective parameters that enhance the composites’ mechanical and electrical performance are the different concentrations of reinforcement and the synergistic effect between the types of reinforcement used in the hybrid structure. Results reveal that all the strengthening types improved flexural strength, toughness, and electrical conductivity by about an order of magnitude compared to the reference specimens. Specifically, the hybrid-modified mortars presented a marginal reduction of 1.5% in compressive strength and an increase in flexural strength of 21%. The hybrid-modified mortar absorbed the most energy, 1509%, 921%, and 544% more than the reference mortar, nano-modified mortar, and micro-modified mortar, respectively. The change rate of impedance, capacitance, and resistivity in piezoresistive 28-day hybrid mortars improved the tree ratios by 289%, 324%, and 576%, respectively, for nano-modified mortars and by 64%, 93%, and 234%, respectively, for micro-modified mortars. Full article

(This article belongs to the Section Intelligent Sensors)

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32 pages, 5673 KiB

Open AccessEditor’s ChoiceReview

Advances in Humidity Nanosensors and Their Application: Review

by Chin-An Ku and Chen-Kuei Chung

Sensors 2023, 23(4), 2328; https://doi.org/10.3390/s23042328 - 20 Feb 2023

Cited by 27 | Viewed by 4840

Abstract

As the technology revolution and industrialization have flourished in the last few decades, the development of humidity nanosensors has become more important for the detection and control of humidity in the industry production line, food preservation, chemistry, agriculture and environmental monitoring. The new [...] Read more.

As the technology revolution and industrialization have flourished in the last few decades, the development of humidity nanosensors has become more important for the detection and control of humidity in the industry production line, food preservation, chemistry, agriculture and environmental monitoring. The new nanostructured materials and fabrication in nanosensors are linked to better sensor performance, especially for superior humidity sensing, following the intensive research into the design and synthesis of nanomaterials in the last few years. Various nanomaterials, such as ceramics, polymers, semiconductor and sulfide, carbon-based, triboelectrical nanogenerator (TENG), and MXene, have been studied for their potential ability to sense humidity with structures of nanowires, nanotubes, nanopores, and monolayers. These nanosensors have been synthesized via a wide range of processes, including solution synthesis, anodization, physical vapor deposition (PVD), or chemical vapor deposition (CVD). The sensing mechanism, process improvement and nanostructure modulation of different types of materials are mostly inexhaustible, but they are all inseparable from the goals of the effective response, high sensitivity and low response–recovery time of humidity sensors. In this review, we focus on the sensing mechanism of direct and indirect sensing, various fabrication methods, nanomaterial geometry and recent advances in humidity nanosensors. Various types of capacitive, resistive and optical humidity nanosensors are introduced, alongside illustration of the properties and nanostructures of various materials. The similarities and differences of the humidity-sensitive mechanisms of different types of materials are summarized. Applications such as IoT, and the environmental and human-body monitoring of nanosensors are the development trends for futures advancements. Full article

(This article belongs to the Special Issue Advances in Nanosensors and Nanogenerators)

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30 pages, 3257 KiB

Open AccessEditor’s ChoiceArticle

Research on Impact of IoT on Warehouse Management

by Aldona Jarašūnienė, Kristina Čižiūnienė and Audrius Čereška

Sensors 2023, 23(4), 2213; https://doi.org/10.3390/s23042213 - 16 Feb 2023

Cited by 17 | Viewed by 11472

Abstract

Automation and digitisation are the driving force of the Industrial Revolution 4.0. Industrial revolutions led to the mass production of goods, which increased the need for modern warehouses. Every year, the operation of warehouses becomes increasingly more complicated due to the increasing abundance [...] Read more.

Automation and digitisation are the driving force of the Industrial Revolution 4.0. Industrial revolutions led to the mass production of goods, which increased the need for modern warehouses. Every year, the operation of warehouses becomes increasingly more complicated due to the increasing abundance of goods, thus the usual warehouse management strategies are no longer suitable. In order to cope with huge product flows, modern innovations should be used more extensively to manage these processes. Successful management will help provide quality service to rapidly changing business sectors. The Internet of Things (IoT) is a technology designed to process large amounts of data with maximum efficiency in real time. This technology can facilitate the implementation of smart identification, tracking, tracing, and management using radio frequency identification (RFID), infrared sensors, global positioning systems (GPS), laser scanners, and other detection tools. Such innovations as IoT have made a significant impact on warehousing operations. The aim of IoT is to perform administrative work, i.e., to efficiently manage warehouse data. IoT can be used to monitor and track goods, forecast demand trends, manage inventory, and perform other warehouse operations in real time. The key elements of a warehouse are sales and customer satisfaction. Implementing IoT improves financial performance, work productivity, and customer satisfaction. However, innovation requires additional investment in, for instance, implementation and maintenance. It is necessary to investigate how warehouse elements such as inventory accuracy or order processing time are affected by the internet of things in companies of different sizes. Research on the impact of IoT on warehouse management focuses on IoT advantages, disadvantages, mitigation risks, and the use of IoT in warehouses. The aim of this work is to research the impact of IoT on warehouse management in companies of different sizes and to determine whether the costs and benefits of IoT differ in the same scenario. As a result, the conceptual model for the adoption of IoT measures in warehouse companies was created, and its suitability was assessed by experts. Full article

(This article belongs to the Special Issue Sensors Technologies in the Era of Smart Factory and Industry 4.0)

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40 pages, 4621 KiB

Open AccessEditor’s ChoiceReview

Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities

by Md Golam Morshed, Tangina Sultana, Aftab Alam and Young-Koo Lee

Sensors 2023, 23(4), 2182; https://doi.org/10.3390/s23042182 - 15 Feb 2023

Cited by 33 | Viewed by 9829

Abstract

Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature [...] Read more.

Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition that investigates recent developments in computer vision. Augmented reality, human–computer interaction, cybersecurity, home monitoring, and surveillance cameras are all examples of computer vision applications that often go in conjunction with human action detection. We give a taxonomy-based, rigorous study of human activity recognition techniques, discussing the best ways to acquire human action features, derived using RGB and depth data, as well as the latest research on deep learning and hand-crafted techniques. We also explain a generic architecture to recognize human actions in the real world and its current prominent research topic. At long last, we are able to offer some study analysis concepts and proposals for academics. In-depth researchers of human action recognition will find this review an effective tool. Full article

(This article belongs to the Section Intelligent Sensors)

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21 pages, 1537 KiB

Open AccessEditor’s ChoiceArticle

Data Dissemination in VANETs Using Particle Swarm Optimization

by Dhwani Desai, Hosam El-Ocla and Surbhi Purohit

Sensors 2023, 23(4), 2124; https://doi.org/10.3390/s23042124 - 13 Feb 2023

Cited by 9 | Viewed by 2333

Abstract

A vehicular Ad-Hoc Network (VANET) is a type of Mobile Ad-Hoc Networks (MANETs) that uses wireless routers inside each vehicle to act as a node. The need for effective solutions to urban traffic congestion issues has increased recently due to the growing number [...] Read more.

A vehicular Ad-Hoc Network (VANET) is a type of Mobile Ad-Hoc Networks (MANETs) that uses wireless routers inside each vehicle to act as a node. The need for effective solutions to urban traffic congestion issues has increased recently due to the growing number of automobile connections in the car communications system. To ensure a high level of service and avoid unsafe situations brought on by congestion or a broadcast storm, data dissemination in a VANET network requires an effective approach. Effective multi-objective optimization methods are required to tackle this because of the implied competing nature of multi-metric approaches. A meta-heuristic technique with a high level of solution interactions can handle efficient optimization. To accomplish this, a meta-heuristic search algorithm particle optimization was chosen. In this paper, we have created a network consisting of vehicles as nodes. The aim is to send emergency messages immediately to the stationary nodes. The normal messages will be sent to the FIFO queue. To send these messages to a destination node, multiple routes were found using Time delay-based Multipath Routing (TMR) method, and to find the optimal and secure path Particle Swarm Optimization (PSO) is used. Our method is compared with different optimization methods such as Ant Colony Optimization (ACO), Firefly Optimization (FFO), and Enhanced Flying Ant Colony Optimization (EFACO). Significant improvements in terms of throughput and packet loss ratio, reduced end-to-end delay, rounding overhead ratio, and the energy consumption are revealed by the experimental results. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Communications)

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26 pages, 1199 KiB

Open AccessEditor’s ChoiceReview

Control and Optimisation of Power Grids Using Smart Meter Data: A Review

by Zhiyi Chen, Ali Moradi Amani, Xinghuo Yu and Mahdi Jalili

Sensors 2023, 23(4), 2118; https://doi.org/10.3390/s23042118 - 13 Feb 2023

Cited by 36 | Viewed by 9643

Abstract

This paper provides a comprehensive review of the applications of smart meters in the control and optimisation of power grids to support a smooth energy transition towards the renewable energy future. The smart grids become more complicated due to the presence of small-scale [...] Read more.

This paper provides a comprehensive review of the applications of smart meters in the control and optimisation of power grids to support a smooth energy transition towards the renewable energy future. The smart grids become more complicated due to the presence of small-scale low inertia generators and the implementation of electric vehicles (EVs), which are mainly based on intermittent and variable renewable energy resources. Optimal and reliable operation of this environment using conventional model-based approaches is very difficult. Advancements in measurement and communication technologies have brought the opportunity of collecting temporal or real-time data from prosumers through Advanced Metering Infrastructure (AMI). Smart metering brings the potential of applying data-driven algorithms for different power system operations and planning services, such as infrastructure sizing and upgrade and generation forecasting. It can also be used for demand-side management, especially in the presence of new technologies such as EVs, 5G/6G networks and cloud computing. These algorithms face privacy-preserving and cybersecurity challenges that need to be well addressed. This article surveys the state-of-the-art of each of these topics, reviewing applications, challenges and opportunities of using smart meters to address them. It also stipulates the challenges that smart grids present to smart meters and the benefits that smart meters can bring to smart grids. Furthermore, the paper is concluded with some expected future directions and potential research questions for smart meters, smart grids and their interplay. Full article

(This article belongs to the Special Issue Deep Learning Control for Sensors and IoT Applications)

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19 pages, 7647 KiB

Open AccessEditor’s ChoiceArticle

Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals

by Taraneh Aminosharieh Najafi, Antonio Affanni, Roberto Rinaldo and Pamela Zontone

Sensors 2023, 23(4), 2039; https://doi.org/10.3390/s23042039 - 11 Feb 2023

Cited by 8 | Viewed by 2988

Abstract

In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual [...] Read more.

In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual and autonomous driving scenarios. Most systems described in the literature use cameras to evaluate features such as blink rate and gaze direction. In this study, we instead analyse the subjects’ Electrodermal activity (EDA) Skin Potential Response (SPR), their Electrocardiogram (ECG), and their Electroencephalogram (EEG). From these signals we extract a number of physiological measures, including eye blink rate and beta frequency band power from EEG, heart rate from ECG, and SPR features, then investigate their capability to assess the mental state and engagement level of the test subjects. In particular, and as confirmed by statistical tests, the signals reveal that in the manual scenario the subjects experienced a more challenged mental state and paid higher attention to driving tasks compared to the autonomous scenario. A different experiment in which subjects drove in three different setups, i.e., a manual driving scenario and two autonomous driving scenarios characterized by different vehicle settings, confirmed that manual driving is more mentally demanding than autonomous driving. Therefore, we can conclude that the proposed approach is an appropriate way to monitor driver attention. Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Automotive)

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