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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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33 pages, 4823 KiB  
Article
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 5 | Viewed by 3439
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  
Article
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 5 | Viewed by 5497
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  
Review
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 12 | Viewed by 5350
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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26 pages, 1275 KiB  
Review
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 7 | Viewed by 2527
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  
Review
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 3280
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  
Article
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 4 | Viewed by 4633
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±8.3 mmHg (mean error ± standard deviation) and a diastolic error of 7.7±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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28 pages, 1981 KiB  
Review
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 3739
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  
Article
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 7 | Viewed by 4467
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  
Review
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 21 | Viewed by 6117
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  
Review
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 5130
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  
Review
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 17 | Viewed by 8074
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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15 pages, 3868 KiB  
Article
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 4129
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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23 pages, 18382 KiB  
Article
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 2755
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
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14 pages, 8451 KiB  
Article
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 10860
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, 32729 KiB  
Article
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 10 | Viewed by 2117
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  
Article
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 7 | Viewed by 5071
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  
Review
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 20 | Viewed by 8896
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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26 pages, 4081 KiB  
Article
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 6954
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
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31 pages, 7945 KiB  
Review
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 5717
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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12 pages, 2540 KiB  
Article
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 5 | Viewed by 2033
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  
Review
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 4259
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  
Review
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 3328
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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32 pages, 5673 KiB  
Review
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 4707
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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19 pages, 7647 KiB  
Article
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 2796
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
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16 pages, 2680 KiB  
Article
Minimum-Time Trajectory Generation for Wheeled Mobile Systems Using Bézier Curves with Constraints on Velocity, Acceleration and Jerk
by Martina Benko Loknar, Gregor Klančar and Sašo Blažič
Sensors 2023, 23(4), 1982; https://doi.org/10.3390/s23041982 - 10 Feb 2023
Cited by 11 | Viewed by 2490
Abstract
This paper considers the problem of minimum-time smooth trajectory planning for wheeled mobile robots. The smooth path is defined by several Bézier curves and the calculated velocity profiles on individual segments are minimum-time with continuous velocity and acceleration in the joints. We describe [...] Read more.
This paper considers the problem of minimum-time smooth trajectory planning for wheeled mobile robots. The smooth path is defined by several Bézier curves and the calculated velocity profiles on individual segments are minimum-time with continuous velocity and acceleration in the joints. We describe a novel solution for the construction of a 5th order Bézier curve that enables a simple and intuitive parameterization. The proposed trajectory optimization considers environment space constraints and constraints on the velocity, acceleration, and jerk. The operation of the trajectory planning algorithm has been demonstrated in two simulations: on a racetrack and in a warehouse environment. Therefore, we have shown that the proposed path construction and trajectory generation algorithm can be applied to a constrained environment and can also be used in real-world driving scenarios. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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23 pages, 6358 KiB  
Article
Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints
by Peter Sarcevic, Dominik Csik and Akos Odry
Sensors 2023, 23(4), 1855; https://doi.org/10.3390/s23041855 - 7 Feb 2023
Cited by 17 | Viewed by 2794
Abstract
Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained [...] Read more.
Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained increasing attention in recent years, since this technology provides stable measurements with low random fluctuations. In this paper, a novel fingerprinting-based indoor 2D positioning method, which utilizes the fusion of RSSI and magnetometer measurements, is proposed for mobile robots. The method applies multilayer perceptron (MLP) feedforward neural networks to determine the 2D position, based on both the magnetometer data and the RSSI values measured between the mobile unit and anchor nodes. The magnetic field strength is measured on the mobile node, and it provides information about the disturbance levels in the given position. The proposed method is validated using data collected in two realistic indoor scenarios with multiple static objects. The magnetic field measurements are examined in three different combinations, i.e., the measurements of the three sensor axes are tested together, the magnetic field magnitude is used alone, and the Z-axis-based measurements are used together with the magnitude in the X-Y plane. The obtained results show that significant improvement can be achieved by fusing the two data types in scenarios where the magnetic field has high variance. The achieved results show that the improvement can be above 35% compared to results obtained by utilizing only RSSI or magnetic sensor data. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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38 pages, 10981 KiB  
Article
UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping
by Canh Nguyen, Vasit Sagan, Sourav Bhadra and Stephen Moose
Sensors 2023, 23(4), 1827; https://doi.org/10.3390/s23041827 - 6 Feb 2023
Cited by 11 | Viewed by 3803
Abstract
Recent advances in unmanned aerial vehicles (UAV), mini and mobile sensors, and GeoAI (a blend of geospatial and artificial intelligence (AI) research) are the main highlights among agricultural innovations to improve crop productivity and thus secure vulnerable food systems. This study investigated the [...] Read more.
Recent advances in unmanned aerial vehicles (UAV), mini and mobile sensors, and GeoAI (a blend of geospatial and artificial intelligence (AI) research) are the main highlights among agricultural innovations to improve crop productivity and thus secure vulnerable food systems. This study investigated the versatility of UAV-borne multisensory data fusion within a framework of multi-task deep learning for high-throughput phenotyping in maize. UAVs equipped with a set of miniaturized sensors including hyperspectral, thermal, and LiDAR were collected in an experimental corn field in Urbana, IL, USA during the growing season. A full suite of eight phenotypes was in situ measured at the end of the season for ground truth data, specifically, dry stalk biomass, cob biomass, dry grain yield, harvest index, grain nitrogen utilization efficiency (Grain NutE), grain nitrogen content, total plant nitrogen content, and grain density. After being funneled through a series of radiometric calibrations and geo-corrections, the aerial data were analytically processed in three primary approaches. First, an extended version normalized difference spectral index (NDSI) served as a simple arithmetic combination of different data modalities to explore the correlation degree with maize phenotypes. The extended NDSI analysis revealed the NIR spectra (750–1000 nm) alone in a strong relation with all of eight maize traits. Second, a fusion of vegetation indices, structural indices, and thermal index selectively handcrafted from each data modality was fed to classical machine learning regressors, Support Vector Machine (SVM) and Random Forest (RF). The prediction performance varied from phenotype to phenotype, ranging from R2 = 0.34 for grain density up to R2 = 0.85 for both grain nitrogen content and total plant nitrogen content. Further, a fusion of hyperspectral and LiDAR data completely exceeded limitations of single data modality, especially addressing the vegetation saturation effect occurring in optical remote sensing. Third, a multi-task deep convolutional neural network (CNN) was customized to take a raw imagery data fusion of hyperspectral, thermal, and LiDAR for multi-predictions of maize traits at a time. The multi-task deep learning performed predictions comparably, if not better in some traits, with the mono-task deep learning and machine learning regressors. Data augmentation used for the deep learning models boosted the prediction accuracy, which helps to alleviate the intrinsic limitation of a small sample size and unbalanced sample classes in remote sensing research. Theoretical and practical implications to plant breeders and crop growers were also made explicit during discussions in the studies. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Smart Agriculture)
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33 pages, 3375 KiB  
Article
A Modular In-Vehicle C-ITS Architecture for Sensor Data Collection, Vehicular Communications and Cloud Connectivity
by David Rocha, Gil Teixeira, Emanuel Vieira, João Almeida and Joaquim Ferreira
Sensors 2023, 23(3), 1724; https://doi.org/10.3390/s23031724 - 3 Feb 2023
Cited by 13 | Viewed by 3373
Abstract
The growth of the automobile industry in recent decades and the overuse of personal vehicles have amplified problems directly related to road safety, such as the increase in traffic congestion and number of accidents, as well as the degradation of the quality of [...] Read more.
The growth of the automobile industry in recent decades and the overuse of personal vehicles have amplified problems directly related to road safety, such as the increase in traffic congestion and number of accidents, as well as the degradation of the quality of roads. At the same time, and with the contribution of climate change effects, dangerous weather events have become more common on road infrastructure. In this context, Cooperative Intelligent Transport Systems (C-ITS) and Internet of Things (IoT) solutions emerge to overcome the limitations of human and local sensory systems, through the collection and distribution of relevant data to Connected and Automated Vehicles (CAVs). In this paper, an intra- and inter-vehicle sensory data collection system is presented, starting with the acquisition of relevant data present on the Controller Area Network (CAN) bus, collected through the vehicle’s On-Board-Diagnostics II (OBD-II) port, as well as on an on-board smartphone device and possibly other additional sensors. Short-range communication technologies, such as Bluetooth Low Energy (BLE), Wi-Fi, and ITS-G5, are employed in conjunction with long-range cellular networks for data dissemination and remote cloud monitoring. The results of the experimental tests allow the analysis of the road environment, as well as the notification in near real-time of adverse road conditions to drivers. The developed data collection system reveals itself as a potentially valuable tool for improving road safety and to iterate on the current Road Weather Models (RWMs). Full article
(This article belongs to the Special Issue Sensor Networks for Vehicular Communications)
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23 pages, 3561 KiB  
Article
Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors
by Pekka Siirtola, Satu Tamminen, Gunjan Chandra, Anusha Ihalapathirana and Juha Röning
Sensors 2023, 23(3), 1598; https://doi.org/10.3390/s23031598 - 1 Feb 2023
Cited by 8 | Viewed by 5503
Abstract
This study aims to predict emotions using biosignals collected via wrist-worn sensor and evaluate the performance of different prediction models. Two dimensions of emotions were considered: valence and arousal. The data collected by the sensor were used in conjunction with target values obtained [...] Read more.
This study aims to predict emotions using biosignals collected via wrist-worn sensor and evaluate the performance of different prediction models. Two dimensions of emotions were considered: valence and arousal. The data collected by the sensor were used in conjunction with target values obtained from questionnaires. A variety of classification and regression models were compared, including Long Short-Term Memory (LSTM) models. Additionally, the effects of different normalization methods and the impact of using different sensors were studied, and the way in which the results differed between the study subjects was analyzed. The results revealed that regression models generally performed better than classification models, with LSTM regression models achieving the best results. The normalization method called baseline reduction was found to be the most effective, and when used with an LSTM-based regression model it achieved high accuracy in detecting valence (mean square error = 0.43 and R2-score = 0.71) and arousal (mean square error = 0.59 and R2-score = 0.81). Moreover, it was found that even if all biosignals were not used in the training phase, reliable models could be obtained; in fact, for certain study subjects the best results were obtained using only a few of the sensors. Full article
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11 pages, 3557 KiB  
Article
A Hydrogel-Based Electronic Skin for Touch Detection Using Electrical Impedance Tomography
by Huiyang Zhang, Anubha Kalra, Andrew Lowe, Yang Yu and Gautam Anand
Sensors 2023, 23(3), 1571; https://doi.org/10.3390/s23031571 - 1 Feb 2023
Cited by 8 | Viewed by 2598
Abstract
Recent advancement in wearable and robot-assisted healthcare technology gives rise to the demand for smart interfaces that allow more efficient human-machine interaction. In this paper, a hydrogel-based soft sensor for subtle touch detection is proposed. Adopting the working principle of a biomedical imaging [...] Read more.
Recent advancement in wearable and robot-assisted healthcare technology gives rise to the demand for smart interfaces that allow more efficient human-machine interaction. In this paper, a hydrogel-based soft sensor for subtle touch detection is proposed. Adopting the working principle of a biomedical imaging technology known as electrical impedance tomography (EIT), the sensor produces images that display the electrical conductivity distribution of its sensitive region to enable touch detection. The sensor was made from a natural gelatin hydrogel whose electrical conductivity is considerably less than that of human skin. The low conductivity of the sensor enabled a touch-detection mechanism based on a novel short-circuiting approach, which resulted in the reconstructed images being predominantly affected by the electrical contact between the sensor and fingertips, rather than the conventionally used piezoresistive response of the sensing material. The experimental results indicated that the proposed sensor was promising for detecting subtle contacts without the necessity of exerting a noticeable force on the sensor. Full article
(This article belongs to the Special Issue Interactive, Mobile, Wearable Sensors and Technology for Elderly Care)
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22 pages, 6249 KiB  
Article
Prehensile and Non-Prehensile Robotic Pick-and-Place of Objects in Clutter Using Deep Reinforcement Learning
by Muhammad Babar Imtiaz, Yuansong Qiao and Brian Lee
Sensors 2023, 23(3), 1513; https://doi.org/10.3390/s23031513 - 29 Jan 2023
Cited by 7 | Viewed by 5067
Abstract
In this study, we develop a framework for an intelligent and self-supervised industrial pick-and-place operation for cluttered environments. Our target is to have the agent learn to perform prehensile and non-prehensile robotic manipulations to improve the efficiency and throughput of the pick-and-place task. [...] Read more.
In this study, we develop a framework for an intelligent and self-supervised industrial pick-and-place operation for cluttered environments. Our target is to have the agent learn to perform prehensile and non-prehensile robotic manipulations to improve the efficiency and throughput of the pick-and-place task. To achieve this target, we specify the problem as a Markov decision process (MDP) and deploy a deep reinforcement learning (RL) temporal difference model-free algorithm known as the deep Q-network (DQN). We consider three actions in our MDP; one is ‘grasping’ from the prehensile manipulation category and the other two are ‘left-slide’ and ‘right-slide’ from the non-prehensile manipulation category. Our DQN is composed of three fully convolutional networks (FCN) based on the memory-efficient architecture of DenseNet-121 which are trained together without causing any bottleneck situations. Each FCN corresponds to each discrete action and outputs a pixel-wise map of affordances for the relevant action. Rewards are allocated after every forward pass and backpropagation is carried out for weight tuning in the corresponding FCN. In this manner, non-prehensile manipulations are learnt which can, in turn, lead to possible successful prehensile manipulations in the near future and vice versa, thus increasing the efficiency and throughput of the pick-and-place task. The Results section shows performance comparisons of our approach to a baseline deep learning approach and a ResNet architecture-based approach, along with very promising test results at varying clutter densities across a range of complex scenario test cases. Full article
(This article belongs to the Special Issue Sensors for Robots II)
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18 pages, 5176 KiB  
Article
Energy Balance of Wireless Sensor Nodes Based on Bluetooth Low Energy and Thermoelectric Energy Harvesting
by Yuming Liu, Jordi-Roger Riba and Manuel Moreno-Eguilaz
Sensors 2023, 23(3), 1480; https://doi.org/10.3390/s23031480 - 28 Jan 2023
Cited by 10 | Viewed by 2057
Abstract
The internet of things (IoT) makes it possible to measure physical variables and acquire data in places that were impossible a few years ago, such as transmission lines and electrical substations. Monitoring and fault diagnosis strategies can then be applied. A battery or [...] Read more.
The internet of things (IoT) makes it possible to measure physical variables and acquire data in places that were impossible a few years ago, such as transmission lines and electrical substations. Monitoring and fault diagnosis strategies can then be applied. A battery or an energy harvesting system charging a rechargeable battery typically powers IoT devices. The energy harvesting unit and rechargeable battery supply the sensors and wireless communications modules. Therefore, the energy harvesting unit must be correctly sized to optimize the availability and reliability of IoT devices. This paper applies a power balance of the entire IoT device, including the energy harvesting module that includes two thermoelectric generators and a DC–DC converter, the battery, and the sensors and communication modules. Due to the small currents typical of the different communication phases and their fast-switching nature, it is not trivial to measure the energy in each phase, requiring very specific instrumentation. This work shows that using conventional instrumentation it is possible to measure the energy involved in the different modes of communication. A detailed energy balance of the battery is also carried out during charge and discharge cycles, as well as communication modes, from which the maximum allowable data transfer rate is determined. The approach presented here can be generalized to many other smart grid IoT devices. Full article
(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)
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17 pages, 2068 KiB  
Article
On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
by Irene Vilà, Oriol Sallent and Jordi Pérez-Romero
Sensors 2023, 23(3), 1197; https://doi.org/10.3390/s23031197 - 20 Jan 2023
Cited by 6 | Viewed by 3349
Abstract
A Network Digital Twin (NDT) is a high-fidelity digital mirror of a real network. Given the increasing complexity of 5G and beyond networks, the use of an NDT becomes useful as a platform for testing configurations and algorithms prior to their application in [...] Read more.
A Network Digital Twin (NDT) is a high-fidelity digital mirror of a real network. Given the increasing complexity of 5G and beyond networks, the use of an NDT becomes useful as a platform for testing configurations and algorithms prior to their application in the real network, as well as for predicting the performance of such algorithms under different conditions. While an NDT can be defined for the different subsystems of the network, this paper proposes an NDT architecture focusing on the Radio Access Network (RAN), describing the components to represent and model the operation of the different RAN elements, and to perform emulations. Different application use cases are identified, and among them, the paper puts the focus on the training of Reinforcement Learning (RL) solutions for the RAN. For this use case, the paper introduces a framework aligned with O-RAN specifications and discusses the functionalities needed to integrate the NDT. This use case is illustrated with the description of a RAN NDT implementation used for training an RL-based capacity-sharing solution for network slicing. Presented results demonstrate that the implemented RAN NDT is a suitable platform to successfully train the RL solution, achieving service-level agreement satisfaction values above 85%. Full article
(This article belongs to the Special Issue State of the Art in beyond 5G and 6G Radio Communications Networks)
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24 pages, 22280 KiB  
Article
Estimation of Sugar Content in Wine Grapes via In Situ VNIR–SWIR Point Spectroscopy Using Explainable Artificial Intelligence Techniques
by Eleni Kalopesa, Konstantinos Karyotis, Nikolaos Tziolas, Nikolaos Tsakiridis, Nikiforos Samarinas and George Zalidis
Sensors 2023, 23(3), 1065; https://doi.org/10.3390/s23031065 - 17 Jan 2023
Cited by 17 | Viewed by 7614
Abstract
Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be [...] Read more.
Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR–SWIR spectrum (350–2500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR–SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination (R2), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm (R2>0.8, RPIQ4), while a good fit was attained for the Chardonnay variety from SVR (R2=0.63, RMSE=2.10, RPIQ=2.24), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way. Full article
(This article belongs to the Special Issue Smart Decision Systems for Digital Farming)
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22 pages, 4707 KiB  
Review
Electrochemical (Bio)Sensing Devices for Human-Microbiome-Related Biomarkers
by Esther Sánchez-Tirado, Lourdes Agüí, Araceli González-Cortés, Susana Campuzano, Paloma Yáñez-Sedeño and José Manuel Pingarrón
Sensors 2023, 23(2), 837; https://doi.org/10.3390/s23020837 - 11 Jan 2023
Cited by 5 | Viewed by 3047
Abstract
The study of the human microbiome is a multidisciplinary area ranging from the field of technology to that of personalized medicine. The possibility of using microbiota biomarkers to improve the diagnosis and monitoring of diseases (e.g., cancer), health conditions (e.g., obesity) or relevant [...] Read more.
The study of the human microbiome is a multidisciplinary area ranging from the field of technology to that of personalized medicine. The possibility of using microbiota biomarkers to improve the diagnosis and monitoring of diseases (e.g., cancer), health conditions (e.g., obesity) or relevant processes (e.g., aging) has raised great expectations, also in the field of bioelectroanalytical chemistry. The well-known advantages of electrochemical biosensors—high sensitivity, fast response, and the possibility of miniaturization, together with the potential for new nanomaterials to improve their design and performance—position them as unique tools to provide a better understanding of the entities of the human microbiome and raise the prospect of huge and important developments in the coming years. This review article compiles recent applications of electrochemical (bio)sensors for monitoring microbial metabolites and disease biomarkers related to different types of human microbiome, with a special focus on the gastrointestinal microbiome. Examples of electrochemical devices applied to real samples are critically discussed, as well as challenges to be faced and where future developments are expected to go. Full article
(This article belongs to the Special Issue Electrochemical Sensors for Analytical Applications)
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15 pages, 1471 KiB  
Review
Robotic Technology in Foot and Ankle Surgery: A Comprehensive Review
by Taylor P. Stauffer, Billy I. Kim, Caitlin Grant, Samuel B. Adams and Albert T. Anastasio
Sensors 2023, 23(2), 686; https://doi.org/10.3390/s23020686 - 6 Jan 2023
Cited by 4 | Viewed by 3503
Abstract
Recent developments in robotic technologies in the field of orthopaedic surgery have largely been focused on higher volume arthroplasty procedures, with a paucity of attention paid to robotic potential for foot and ankle surgery. The aim of this paper is to summarize past [...] Read more.
Recent developments in robotic technologies in the field of orthopaedic surgery have largely been focused on higher volume arthroplasty procedures, with a paucity of attention paid to robotic potential for foot and ankle surgery. The aim of this paper is to summarize past and present developments foot and ankle robotics and describe outcomes associated with these interventions, with specific emphasis on the following topics: translational and preclinical utilization of robotics, deep learning and artificial intelligence modeling in foot and ankle, current applications for robotics in foot and ankle surgery, and therapeutic and orthotic-related utilizations of robotics related to the foot and ankle. Herein, we describe numerous recent robotic advancements across foot and ankle surgery, geared towards optimizing intra-operative performance, improving detection of foot and ankle pathology, understanding ankle kinematics, and rehabilitating post-surgically. Future research should work to incorporate robotics specifically into surgical procedures as other specialties within orthopaedics have done, and to further individualize machinery to patients, with the ultimate goal to improve perioperative and post-operative outcomes. Full article
(This article belongs to the Special Issue Medical Robotics 2022-2023)
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18 pages, 10051 KiB  
Article
Development of Magnetocardiograph without Magnetically Shielded Room Using High-Detectivity TMR Sensors
by Koshi Kurashima, Makoto Kataoka, Takafumi Nakano, Kosuke Fujiwara, Seiichi Kato, Takenobu Nakamura, Masaki Yuzawa, Masanori Masuda, Kakeru Ichimura, Shigeki Okatake, Yoshitaka Moriyasu, Kazuhiro Sugiyama, Mikihiko Oogane, Yasuo Ando, Seiji Kumagai, Hitoshi Matsuzaki and Hidenori Mochizuki
Sensors 2023, 23(2), 646; https://doi.org/10.3390/s23020646 - 6 Jan 2023
Cited by 13 | Viewed by 3872
Abstract
A magnetocardiograph that enables the clear observation of heart magnetic field mappings without magnetically shielded rooms at room temperatures has been successfully manufactured. Compared to widespread electrocardiographs, magnetocardiographs commonly have a higher spatial resolution, which is expected to lead to early diagnoses of [...] Read more.
A magnetocardiograph that enables the clear observation of heart magnetic field mappings without magnetically shielded rooms at room temperatures has been successfully manufactured. Compared to widespread electrocardiographs, magnetocardiographs commonly have a higher spatial resolution, which is expected to lead to early diagnoses of ischemic heart disease and high diagnostic accuracy of ventricular arrhythmia, which involves the risk of sudden death. However, as the conventional superconducting quantum interference device (SQUID) magnetocardiographs require large magnetically shielded rooms and huge running costs to cool the SQUID sensors, magnetocardiography is still unfamiliar technology. Here, in order to achieve the heart field detectivity of 1.0 pT without magnetically shielded rooms and enough magnetocardiography accuracy, we aimed to improve the detectivity of tunneling magnetoresistance (TMR) sensors and to decrease the environmental and sensor noises with a mathematical algorithm. The magnetic detectivity of the TMR sensors was confirmed to be 14.1 pTrms on average in the frequency band between 0.2 and 100 Hz in uncooled states, thanks to the original multilayer structure and the innovative pattern of free layers. By constructing a sensor array using 288 TMR sensors and applying the mathematical magnetic shield technology of signal space separation (SSS), we confirmed that SSS reduces the environmental magnetic noise by −73 dB, which overtakes the general triple magnetically shielded rooms. Moreover, applying digital processing that combined the signal average of heart magnetic fields for one minute and the projection operation, we succeeded in reducing the sensor noise by about −23 dB. The heart magnetic field resolution measured on a subject in a laboratory in an office building was 0.99 pTrms and obtained magnetocardiograms and current arrow maps as clear as the SQUID magnetocardiograph does in the QRS and ST segments. Upon utilizing its superior spatial resolution, this magnetocardiograph has the potential to be an important tool for the early diagnosis of ischemic heart disease and the risk management of sudden death triggered by ventricular arrhythmia. Full article
(This article belongs to the Special Issue Advanced Imaging and Sensing Technologies of Cardiovascular Disease)
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47 pages, 6423 KiB  
Review
A Review on Bio- and Chemosensors for the Detection of Biogenic Amines in Food Safety Applications: The Status in 2022
by Stella Givanoudi, Marc Heyndrickx, Tom Depuydt, Mehran Khorshid, Johan Robbens and Patrick Wagner
Sensors 2023, 23(2), 613; https://doi.org/10.3390/s23020613 - 5 Jan 2023
Cited by 14 | Viewed by 6013
Abstract
This article provides an overview on the broad topic of biogenic amines (BAs) that are a persistent concern in the context of food quality and safety. They emerge mainly from the decomposition of amino acids in protein-rich food due to enzymes excreted by [...] Read more.
This article provides an overview on the broad topic of biogenic amines (BAs) that are a persistent concern in the context of food quality and safety. They emerge mainly from the decomposition of amino acids in protein-rich food due to enzymes excreted by pathogenic bacteria that infect food under inappropriate storage conditions. While there are food authority regulations on the maximum allowed amounts of, e.g., histamine in fish, sensitive individuals can still suffer from medical conditions triggered by biogenic amines, and mass outbreaks of scombroid poisoning are reported regularly. We review first the classical techniques used for selective BA detection and quantification in analytical laboratories and focus then on sensor-based solutions aiming at on-site BA detection throughout the food chain. There are receptor-free chemosensors for BA detection and a vastly growing range of bio- and biomimetic sensors that employ receptors to enable selective molecular recognition. Regarding the receptors, we address enzymes, antibodies, molecularly imprinted polymers (MIPs), and aptamers as the most recent class of BA receptors. Furthermore, we address the underlying transducer technologies, including optical, electrochemical, mass-sensitive, and thermal-based sensing principles. The review concludes with an assessment on the persistent limitations of BA sensors, a technological forecast, and thoughts on short-term solutions. Full article
(This article belongs to the Special Issue Feature Review Papers for Chemical Sensors and Biosensors in 2022)
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20 pages, 9638 KiB  
Article
Grass Cutting Robot for Inclined Surfaces in Hilly and Mountainous Areas
by Yuki Nishimura and Tomoyuki Yamaguchi
Sensors 2023, 23(1), 528; https://doi.org/10.3390/s23010528 - 3 Jan 2023
Cited by 3 | Viewed by 4304
Abstract
Grass cutting is necessary to prevent grass from diverting essential nutrients and water from crops. Usually, in hilly and mountainous areas, grass cutting is performed on steep slopes with an inclination angle of up to 60° (inclination gradient of 173%). However, such grass [...] Read more.
Grass cutting is necessary to prevent grass from diverting essential nutrients and water from crops. Usually, in hilly and mountainous areas, grass cutting is performed on steep slopes with an inclination angle of up to 60° (inclination gradient of 173%). However, such grass cutting tasks are dangerous owing to the unstable positioning of workers. For robots to perform these grass cutting tasks, slipping and falling must be prevented on inclined surfaces. In this study, a robot based on stable propeller control and four-wheel steering was developed to provide stable locomotion during grass cutting tasks. The robot was evaluated in terms of locomotion for different steering methods, straight motion on steep slopes, climbing ability, and coverage area. The results revealed that the robot was capable of navigating uneven terrains with steep slope angles. Moreover, no slipping actions that could have affected the grass cutting operations were observed. We confirmed that the proposed robot is able to cover 99.95% and 98.45% of an area on a rubber and grass slope, respectively. Finally, the robot was tested on different slopes with different angles in hilly and mountainous areas. The developed robot was able to perform the grass cutting task as expected. Full article
(This article belongs to the Special Issue Sensors and Robotic Systems for Agriculture Applications)
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18 pages, 3816 KiB  
Article
Deep Learning Framework for Controlling Work Sequence in Collaborative Human–Robot Assembly Processes
by Pedro P. Garcia, Telmo G. Santos, Miguel A. Machado and Nuno Mendes
Sensors 2023, 23(1), 553; https://doi.org/10.3390/s23010553 - 3 Jan 2023
Cited by 7 | Viewed by 3108
Abstract
The human–robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human’s state or on specific gestures purposely performed by the human, thus increasing the time required to perform a task and [...] Read more.
The human–robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human’s state or on specific gestures purposely performed by the human, thus increasing the time required to perform a task and slowing down the pace of human labor, making such solutions uninteresting. In this study, a different concept of the HRC system is introduced, consisting of an HRC framework for managing assembly processes that are executed simultaneously or individually by humans and robots. This HRC framework based on deep learning models uses only one type of data, RGB camera data, to make predictions about the collaborative workspace and human action, and consequently manage the assembly process. To validate the HRC framework, an industrial HRC demonstrator was built to assemble a mechanical component. Four different HRC frameworks were created based on the convolutional neural network (CNN) model structures: Faster R-CNN ResNet-50 and ResNet-101, YOLOv2 and YOLOv3. The HRC framework with YOLOv3 structure showed the best performance, showing a mean average performance of 72.26% and allowed the HRC industrial demonstrator to successfully complete all assembly tasks within a desired time window. The HRC framework has proven effective for industrial assembly applications. Full article
(This article belongs to the Special Issue Sensors for Robotic Applications in Europe)
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30 pages, 7537 KiB  
Article
Adsorbed Oxygen Ions and Oxygen Vacancies: Their Concentration and Distribution in Metal Oxide Chemical Sensors and Influencing Role in Sensitivity and Sensing Mechanisms
by Engin Ciftyurek, Zheshen Li and Klaus Schierbaum
Sensors 2023, 23(1), 29; https://doi.org/10.3390/s23010029 - 20 Dec 2022
Cited by 24 | Viewed by 4671
Abstract
Oxidation reactions on semiconducting metal oxide (SMOs) surfaces have been extensively worked on in catalysis, fuel cells, and sensors. SMOs engage powerfully in energy-related applications such as batteries, supercapacitors, solid oxide fuel cells (SOFCs), and sensors. A deep understanding of SMO surface and [...] Read more.
Oxidation reactions on semiconducting metal oxide (SMOs) surfaces have been extensively worked on in catalysis, fuel cells, and sensors. SMOs engage powerfully in energy-related applications such as batteries, supercapacitors, solid oxide fuel cells (SOFCs), and sensors. A deep understanding of SMO surface and oxygen interactions and defect engineering has become significant because all of the above-mentioned applications are based on the adsorption/absorption and consumption/transportation of adsorbed (physisorbed-chemisorbed) oxygen. More understanding of adsorbed oxygen and oxygen vacancies (VO,VO) is needed, as the former is the vital requirement for sensing chemical reactions, while the latter facilitates the replenishment of adsorbed oxygen ions on the surface. We determined the relation between sensor response (sensitivity) and the amounts of adsorbed oxygen ions (O2(ads), O(ads), O2(ads)2, O(ads)2), water/hydroxide groups (H2O/OH), oxygen vacancies (VO, VO), and ordinary lattice oxygen ions (Olattice2) as a function of temperature. During hydrogen (H2) testing, the different oxidation states (W6+, W5+, and W4+) of WO3 were quantified and correlated with oxygen vacancy formation (VO, VO). We used a combined application of XPS, UPS, XPEEM-LEEM, and chemical, electrical, and sensory analysis for H2 sensing. The sensor response was extraordinarily high: 424 against H2 at a temperature of 250 °C was recorded and explained on the basis of defect engineering, including oxygen vacancies and chemisorbed oxygen ions and surface stoichiometry of WO3. We established a correlation between the H2 sensing mechanism of WO3, sensor signal magnitude, the amount of adsorbed oxygen ions, and sensor testing temperature. This paper also provides a review of the detection, quantification, and identification of different adsorbed oxygen species. The different surface and bulk-sensitive characterization techniques relevant to analyzing the SMOs-based sensor are tabulated, providing the sensor designer with the chemical, physical, and electronic information extracted from each technique. Full article
(This article belongs to the Collection Gas Sensors)
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15 pages, 7710 KiB  
Article
A GNSS/INS/LiDAR Integration Scheme for UAV-Based Navigation in GNSS-Challenging Environments
by Ahmed Elamin, Nader Abdelaziz and Ahmed El-Rabbany
Sensors 2022, 22(24), 9908; https://doi.org/10.3390/s22249908 - 16 Dec 2022
Cited by 17 | Viewed by 3728
Abstract
Unmanned aerial vehicle (UAV) navigation has recently been the focus of many studies. The most challenging aspect of UAV navigation is maintaining accurate and reliable pose estimation. In outdoor environments, global navigation satellite systems (GNSS) are typically used for UAV localization. However, relying [...] Read more.
Unmanned aerial vehicle (UAV) navigation has recently been the focus of many studies. The most challenging aspect of UAV navigation is maintaining accurate and reliable pose estimation. In outdoor environments, global navigation satellite systems (GNSS) are typically used for UAV localization. However, relying solely on GNSS might pose safety risks in the event of receiver malfunction or antenna installation error. In this research, an unmanned aerial system (UAS) employing the Applanix APX15 GNSS/IMU board, a Velodyne Puck LiDAR sensor, and a Sony a7R II high-resolution camera was used to collect data for the purpose of developing a multi-sensor integration system. Unfortunately, due to a malfunctioning GNSS antenna, there were numerous prolonged GNSS signal outages. As a result, the GNSS/INS processing failed after obtaining an error that exceeded 25 km. To resolve this issue and to recover the precise trajectory of the UAV, a GNSS/INS/LiDAR integrated navigation system was developed. The LiDAR data were first processed using the optimized LOAM SLAM algorithm, which yielded the position and orientation estimates. Pix4D Mapper software was then used to process the camera images in the presence of ground control points (GCPs), which resulted in the precise camera positions and orientations that served as ground truth. All sensor data were timestamped by GPS, and all datasets were sampled at 10 Hz to match those of the LiDAR scans. Two case studies were considered, namely complete GNSS outage and assistance from GNSS PPP solution. In comparison to the complete GNSS outage, the results for the second case study were significantly improved. The improvement is described in terms of RMSE reductions of approximately 51% and 78% for the horizontal and vertical directions, respectively. Additionally, the RMSE of the roll and yaw angles was reduced by 13% and 30%, respectively. However, the RMSE of the pitch angle was increased by about 13%. Full article
(This article belongs to the Section Navigation and Positioning)
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25 pages, 3483 KiB  
Article
A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5
by Mukhriddin Mukhiddinov, Akmalbek Bobomirzaevich Abdusalomov and Jinsoo Cho
Sensors 2022, 22(23), 9384; https://doi.org/10.3390/s22239384 - 1 Dec 2022
Cited by 34 | Viewed by 6604
Abstract
Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural [...] Read more.
Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural environment and global warming. Early detection of fire ignition from initial smoke can help firefighters react to such blazes before they become difficult to handle. Previous deep-learning approaches for wildfire smoke detection have been hampered by small or untrustworthy datasets, making it challenging to extrapolate the performances to real-world scenarios. In this study, we propose an early wildfire smoke detection system using unmanned aerial vehicle (UAV) images based on an improved YOLOv5. First, we curated a 6000-wildfire image dataset using existing UAV images. Second, we optimized the anchor box clustering using the K-mean++ technique to reduce classification errors. Then, we improved the network’s backbone using a spatial pyramid pooling fast-plus layer to concentrate small-sized wildfire smoke regions. Third, a bidirectional feature pyramid network was applied to obtain a more accessible and faster multi-scale feature fusion. Finally, network pruning and transfer learning approaches were implemented to refine the network architecture and detection speed, and correctly identify small-scale wildfire smoke areas. The experimental results proved that the proposed method achieved an average precision of 73.6% and outperformed other one- and two-stage object detectors on a custom image dataset. Full article
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29 pages, 9493 KiB  
Review
Visual SLAM: What Are the Current Trends and What to Expect?
by Ali Tourani, Hriday Bavle, Jose Luis Sanchez-Lopez and Holger Voos
Sensors 2022, 22(23), 9297; https://doi.org/10.3390/s22239297 - 29 Nov 2022
Cited by 32 | Viewed by 16284
Abstract
In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are [...] Read more.
In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over Light Detection And Ranging (LiDAR)-based methods due to their lighter weight, lower acquisition costs, and richer environment representation. Hence, several VSLAM approaches have evolved using different camera types (e.g., monocular or stereo), and have been tested on various datasets (e.g., Technische Universität München (TUM) RGB-D or European Robotics Challenge (EuRoC)) and in different conditions (i.e., indoors and outdoors), and employ multiple methodologies to have a better understanding of their surroundings. The mentioned variations have made this topic popular for researchers and have resulted in various methods. In this regard, the primary intent of this paper is to assimilate the wide range of works in VSLAM and present their recent advances, along with discussing the existing challenges and trends. This survey is worthwhile to give a big picture of the current focuses in robotics and VSLAM fields based on the concentrated resolutions and objectives of the state-of-the-art. This paper provides an in-depth literature survey of fifty impactful articles published in the VSLAMs domain. The mentioned manuscripts have been classified by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. The paper also discusses the current trends and contemporary directions of VSLAM techniques that may help researchers investigate them. Full article
(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)
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20 pages, 7843 KiB  
Article
A Force-Feedback Methodology for Teleoperated Suturing Task in Robotic-Assisted Minimally Invasive Surgery
by Armin Ehrampoosh, Bijan Shirinzadeh, Joshua Pinskier, Julian Smith, Randall Moshinsky and Yongmin Zhong
Sensors 2022, 22(20), 7829; https://doi.org/10.3390/s22207829 - 14 Oct 2022
Cited by 11 | Viewed by 3022
Abstract
With robotic-assisted minimally invasive surgery (RAMIS), patients and surgeons benefit from a reduced incision size and dexterous instruments. However, current robotic surgery platforms lack haptic feedback, which is an essential element of safe operation. Moreover, teleportation control challenges make complex surgical tasks like [...] Read more.
With robotic-assisted minimally invasive surgery (RAMIS), patients and surgeons benefit from a reduced incision size and dexterous instruments. However, current robotic surgery platforms lack haptic feedback, which is an essential element of safe operation. Moreover, teleportation control challenges make complex surgical tasks like suturing more time-consuming than those that use manual tools. This paper presents a new force-sensing instrument that semi-automates the suturing task and facilitates teleoperated robotic manipulation. In order to generate the ideal needle insertion trajectory and pass the needle through its curvature, the end-effector mechanism has a rotating degree of freedom. Impedance control was used to provide sensory information about needle–tissue interaction forces to the operator using an indirect force estimation approach based on data-based models. The operator’s motion commands were then regulated using a hyperplanar virtual fixture (VF) designed to maintain the desired distance between the end-effector and tissue surface while avoiding unwanted contact. To construct the geometry of the VF, an optoelectronic sensor-based approach was developed. Based on the experimental investigation of the hyperplane VF methodology, improved needle–tissue interaction force, manipulation accuracy, and task completion times were demonstrated. Finally, experimental validation of the trained force estimation models and the perceived interaction forces by the user was conducted using online data, demonstrating the potential of the developed approach in improving task performance. Full article
(This article belongs to the Special Issue Robotics and Haptics: Haptic Feedback for Medical Robots)
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31 pages, 5249 KiB  
Review
A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors
by Wenbin Sun, Zilong Guo, Zhiqiang Yang, Yizhou Wu, Weixia Lan, Yingjie Liao, Xian Wu and Yuanyuan Liu
Sensors 2022, 22(20), 7784; https://doi.org/10.3390/s22207784 - 13 Oct 2022
Cited by 29 | Viewed by 6721
Abstract
In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical [...] Read more.
In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant. Full article
(This article belongs to the Section Wearables)
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