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13 pages, 10079 KiB

Open AccessArticle

Full-BAPose: Bottom Up Framework for Full Body Pose Estimation

by Bruno Artacho and Andreas Savakis

Sensors 2023, 23(7), 3725; https://doi.org/10.3390/s23073725 - 4 Apr 2023

Cited by 2 | Viewed by 2541

Abstract

We present Full-BAPose, a novel bottom-up approach for full body pose estimation that achieves state-of-the-art results without relying on external people detectors. The Full-BAPose method addresses the broader task of full body pose estimation including hands, feet, and facial landmarks. Our deep learning [...] Read more.

We present Full-BAPose, a novel bottom-up approach for full body pose estimation that achieves state-of-the-art results without relying on external people detectors. The Full-BAPose method addresses the broader task of full body pose estimation including hands, feet, and facial landmarks. Our deep learning architecture is end-to-end trainable based on an encoder-decoder configuration with HRNet backbone and multi-scale representations using a disentangled waterfall atrous spatial pooling module. The disentangled waterfall module leverages the efficiency of progressive filtering, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Additionally, it combines multi-scale features obtained from the waterfall flow with the person-detection capability of the disentangled adaptive regression and incorporates adaptive convolutions to infer keypoints more precisely in crowded scenes. Full-BAPose achieves state-of-the art performance on the challenging CrowdPose and COCO-WholeBody datasets, with AP of 72.2% and 68.4%, respectively, based on 133 keypoints. Our results demonstrate that Full-BAPose is efficient and robust when operating under a variety conditions, including multiple people, changes in scale, and occlusions. Full article

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

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14 pages, 8384 KiB

Open AccessArticle

Sequential Dual Coating with Thermosensitive Polymers for Advanced Fiber Optic Temperature Sensors

by Tejaswi Tanaji Salunkhe and Il Tae Kim

Sensors 2023, 23(6), 2898; https://doi.org/10.3390/s23062898 - 7 Mar 2023

Cited by 3 | Viewed by 1662

Abstract

We systematically designed dual polymer Fabry–Perrot interferometer (DPFPI) sensors, which were used to achieve highly sensitive temperature sensors. The designed and fabricated DPFPI has a dual polymer coating layer consisting of thermosensitive poly (methyl methacrylate) (PMMA) and polycarbonate (PC) polymers. Four different DPFPI [...] Read more.

We systematically designed dual polymer Fabry–Perrot interferometer (DPFPI) sensors, which were used to achieve highly sensitive temperature sensors. The designed and fabricated DPFPI has a dual polymer coating layer consisting of thermosensitive poly (methyl methacrylate) (PMMA) and polycarbonate (PC) polymers. Four different DPFPI sensors were developed, in which different coating optical path lengths and the resultant optical properties were generated by the Vernier effect, changing the sequence of the applied polymers and varying the concentration of the coating solutions. The experimental results confirmed that the PC_PMMA_S1 DPFPI sensor delivered a temperature sensitivity of 1238.7 pm °C⁻¹, which was approximately 4.4- and 1.4-fold higher than that of the PMMA and PMMA_PC_S1-coated sensor, respectively. Thus, the results reveal that the coating sequence, the compact thickness of the dual polymer layers, and the resultant optical parameters are accountable for achieving sensors with high sensitivity. In the PC_ PMMA-coated sensor, the PMMA outer layer has comparatively better optical properties than the PC, which might produce synergistic effects that create a large wavelength shift with small temperature deviations. Therefore, it is considered that the extensive results with the PC_PMMA_S1 DPFPI sensor validate the efficacy, repeatability, reliability, quick reaction, feasibility, and precision of the temperature readings. Full article

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

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16 pages, 770 KiB

Open AccessArticle

Self-Excited Microcantilever with Higher Mode Using Band-Pass Filter

by Yuji Hyodo and Hiroshi Yabuno

Sensors 2023, 23(5), 2849; https://doi.org/10.3390/s23052849 - 6 Mar 2023

Cited by 1 | Viewed by 1885

Abstract

Microresonators have a variety of scientific and industrial applications. The measurement methods based on the natural frequency shift of a resonator have been studied for a wide range of applications, including the detection of the microscopic mass and measurements of viscosity and stiffness. [...] Read more.

Microresonators have a variety of scientific and industrial applications. The measurement methods based on the natural frequency shift of a resonator have been studied for a wide range of applications, including the detection of the microscopic mass and measurements of viscosity and stiffness. A higher natural frequency of the resonator realizes an increase in the sensitivity and a higher-frequency response of the sensors. In the present study, by utilizing the resonance of a higher mode, we propose a method to produce the self-excited oscillation with a higher natural frequency without downsizing the resonator. We establish the feedback control signal for the self-excited oscillation using the band-pass filter so that the signal consists of only the frequency corresponding to the desired excitation mode. It results that careful position setting of the sensor for constructing a feedback signal, which is needed in the method based on the mode shape, is not necessary. By the theoretical analysis of the equations governing the dynamics of the resonator coupled with the band-pass filter, it is clarified that the self-excited oscillation is produced with the second mode. Furthermore, the validity of the proposed method is experimentally confirmed by an apparatus using a microcantilever. Full article

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

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13 pages, 3467 KiB

Open AccessArticle

Segmental Tissue Resistance of Healthy Young Adults during Four Hours of 6-Degree Head-Down-Tilt Positioning

by Todd J. Freeborn, Shelby Critcher and Gwendolyn Hooper

Sensors 2023, 23(5), 2793; https://doi.org/10.3390/s23052793 - 3 Mar 2023

Cited by 4 | Viewed by 1418

Abstract

(1) Background: One effect of microgravity on the human body is fluid redistribution due to the removal of the hydrostatic gravitational gradient. These fluid shifts are expected to be the source of severe medical risks and it is critical to advance methods to [...] Read more.

(1) Background: One effect of microgravity on the human body is fluid redistribution due to the removal of the hydrostatic gravitational gradient. These fluid shifts are expected to be the source of severe medical risks and it is critical to advance methods to monitor them in real-time. One technique to monitor fluid shifts captures the electrical impedance of segmental tissues, but limited research is available to evaluate if fluid shifts in response to microgravity are symmetrical due to the bilateral symmetry of the body. This study aims to evaluate this fluid shift symmetry. (2) Methods: Segmental tissue resistance at 10 kHz and 100 kHz was collected at 30 min intervals from the left/right arm, leg, and trunk of 12 healthy adults over 4 h of 6° head-down-tilt body positioning. (3) Results: Statistically significant increases were observed in the segmental leg resistances, first observed at 120 min and 90 min for 10 kHz and 100 kHz measurements, respectively. Median increases were approximately

11 %

to

12 %

for the 10 kHz resistance and

9 %

for the 100 kHz resistance. No statistically significant changes in the segmental arm or trunk resistance. Comparing the left and right segmental leg resistance, there were no statistically significant differences in the resistance changes based on the side of the body. (4) Conclusions: The fluid shifts induced by the 6° body position resulted in similar changes in both left and right body segments (that had statistically significant changes in this work). These findings support that future wearable systems to monitor microgravity-induced fluid shifts may only require monitoring of one side of body segments (reducing the hardware needed for the system). Full article

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

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20 pages, 665 KiB

Open AccessArticle

A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization

by Danilo Avola, Luigi Cinque, Gian Luca Foresti, Romeo Lanzino, Marco Raoul Marini, Alessio Mecca and Francesco Scarcello

Sensors 2023, 23(5), 2655; https://doi.org/10.3390/s23052655 - 28 Feb 2023

Cited by 4 | Viewed by 3254

Abstract

During flight, unmanned aerial vehicles (UAVs) need several sensors to follow a predefined path and reach a specific destination. To this aim, they generally exploit an inertial measurement unit (IMU) for pose estimation. Usually, in the UAV context, an IMU entails a three-axis [...] Read more.

During flight, unmanned aerial vehicles (UAVs) need several sensors to follow a predefined path and reach a specific destination. To this aim, they generally exploit an inertial measurement unit (IMU) for pose estimation. Usually, in the UAV context, an IMU entails a three-axis accelerometer and a three-axis gyroscope. However, as happens for many physical devices, they can present some misalignment between the real value and the registered one. These systematic or occasional errors can derive from different sources and could be related to the sensor itself or to external noise due to the place where it is located. Hardware calibration requires special equipment, which is not always available. In any case, even if possible, it can be used to solve the physical problem and sometimes requires removing the sensor from its location, which is not always feasible. At the same time, solving the problem of external noise usually requires software procedures. Moreover, as reported in the literature, even two IMUs from the same brand and the same production chain could produce different measurements under identical conditions. This paper proposes a soft calibration procedure to reduce the misalignment created by systematic errors and noise based on the grayscale or RGB camera built-in on the drone. Based on the transformer neural network architecture trained in a supervised learning fashion on pairs of short videos shot by the UAV’s camera and the correspondent UAV measurements, the strategy does not require any special equipment. It is easily reproducible and could be used to increase the trajectory accuracy of the UAV during the flight. Full article

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

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18 pages, 6202 KiB

Open AccessArticle

Photoplethysmography Signal Wavelet Enhancement and Novel Features Selection for Non-Invasive Cuff-Less Blood Pressure Monitoring

by Filippo Attivissimo, Luisa De Palma, Attilio Di Nisio, Marco Scarpetta and Anna Maria Lucia Lanzolla

Sensors 2023, 23(4), 2321; https://doi.org/10.3390/s23042321 - 19 Feb 2023

Cited by 15 | Viewed by 3046

Abstract

In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from [...] Read more.

In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from 1080 different patients. Three feature selection methods, namely Correlation-based Feature Selection (CFS), RReliefF and Minimum Redundancy Maximum Relevance (MRMR), have then been applied to identify the most significant features for BP estimation. Some of these features have been extracted through a novel PPG signal enhancement method based on the use of the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the enhanced signal leads to a reliable identification of the characteristic points of the PPG signal (e.g., systolic, diastolic and dicrotic notch points) by simple means, obtaining results comparable with those from purposely defined algorithms. For systolic points, mean and std of errors computed as the difference between the locations obtained using a purposely defined already known algorithm and those using the MODWT enhancement are, respectively, 0.0097 s and 0.0202 s; for diastolic points they are, respectively, 0.0441 s and 0.0486 s; for dicrotic notch points they are 0.0458 s and 0.0896 s. Hence, this study leads to the selection of several new features from the MODWT enhanced signal on every single pulse extracted from PPG signals, in addition to features already known in the literature. These features can be employed to train machine learning (ML) models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a non-invasive way, which is suitable for telemedicine health-care monitoring. Full article

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

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29 pages, 7007 KiB

Open AccessArticle

Trusted Operation of Cyber-Physical Processes Based on Assessment of the System’s State and Operating Mode

by Elena Basan, Alexandr Basan, Alexey Nekrasov, Colin Fidge, Evgeniya Ishchukova, Anatoly Basyuk and Alexandr Lesnikov

Sensors 2023, 23(4), 1996; https://doi.org/10.3390/s23041996 - 10 Feb 2023

Cited by 5 | Viewed by 1850

Abstract

We consider the trusted operation of cyber-physical processes based on an assessment of the system’s state and operating mode and present a method for detecting anomalies in the behavior of a cyber-physical system (CPS) based on the analysis of the data transmitted by [...] Read more.

We consider the trusted operation of cyber-physical processes based on an assessment of the system’s state and operating mode and present a method for detecting anomalies in the behavior of a cyber-physical system (CPS) based on the analysis of the data transmitted by its sensory subsystem. Probability theory and mathematical statistics are used to process and normalize the data in order to determine whether or not the system is in the correct operating mode and control process state. To describe the mode-specific control processes of a CPS, the paradigm of using cyber-physical parameters is taken as a basis, as it is the feature that most clearly reflects the system’s interaction with physical processes. In this study, two metrics were taken as a sign of an anomaly: the probability of falling into the sensor values’ confidence interval and parameter change monitoring. These two metrics, as well as the current mode evaluation, produce a final probability function for our trust in the CPS’s currently executing control process, which is, in turn, determined by the operating mode of the system. Based on the results of this trust assessment, it is possible to draw a conclusion about the processing state in which the system is operating. If the score is higher than 0.6, it means the system is in a trusted state. If the score is equal to 0.6, it means the system is in an uncertain state. If the trust score tends towards zero, then the system can be interpreted as unstable or under stress due to a system failure or deliberate attack. Through a case study using cyber-attack data for an unmanned aerial vehicle (UAV), it was found that the method works well. When we were evaluating the normal flight mode, there were no false positive anomaly estimates. When we were evaluating the UAV’s state during an attack, a deviation and an untrusted state were detected. This method can be used to implement software solutions aimed at detecting system faults and cyber-attacks, and thus make decisions about the presence of malfunctions in the operation of a CPS, thereby minimizing the amount of knowledge and initial data about the system. Full article

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

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12 pages, 5567 KiB

Open AccessArticle

Magnetoelastic Monitoring System for Tracking Growth of Human Mesenchymal Stromal Cells

by William S. Skinner, Sunny Zhang, Jasmine R. Garcia, Robert E. Guldberg and Keat Ghee Ong

Sensors 2023, 23(4), 1832; https://doi.org/10.3390/s23041832 - 7 Feb 2023

Cited by 3 | Viewed by 1866

Abstract

Magnetoelastic sensors, which undergo mechanical resonance when interrogated with magnetic fields, can be functionalized to measure various physical quantities and chemical/biological analytes by tracking their resonance behaviors. The unique wireless and functionalizable nature of these sensors makes them good candidates for biological sensing [...] Read more.

Magnetoelastic sensors, which undergo mechanical resonance when interrogated with magnetic fields, can be functionalized to measure various physical quantities and chemical/biological analytes by tracking their resonance behaviors. The unique wireless and functionalizable nature of these sensors makes them good candidates for biological sensing applications, from the detection of specific bacteria to tracking force loading inside the human body. In this study, we evaluate the viability of magnetoelastic sensors based on a commercially available magnetoelastic material (Metglas 2826 MB) for wirelessly monitoring the attachment and growth of human mesenchymal stromal cells (hMSCs) in 2D in vitro cell culture. The results indicate that the changes in sensor resonance are linearly correlated with cell quantity. Experiments using a custom-built monitoring system also demonstrated the ability of this technology to collect temporal profiles of cell growth, which could elucidate key stages of cell proliferation based on acute features in the profile. Additionally, there was no observed change in the morphology of cells after they were subjected to magnetic and mechanical stimuli from the monitoring system, indicating that this method for tracking cell growth may have minimal impact on cell quality and potency. Full article

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

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20 pages, 10671 KiB

Open AccessArticle

Novel Corrugated Long Period Grating Surface Balloon-Shaped Heterocore-Structured Plastic Optical Fibre Sensor for Microalgal Bioethanol Production

by Sanober Farheen Memon, Ruoning Wang, Bob Strunz, Bhawani Shankar Chowdhry, J. Tony Pembroke and Elfed Lewis

Sensors 2023, 23(3), 1644; https://doi.org/10.3390/s23031644 - 2 Feb 2023

Viewed by 1977

Abstract

A novel long period grating (LPG) inscribed balloon-shaped heterocore-structured plastic optical fibre (POF) sensor is described and experimentally demonstrated for real-time measurement of the ultra-low concentrations of ethanol in microalgal bioethanol production applications. The heterocore structure is established by coupling a 250 μm [...] Read more.

A novel long period grating (LPG) inscribed balloon-shaped heterocore-structured plastic optical fibre (POF) sensor is described and experimentally demonstrated for real-time measurement of the ultra-low concentrations of ethanol in microalgal bioethanol production applications. The heterocore structure is established by coupling a 250 μm core diameter POF between two 1000 μm diameter POFs, thus representing a large core—small core—large core configuration. Before coupling as a heterocore structure, the sensing region or small core fibre (SCF; i.e., 250 μm POF) is modified by polishing, LPG inscription, and macro bending into a balloon shape to enhance the sensitivity of the sensor. The sensor was characterized for ethanol–water solutions in the ethanol concentration ranges of 20 to 80 %v/v, 1 to 10 %v/v, 0.1 to 1 %v/v, and 0.00633 to 0.0633 %v/v demonstrating a maximum sensitivity of 3 × 10⁶ %/RIU, a resolution of 7.9 × 10⁻⁶ RIU, and a limit of detection (LOD) of 9.7 × 10⁻⁶ RIU. The experimental results are included for the intended application of bioethanol production using microalgae. The characterization was performed in the ultra-low-level ethanol concentration range, i.e., 0.00633 to 0.03165 %v/v, that is present in real culturing and production conditions, e.g., ethanol-producing blue-green microalgae mixtures. The sensor demonstrated a maximum sensitivity of 210,632.8 %T/%v/v (or 5 × 10⁶ %/RIU as referenced from the RI values of ethanol–water solutions), resolution of 2 × 10⁻⁴%v/v (or 9.4 × 10⁻⁶ RIU), and LOD of 4.9 × 10⁻⁴%v/v (or 2.3 × 10⁻⁵ RIU). Additionally, the response and recovery times of the sensor were investigated in the case of measurement in the air and the ethanol-microalgae mixtures. The experimentally verified, extremely high sensitivity and resolution and very low LOD corresponding to the initial rate of bioethanol production using microalgae of this sensor design, combined with ease of fabrication, low cost, and wide measurement range, makes it a promising candidate to be incorporated into the bioethanol production industry as a real-time sensing solution as well as in other ethanol sensing and/or RI sensing applications. Full article

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

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16 pages, 5672 KiB

Open AccessArticle

Competition between Direct Detection Mechanisms in Planar Bow-Tie Microwave Diodes on the Base of InAlAs/InGaAs/InAlAs Heterostructures

by Algirdas Sužiedėlis, Steponas Ašmontas, Jonas Gradauskas, Aurimas Čerškus, Karolis Požela and Maksimas Anbinderis

Sensors 2023, 23(3), 1441; https://doi.org/10.3390/s23031441 - 28 Jan 2023

Cited by 1 | Viewed by 1490

Abstract

The application of the unique properties of terahertz radiation is increasingly needed in sensors, especially in those operating at room temperature without an external bias voltage. Bow-tie microwave diodes on the base of InGaAs semiconductor structures meet these requirements. These diodes operate on [...] Read more.

The application of the unique properties of terahertz radiation is increasingly needed in sensors, especially in those operating at room temperature without an external bias voltage. Bow-tie microwave diodes on the base of InGaAs semiconductor structures meet these requirements. These diodes operate on the basis of free-carrier heating in microwave electric fields, which allows for the use of such sensors in millimeter- and submillimeter-wavelength ranges. However, there still exists some uncertainty concerning the origin of the voltage detected across these diodes. This work provides a more detailed analysis of the detection mechanisms in InAlAs/InGaAs selectively doped bow-tie-shaped semiconductor structures. The influence of the InAs inserts in the InGaAs layer is investigated under various illumination and temperature conditions. A study of the voltage–power characteristics, the voltage sensitivity dependence on frequency in the K_a range, temperature dependence of the detected voltage and its relaxation characteristics lead to the conclusion that a photo-gradient electromotive force arises in bow-tie diodes under simultaneous light illumination and microwave radiation. Full article

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

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21 pages, 6113 KiB

Open AccessArticle

Enhancing System Performance through Objective Feature Scoring of Multiple Persons’ Breathing Using Non-Contact RF Approach

by Mubashir Rehman, Raza Ali Shah, Najah Abed Abu Ali, Muhammad Bilal Khan, Syed Aziz Shah, Akram Alomainy, Mohammad Hayajneh, Xiaodong Yang, Muhammad Ali Imran and Qammer H. Abbasi

Sensors 2023, 23(3), 1251; https://doi.org/10.3390/s23031251 - 21 Jan 2023

Cited by 7 | Viewed by 2414

Abstract

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates [...] Read more.

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system’s performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system’s performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively. Full article

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

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11 pages, 4552 KiB

Open AccessArticle

Packaging and Optimization of a Capacitive Biosensor and Its Readout Circuit

by Antonios Georgas, Lampros Nestoras, Aris Ioannis Kanaris, Spyridon Angelopoulos, Angelo Ferraro and Evangelos Hristoforou

Sensors 2023, 23(2), 765; https://doi.org/10.3390/s23020765 - 9 Jan 2023

Viewed by 1736

Abstract

In pipeline production, there is a considerable distance between the moment when the operation principle of a biosensor will be verified in the laboratory until the moment when it can be used in real conditions. This distance is often covered by an optimization [...] Read more.

In pipeline production, there is a considerable distance between the moment when the operation principle of a biosensor will be verified in the laboratory until the moment when it can be used in real conditions. This distance is often covered by an optimization and packaging process. This article described the packaging and optimization of a SARS-CoV-2 biosensor, as well as the packaging of its electronic readout circuit. The biosensor was packed with a photosensitive tape, which forms a protective layer and is patterned in a way to form a well in the sensing area. The well is meant to limit the liquid diffusion, thereby reducing the measurement error. Subsequently, a connector between the biosensor and its readout circuit was designed and 3D-printed, ensuring the continuous and easy reading of the biosensor. In the last step, a three-dimensional case was designed and printed, thus protecting the circuit from any damage, and allowing its operation in real conditions. Full article

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

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12 pages, 1501 KiB

Open AccessArticle

Backward Acoustic Waves in Piezoelectric Plates: Possible Application as Base for Liquid Sensors

by Andrey Smirnov, Boris Zaitsev, Ilya Nedospasov, Gleb Nazarov and Iren Kuznetsova

Sensors 2023, 23(2), 648; https://doi.org/10.3390/s23020648 - 6 Jan 2023

Cited by 3 | Viewed by 1761

Abstract

Backward acoustic waves are characterized by oppositely directed phase and group velocities. These waves can exist in isotropic and piezoelectric plates. They can be detected using a set of interdigital transducers with different spatial periods located on the same piezoelectric substrate. In this [...] Read more.

Backward acoustic waves are characterized by oppositely directed phase and group velocities. These waves can exist in isotropic and piezoelectric plates. They can be detected using a set of interdigital transducers with different spatial periods located on the same piezoelectric substrate. In this paper, the effect of a nonviscous and nonconductive liquid on the characteristics of a first-order backward antisymmetric wave in a YX plate of lithium niobate is studied theoretically and experimentally. It is shown that the presence of liquid does not lead to the transformation or disappearance of this wave. It is shown that these waves are close to the cutoff frequency and are characterized by the presence of a point with zero group velocity. The design of a liquid sensor based on these waves is proposed. Full article

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

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11 pages, 3397 KiB

Open AccessEditor’s ChoiceCommunication

Evaluation of a New Real-Time Dosimeter Sensor for Interventional Radiology Staff

by Kenshin Hattori, Yohei Inaba, Toshiki Kato, Masaki Fujisawa, Hikaru Yasuno, Ayumi Yamada, Yoshihiro Haga, Masatoshi Suzuki, Masayuki Zuguchi and Koichi Chida

Sensors 2023, 23(1), 512; https://doi.org/10.3390/s23010512 - 3 Jan 2023

Cited by 14 | Viewed by 2962

Abstract

In 2011, the International Commission on Radiological Protection (ICRP) recommended a significant reduction in the lens-equivalent radiation dose limit, thus from an average of 150 to 20 mSv/year over 5 years. In recent years, the occupational dose has been rising with the increased [...] Read more.

In 2011, the International Commission on Radiological Protection (ICRP) recommended a significant reduction in the lens-equivalent radiation dose limit, thus from an average of 150 to 20 mSv/year over 5 years. In recent years, the occupational dose has been rising with the increased sophistication of interventional radiology (IVR); management of IVR staff radiation doses has become more important, making real-time radiation monitoring of such staff desirable. Recently, the i3 real-time occupational exposure monitoring system (based on RaySafe^TM) has replaced the conventional i2 system. Here, we compared the i2 and i3 systems in terms of sensitivity (batch uniformity), tube-voltage dependency, dose linearity, dose-rate dependency, and angle dependency. The sensitivity difference (batch uniformity) was approximately 5%, and the tube-voltage dependency was <±20% between 50 and 110 kV. Dose linearity was good (R² = 1.00); a slight dose-rate dependency (~20%) was evident at very high dose rates (250 mGy/h). The i3 dosimeter showed better performance for the lower radiation detection limit compared with the i2 system. The horizontal and vertical angle dependencies of i3 were superior to those of i2. Thus, i3 sensitivity was higher over a wider angle range compared with i2, aiding the measurement of scattered radiation. Unlike the i2 sensor, the influence of backscattered radiation (i.e., radiation from an angle of 180°) was negligible. Therefore, the i3 system may be more appropriate in areas affected by backscatter. In the future, i3 will facilitate real-time dosimetry and dose management during IVR and other applications. Full article

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

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29 pages, 8995 KiB

Open AccessArticle

Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals

by Vicente Biot-Monterde, Angela Navarro-Navarro, Israel Zamudio-Ramirez, Jose A. Antonino-Daviu and Roque A. Osornio-Rios

Sensors 2023, 23(1), 316; https://doi.org/10.3390/s23010316 - 28 Dec 2022

Cited by 11 | Viewed by 1988

Abstract

Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is [...] Read more.

Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is mainly related to the high currents flowing along those bars during start-up. In order to reduce the stresses that could lead to the appearance of these faults, the use of soft starters is becoming usual. However, these devices introduce additional components in the current and flux signals, affecting the evolution of the fault-related patterns and so making the fault diagnosis process more difficult. This paper proposes a new method to automatically classify the rotor health state in IMs driven by soft starters. The proposed method relies on obtaining the Persistence Spectrum (PS) of the start-up stray-flux signals. To obtain a proper dataset, Data Augmentation Techniques (DAT) are applied, adding Gaussian noise to the original signals. Then, these PS images are used to train a Convolutional Neural Network (CNN), in order to automatically classify the rotor health state, depending on the severity of the fault, namely: healthy motor, one broken bar and two broken bars. This method has been validated by means of a test bench consisting of a 1.1 kW IM driven by four different soft starters coupled to a DC motor. The results confirm the reliability of the proposed method, obtaining a classification rate of 100.00% when analyzing each model separately and 99.89% when all the models are analyzed at a time. Full article

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

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11 pages, 4666 KiB

Open AccessArticle

Compensation of Thermal Gradients Effects on a Quartz Crystal Microbalance

by Marianna Magni, Diego Scaccabarozzi and Bortolino Saggin

Sensors 2023, 23(1), 24; https://doi.org/10.3390/s23010024 - 20 Dec 2022

Cited by 2 | Viewed by 1735

Abstract

Quartz Crystal Microbalances (QCM) are widely used instruments thanks to their stability, low mass, and low cost. Nevertheless, the sensitivity to temperature is their main drawback and is often a driver for their design. Though the crystal average temperature is mostly considered as [...] Read more.

Quartz Crystal Microbalances (QCM) are widely used instruments thanks to their stability, low mass, and low cost. Nevertheless, the sensitivity to temperature is their main drawback and is often a driver for their design. Though the crystal average temperature is mostly considered as the only disturbance, temperature affects the QCM measurements also through the in-plane temperature gradients, an effect identified in the past but mostly neglected. Recently, it has been shown that this effect can prevail over that of the average temperature in implementations where the heat for thermal control is released directly on the crystal through deposited film heaters. In this study, the effect of temperature gradients for this kind of crystal is analyzed, the sensitivity of frequency to the average temperature gradient on the electrode border is determined, and a correction is proposed and verified. A numerical thermal model of the QCM has been created to determine the temperature gradients on the electrode borders. The frequency versus temperature-gradient function has been experimentally determined in different thermal conditions. The correction function has been eventually applied to a QCM implementing a crystal of the same manufacturing lot as the one used for the characterization. The residual errors after the implementation of the correction of both average temperature and temperature gradients were always lower than 5% of the initial temperature disturbance. Moreover, using the correlation between the heater power dissipation and the generated temperature gradients, it has been shown that an effective correction strategy can be based on the measurement of the power delivered to the crystal without the determination of the temperature gradient. Full article

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

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21 pages, 3426 KiB

Open AccessArticle

Experimental Investigation and CFD Analysis of Pressure Drop in an ORC Boiler for a WHRS Implementation

by Concepción Paz, Eduardo Suárez, Adrián Cabarcos and Antonio Díaz

Sensors 2022, 22(23), 9437; https://doi.org/10.3390/s22239437 - 2 Dec 2022

Cited by 1 | Viewed by 2370

Abstract

Waste heat dissipated in the exhaust system of a combustion engine represents a major source of energy to be recovered and converted into useful work. The Waste Heat Recovery System (WHRS) based in an Organic Rankine Cycle (ORC) is an approach for recovering [...] Read more.

Waste heat dissipated in the exhaust system of a combustion engine represents a major source of energy to be recovered and converted into useful work. The Waste Heat Recovery System (WHRS) based in an Organic Rankine Cycle (ORC) is an approach for recovering energy from heat sources, achieving a significant reduction in fuel consumption and, as a result, exhaust emissions. This paper studies pressure drop in an ORC shell-and-tubes boiler for a WHRS implementation experimentally and with computational simulations based on a 1-dimensional heat transfer model coupled with 3D calculations. An experimental database is developed, using ethanol in a pressure range of 10–15 absolute bar as working fluid, with mass fluxes inside the tubes in the range of 349.31 kg/s-m² and 523.97 kg/s-m², and inlet temperatures in the range of 60 °C and 80 °C. Thus, the friction factor of different regions of the boiler were estimated using both CFD simulations, experimental data, and bibliographic correlations. Simulations of operating points and the results of the experimental test bench showed good agreement in pressure drop results, with a mean absolute error of 15.47%, without a significant increment in the computational cost. Full article

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

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11 pages, 5341 KiB

Open AccessArticle

Mueller Matrix Microscopy for In Vivo Scar Tissue Diagnostics and Treatment Evaluation

by Lennart Jütte and Bernhard Roth

Sensors 2022, 22(23), 9349; https://doi.org/10.3390/s22239349 - 1 Dec 2022

Cited by 6 | Viewed by 1973

Abstract

Scars usually do not show strong contrast under standard skin examination relying on dermoscopes. They usually develop after skin injury when the body repairs the damaged tissue. In general, scars cause multiple types of distress such as movement restrictions, pain, itchiness and the [...] Read more.

Scars usually do not show strong contrast under standard skin examination relying on dermoscopes. They usually develop after skin injury when the body repairs the damaged tissue. In general, scars cause multiple types of distress such as movement restrictions, pain, itchiness and the psychological impact of the associated cosmetic disfigurement with no universally successful treatment option available at the moment. Scar treatment has significant economic impact as well. Mueller matrix polarimetry with integrated autofocus and automatic data registration can potentially improve scar assessment by the dermatologist and help to make the evaluation of the treatment outcome objective. Polarimetry can provide new physical parameters for an objective treatment evaluation. We show that Mueller matrix polarimetry can enable strong contrast for in vivo scar imaging. Additionally, our results indicate that the polarization stain images obtained form there could be a useful tool for dermatology. Furthermore, we demonstrate that polarimetry can be used to monitor wound healing, which may help prevent scarring altogether. Full article

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

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13 pages, 447 KiB

Open AccessArticle

Leveraging Self-Attention Mechanism for Attitude Estimation in Smartphones

by James Brotchie, Wei Shao, Wenchao Li and Allison Kealy

Sensors 2022, 22(22), 9011; https://doi.org/10.3390/s22229011 - 21 Nov 2022

Cited by 3 | Viewed by 2389

Abstract

Inertial attitude estimation is a crucial component of many modern systems and applications. Attitude estimation from commercial-grade inertial sensors has been the subject of an abundance of research in recent years due to the proliferation of Inertial Measurement Units (IMUs) in mobile devices, [...] Read more.

Inertial attitude estimation is a crucial component of many modern systems and applications. Attitude estimation from commercial-grade inertial sensors has been the subject of an abundance of research in recent years due to the proliferation of Inertial Measurement Units (IMUs) in mobile devices, such as the smartphone. Traditional methodologies involve probabilistic, iterative-state estimation; however, these approaches do not generalise well over changing motion dynamics and environmental conditions, as they require context-specific parameter tuning. In this work, we explore novel methods for attitude estimation from low-cost inertial sensors using a self-attention-based neural network, the Attformer. This paper proposes to part ways from the traditional cycle of continuous integration algorithms, and formulate it as an optimisation problem. This approach separates itself by leveraging attention operations to learn the complex patterns and dynamics associated with inertial data, allowing for the linear complexity in the dimension of the feature vector to account for these patterns. Additionally, we look at combining traditional state-of-the-art approaches with our self-attention method. These models were evaluated on entirely unseen sequences, over a range of different activities, users and devices, and compared with a recent alternate deep learning approach, the unscented Kalman filter and the iOS CoreMotion API. The inbuilt iOS had a mean angular distance from the true attitude of

117 . 31^{\circ}

, the GRU

21 . 90^{\circ}

, the UKF

16 . 38^{\circ}

, the Attformer

16 . 28^{\circ}

and, finally, the UKF–Attformer had mean angular distance of

10 . 86^{\circ}

. We show that this plug-and-play solution outperforms previous approaches and generalises well across different users, devices and activities. Full article

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

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19 pages, 3255 KiB

Open AccessArticle

COVID-19 Contagion Risk Estimation Model for Indoor Environments

by Sandra Costanzo and Alexandra Flores

Sensors 2022, 22(19), 7668; https://doi.org/10.3390/s22197668 - 9 Oct 2022

Cited by 4 | Viewed by 2490

Abstract

COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources [...] Read more.

COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources of infection, mainly due to poor ventilation, making it necessary to take measures to improve indoor air quality. In this work, an accurate model for COVID-19 contagion risk estimation based on the Wells–Riley probabilistic approach for indoor environments is proposed and implemented as an Android mobile App. The implemented algorithm takes into account all relevant parameters, such as environmental conditions, age, kind of activities, and ventilation conditions, influencing the risk of contagion to provide the real-time probability of contagion with respect to the permanence time, the maximum allowed number of people for the specified area, the expected number of COVID-19 cases, and the required number of Air Changes per Hour. Alerts are provided to the user in the case of a high probability of contagion and CO₂ concentration. Additionally, the app exploits a Bluetooth signal to estimate the distance to other devices, allowing the regulation of social distance between people. The results from the application of the model are provided and discussed for different scenarios, such as offices, restaurants, classrooms, and libraries, thus proving the effectiveness of the proposed tool, helping to reduce the spread of the virus still affecting the world population. Full article

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

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18 pages, 5997 KiB

Open AccessArticle

Design and Implementation of a Smart Insole System to Measure Plantar Pressure and Temperature

by Amith Khandakar, Sakib Mahmud, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Serkan Kiranyaz, Zaid Bin Mahbub, Sawal Hamid Md Ali, Ahmad Ashrif A. Bakar, Mohamed Arselene Ayari, Mohammed Alhatou, Mohammed Abdul-Moniem and Md Ahasan Atick Faisal

Sensors 2022, 22(19), 7599; https://doi.org/10.3390/s22197599 - 7 Oct 2022

Cited by 29 | Viewed by 8573

Abstract

An intelligent insole system may monitor the individual’s foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from [...] Read more.

An intelligent insole system may monitor the individual’s foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors. The design provides details of specific temperature and pressure sensors, circuit configuration for characterizing the sensors, and design considerations for creating a small system with suitable electronics. The procedure also details how, using a low-power communication protocol, data about the individuals’ foot pressure and temperatures may be sent wirelessly to a centralized device for storage. This research may aid in the creation of an affordable, practical, and portable foot monitoring system for patients. The solution can be used for continuous, at-home monitoring of foot problems through pressure patterns and temperature differences between the two feet. The generated maps can be used for early detection of diabetic foot complication with the help of artificial intelligence. Full article

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

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16 pages, 3923 KiB

Open AccessArticle

Strategies to Enhance the Data Density in Synchronous Electromagnetic Encoders

by Ferran Paredes, Amirhossein Karami-Horestani and Ferran Martín

Sensors 2022, 22(12), 4356; https://doi.org/10.3390/s22124356 - 8 Jun 2022

Cited by 5 | Viewed by 1753

Abstract

In this paper, we report two different strategies to enhance the data density in electromagnetic encoders with synchronous reading. One approach uses a periodic chain of rectangular metallic patches (clock chain) that determines the encoder velocity, and dictates the instants of time for [...] Read more.

In this paper, we report two different strategies to enhance the data density in electromagnetic encoders with synchronous reading. One approach uses a periodic chain of rectangular metallic patches (clock chain) that determines the encoder velocity, and dictates the instants of time for retrieving the bits of the identification (ID) code. However, contrary to previous electromagnetic encoders, the ID is inferred at both the rising and the falling edges of the clock signal generated by the clock chain. Moreover, the bits of information are not given by the presence or absence of metallic patches at their predefined positions in the so-called ID code chain. With this novel encoding system, a bit state corresponding to a certain instant of time is identical to the previous bit state, unless there is a change in the envelope function of the ID code signal, determined by the additional non-periodic ID code chain. The other encoding strategy utilizes a single chain of C-shaped resonators, and encoding is achieved by considering four different resonator dimensions, corresponding to four states and, hence, to two bits per resonator of the chain. Thus, with these two strategies, the data density is twice the one achievable in previously reported synchronous electromagnetic encoders. Full article

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

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14 pages, 7999 KiB

Open AccessArticle

FOODCAM: A Novel Structured Light-Stereo Imaging System for Food Portion Size Estimation

by Viprav B. Raju and Edward Sazonov

Sensors 2022, 22(9), 3300; https://doi.org/10.3390/s22093300 - 26 Apr 2022

Cited by 7 | Viewed by 2802

Abstract

Imaging-based methods of food portion size estimation (FPSE) promise higher accuracies compared to traditional methods. Many FPSE methods require dimensional cues (fiducial markers, finger-references, object-references) in the scene of interest and/or manual human input (wireframes, virtual models). This paper proposes a novel passive, [...] Read more.

Imaging-based methods of food portion size estimation (FPSE) promise higher accuracies compared to traditional methods. Many FPSE methods require dimensional cues (fiducial markers, finger-references, object-references) in the scene of interest and/or manual human input (wireframes, virtual models). This paper proposes a novel passive, standalone, multispectral, motion-activated, structured light-supplemented, stereo camera for food intake monitoring (FOODCAM) and an associated methodology for FPSE that does not need a dimensional reference given a fixed setup. The proposed device integrated a switchable band (visible/infrared) stereo camera with a structured light emitter. The volume estimation methodology focused on the 3-D reconstruction of food items based on the stereo image pairs captured by the device. The FOODCAM device and the methodology were validated using five food models with complex shapes (banana, brownie, chickpeas, French fries, and popcorn). Results showed that the FOODCAM was able to estimate food portion sizes with an average accuracy of 94.4%, which suggests that the FOODCAM can potentially be used as an instrument in diet and eating behavior studies. Full article

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

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Review

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25 pages, 6261 KiB

Open AccessReview

Reducing the Impact of Influence Factors on the Measurement Results from Single-Coil Eddy Current Sensors

by Sergey Borovik, Marina Kuteynikova and Yuriy Sekisov

Sensors 2023, 23(1), 351; https://doi.org/10.3390/s23010351 - 29 Dec 2022

Cited by 4 | Viewed by 2160

Abstract

Single-coil eddy current sensors (SCECS) form a separate and independent branch among the existing eddy current probes. Such sensors are often used for aviation and aerospace applications where the conditions accompanying the measuring process are harsh and even extreme. High temperatures (up to [...] Read more.

Single-coil eddy current sensors (SCECS) form a separate and independent branch among the existing eddy current probes. Such sensors are often used for aviation and aerospace applications where the conditions accompanying the measuring process are harsh and even extreme. High temperatures (up to +600 °C in the compressor and over +1000 °C in the turbine of gas turbine engines), the complex shape surfaces of the monitored parts, the multidimensional movement of the power plants’ structural elements, restrictions on the probes number and their placement in the measuring zone are the main factors affecting the reliability and accuracy of the measurement results obtained by the sensors. The article provides an overview of the relevant approaches and methods for reducing the impact of influence factors on the measurement results from SCECS based on the extensive experience of more than 30 years of research and development being carried out in the Institute for the Control of Complex Systems of Russian Academy of Sciences. The scope of the solutions discussed in the article is not limited to SCECS measurement systems only but can also be extended to the systems with primary transducers of other designs or other physical principles. Full article

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

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17 pages, 3238 KiB

Open AccessReview

Wearable Sensors for Learning Enhancement in Higher Education

by Sara Khosravi, Stuart G. Bailey, Hadi Parvizi and Rami Ghannam

Sensors 2022, 22(19), 7633; https://doi.org/10.3390/s22197633 - 8 Oct 2022

Cited by 17 | Viewed by 5505

Abstract

Wearable sensors have traditionally been used to measure and monitor vital human signs for well-being and healthcare applications. However, there is a growing interest in using and deploying these technologies to facilitate teaching and learning, particularly in a higher education environment. The aim [...] Read more.

Wearable sensors have traditionally been used to measure and monitor vital human signs for well-being and healthcare applications. However, there is a growing interest in using and deploying these technologies to facilitate teaching and learning, particularly in a higher education environment. The aim of this paper is therefore to systematically review the range of wearable devices that have been used for enhancing the teaching and delivery of engineering curricula in higher education. Moreover, we compare the advantages and disadvantages of these devices according to the location in which they are worn on the human body. According to our survey, wearable devices for enhanced learning have mainly been worn on the head (e.g., eyeglasses), wrist (e.g., watches) and chest (e.g., electrocardiogram patch). In fact, among those locations, head-worn devices enable better student engagement with the learning materials, improved student attention as well as higher spatial and visual awareness. We identify the research questions and discuss the research inclusion and exclusion criteria to present the challenges faced by researchers in implementing learning technologies for enhanced engineering education. Furthermore, we provide recommendations on using wearable devices to improve the teaching and learning of engineering courses in higher education. Full article

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

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19 pages, 2810 KiB

Open AccessReview

Application of Image Sensors to Detect and Locate Electrical Discharges: A Review

by Jordi-Roger Riba

Sensors 2022, 22(15), 5886; https://doi.org/10.3390/s22155886 - 6 Aug 2022

Cited by 21 | Viewed by 4496

Abstract

Today, there are many attempts to introduce the Internet of Things (IoT) in high-voltage systems, where partial discharges are a focus of concern since they degrade the insulation. The idea is to detect such discharges at a very early stage so that corrective [...] Read more.

Today, there are many attempts to introduce the Internet of Things (IoT) in high-voltage systems, where partial discharges are a focus of concern since they degrade the insulation. The idea is to detect such discharges at a very early stage so that corrective actions can be taken before major damage is produced. Electronic image sensors are traditionally based on charge-coupled devices (CCDs) and, next, on complementary metal oxide semiconductor (CMOS) devices. This paper performs a review and analysis of state-of-the-art image sensors for detecting, locating, and quantifying partial discharges in insulation systems and, in particular, corona discharges since it is an area with an important potential for expansion due to the important consequences of discharges and the complexity of their detection. The paper also discusses the recent progress, as well as the research needs and the challenges to be faced, in applying image sensors in this area. Although many of the cited research works focused on high-voltage applications, partial discharges can also occur in medium- and low-voltage applications. Thus, the potential applications that could potentially benefit from the introduction of image sensors to detect electrical discharges include power substations, buried power cables, overhead power lines, and automotive applications, among others. Full article

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

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