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Women’s Special Issue Series: Sensors

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 24886

Special Issue Editors

Dr. Maria Leilani Torres

E-Mail Website
Guest Editor
Faculty of Mathematics and Physics, Gottfried Wilhelm Leibniz University Hannover, Hanover, Germany
Interests: biophotonics; optical manipulation; lab-on-a-chip devices; point-of-care sensors; flexible sensors
Dr. Areti Mourka

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece
Interests: novel ultrafast laser micro/nano processing techniques; optoelectronics engineering; laser methods for biomolecules patterning and materials processing; light–matter interaction; 3D printing; laser beam shaping; adaptive optics
Dr. Anna Chiara De Luca

E-Mail Website
Guest Editor
Institute of Endocrinology and Experimental Oncology “G. Salvatore” - National Research Council of Italy, Via Pietro Castellino n.111, 80131 Napoli, Italy
Interests: biophotonics; Raman spectroscopy; SERS; imaging; plasmonics; optical biosensors; nanotechnology; nanophotonics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To celebrate and highlight the achievements of women in the sensors research area, this Special Issue, entitled "Women’s Special Issue Series: Sensors", will present the sensors-related work from leading female scientists. We also hope that this Special Issue will further encourage and promote the scientific contributions of female researchers in this field.

A “Women in Sensors Award” will be launched and granted to the best paper published in this Special Issue. Each award nominee will be assessed on her paper’s originality, quality, and contribution to the field by the Evaluation Committee. The winner will receive a certificate, an award of 1000 CHF, and an opportunity to publish her next submission in Sensors free of charge.

The topic of this Special Issue include, but are not limited to:

  • 3D sensing
  • Wearable sensors, devices and electronics
  • Lab-on-a-chip
  • Sensor devices, technology and applications
  • Advanced materials for sensing
  • Photonic sensor
  • Nanophotonics

We welcome submissions from all authors, irrespective of gender.

Dr. Maria Leilani Torres
Dr. Areti Mourka
Dr. Anna Chiara De Luca
Guest Editors

Women’s Special Issue Series

This Special Issue is part of Sensors's Women’s Special Issue Series, hosted by women editors for women researchers. The Series advocates the advancement of women in science. We invite contributions to the Special Issue whose lead authors identify as women. The submission of articles with all-women authorship is especially encouraged. However, we do welcome articles from all authors, irrespective of gender.

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • laser scanning
  • structured light
  • reference measurements
  • natural head position
  • laser beam computer topography
  • medical image registration
  • kinect
  • reliability
  • inertial sensor
  • meta-analysis
  • flexible sensors
  • force measurements
  • spectroscopy
  • plasmonic sensing
  • point-of-care sensors, flexible sensors, SERS sensing, nanophotonics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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13 pages, 7639 KiB  
Article
Home-Based Electrochemical Rapid Sensor (HERS): A Diagnostic Tool for Bacterial Vaginosis
by Melissa Banks, Farbod Amirghasemi, Evelyn Mitchell and Maral P. S. Mousavi
Sensors 2023, 23(4), 1891; https://doi.org/10.3390/s23041891 - 8 Feb 2023
Cited by 8 | Viewed by 5476
Abstract
Bacterial vaginosis (BV) is the most frequently occurring vaginal infection worldwide, yet it remains significantly underdiagnosed as a majority of patients are asymptomatic. Untreated BV poses a serious threat as it increases one’s risk of STI acquisition, pregnancy complications, and infertility. We aim [...] Read more.
Bacterial vaginosis (BV) is the most frequently occurring vaginal infection worldwide, yet it remains significantly underdiagnosed as a majority of patients are asymptomatic. Untreated BV poses a serious threat as it increases one’s risk of STI acquisition, pregnancy complications, and infertility. We aim to minimize these risks by creating a low-cost disposable sensor for at-home BV diagnosis. A clinical diagnosis of BV is most commonly made according to the Amsel criteria. In this method, a fish-like odor, caused by increased levels of trimethylamine (TMA) in vaginal fluid, is used as a key diagnostic. This paper outlines the development of a Home-Based Electrochemical Rapid Sensor (HERS), capable of detecting TMA in simulated vaginal fluid (sVF). Instead of odor-based detection of volatilized TMA, we identify TMA in trimethylammonium form by utilizing HERS and a potentiometric readout. We fabricated the ion selective electrode using a carbon-black-coated cotton string and a TMA-selective membrane consisting of calix[4]arene and sodium tetrakis[3,5-bis(trifluoromethyl)phenyl]borate. When paired with a standard reference electrode, our device was able to quantify TMA concentration in deionized (DI) water, as well as sVF samples at multiple pH levels with a clinically relevant limit of detection (8.66 µM, and theoretically expected Nernstian slope of 55.14 mV/decade). Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
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16 pages, 5361 KiB  
Article
PA-Tran: Learning to Estimate 3D Hand Pose with Partial Annotation
by Tianze Yu, Luke Bidulka, Martin J. McKeown and Z. Jane Wang
Sensors 2023, 23(3), 1555; https://doi.org/10.3390/s23031555 - 31 Jan 2023
Cited by 2 | Viewed by 2471
Abstract
This paper tackles a novel and challenging problem—3D hand pose estimation (HPE) from a single RGB image using partial annotation. Most HPE methods ignore the fact that the keypoints could be partially visible (e.g., under occlusions). In contrast, we propose a deep-learning framework, [...] Read more.
This paper tackles a novel and challenging problem—3D hand pose estimation (HPE) from a single RGB image using partial annotation. Most HPE methods ignore the fact that the keypoints could be partially visible (e.g., under occlusions). In contrast, we propose a deep-learning framework, PA-Tran, that jointly estimates the keypoints status and 3D hand pose from a single RGB image with two dependent branches. The regression branch consists of a Transformer encoder which is trained to predict a set of target keypoints, given an input set of status, position, and visual features embedding from a convolutional neural network (CNN); the classification branch adopts a CNN for estimating the keypoints status. One key idea of PA-Tran is a selective mask training (SMT) objective that uses a binary encoding scheme to represent the status of the keypoints as observed or unobserved during training. In addition, by explicitly encoding the label status (observed/unobserved), the proposed PA-Tran can efficiently handle the condition when only partial annotation is available. Investigating the annotation percentage ranging from 50–100%, we show that training with partial annotation is more efficient (e.g., achieving the best 6.0 PA-MPJPE when using about 85% annotations). Moreover, we provide two new datasets. APDM-Hand, is for synthetic hands with APDM sensor accessories, which is designed for a specific hand task. PD-APDM-Hand, is a real hand dataset collected from Parkinson’s Disease (PD) patients with partial annotation. The proposed PA-Tran can achieve higher estimation accuracy when evaluated on both proposed datasets and a more general hand dataset. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
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14 pages, 3386 KiB  
Article
Ultraviolet-C Photoresponsivity Using Fabricated TiO2 Thin Films and Transimpedance-Amplifier-Based Test Setup
by Marilou Cadatal-Raduban, Jade Pope, Jiří Olejníček, Michal Kohout, John A. Harrison and S. M. Rezaul Hasan
Sensors 2022, 22(21), 8176; https://doi.org/10.3390/s22218176 - 25 Oct 2022
Cited by 5 | Viewed by 1577
Abstract
We report on fabricated titanium dioxide (TiO2) thin films along with a transimpedance amplifier (TIA) test setup as a photoconductivity detector (sensor) in the ultraviolet-C (UV-C) wavelength region, particularly at 260 nm. TiO2 thin films deposited on high-resistivity undoped silicon-substrate [...] Read more.
We report on fabricated titanium dioxide (TiO2) thin films along with a transimpedance amplifier (TIA) test setup as a photoconductivity detector (sensor) in the ultraviolet-C (UV-C) wavelength region, particularly at 260 nm. TiO2 thin films deposited on high-resistivity undoped silicon-substrate at thicknesses of 100, 500, and 1000 nm exhibited photoresponsivities of 81.6, 55.6, and 19.6 mA/W, respectively, at 30 V bias voltage. Despite improvements in the crystallinity of the thicker films, the decrease in photocurrent, photoconductivity, photoconductance, and photoresponsivity in thicker films is attributed to an increased number of defects. Varying the thickness of the film can, however, be leveraged to control the wavelength response of the detector. Future development of a chip-based portable UV-C detector using TiO2 thin films will open new opportunities for a wide range of applications. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
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16 pages, 4522 KiB  
Article
A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving
by Georgia A. Tuckwell, James A. Keal, Charlotte C. Gupta, Sally A. Ferguson, Jarrad D. Kowlessar and Grace E. Vincent
Sensors 2022, 22(17), 6598; https://doi.org/10.3390/s22176598 - 1 Sep 2022
Cited by 2 | Viewed by 2226
Abstract
Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver’s recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data collected during a simulated driving task to [...] Read more.
Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver’s recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data collected during a simulated driving task to classify recent sitting and sleep history. Participants (n = 84, Mean ± SD age = 23.5 ± 4.8, 49% Female) completed a seven-day laboratory study. Raw accelerometry data were collected from a thigh-worn accelerometer during a 20-min simulated drive (8:10 h and 17:30 h each day). Two convolutional neural networks (CNNs; ResNet-18 and DixonNet) were trained to classify accelerometry data into four classes (sitting or breaking up sitting and 9-h or 5-h sleep). Accuracy was determined using five-fold cross-validation. ResNet-18 produced higher accuracy scores: 88.6 ± 1.3% for activity (compared to 77.2 ± 2.6% from DixonNet) and 88.6 ± 1.1% for sleep history (compared to 75.2 ± 2.6% from DixonNet). Class activation mapping revealed distinct patterns of movement and postural changes between classes. Findings demonstrate the suitability of CNNs in classifying sitting and sleep history using thigh-worn accelerometer data collected during a simulated drive. This approach has implications for the identification of drivers at risk of fatigue-related impairment. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
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21 pages, 5097 KiB  
Article
The Method of Evaluation of Radio Altimeter Methodological Error in Laboratory Environment
by Pavol Kurdel, Marek Češkovič, Natália Gecejová, Ján Labun and Ján Gamec
Sensors 2022, 22(14), 5394; https://doi.org/10.3390/s22145394 - 19 Jul 2022
Cited by 5 | Viewed by 2977
Abstract
The presented article is focused on the evaluation of aviation radio altimeter (ALT) methodological error in order to increase air traffic safety. It briefly explains the background of methodological error at the theoretical level and offers practical conclusions to understand the [...] Read more.
The presented article is focused on the evaluation of aviation radio altimeter (ALT) methodological error in order to increase air traffic safety. It briefly explains the background of methodological error at the theoretical level and offers practical conclusions to understand the issue. A radio altimeter provides information on an aircraft or helicopter’s instantaneous (radar) altitude or UAV to the pilot and another assistance system, such as an autopilot or an anticollision system. The height measurement of the most common used ALTs is realized with an accuracy of from ±0.30 m to ±0.75 m. This error rate corresponds to and is caused by the radio altimeter’s methodological error (ΔH). The ALT operating parameters are defined by carrier frequency, modulation frequency, and frequency lift. The methodological error of ALT can be obtained in three ways—calculated on a theoretical level, simulated in a suitable simulation environment, or evaluated in laboratory conditions. The ambiguity of ALT methodological error measurement causes bias in its presentation. This often leads to an incorrect determination of measurement inaccuracy (too optimistic statement of error value). The article’s primary goal is to present a new method for determining the value of the methodological error and its effect on the resulting error of measurement of the radio altitude (radar altitude). It presents a new experimental laboratory method for measuring ΔH and the resulting accuracy of height measurement with a radio altimeter. Thanks to this method, it can be verified that the information obtained by measuring the height above the ground corresponds to the standard specified by the manufacturer. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
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19 pages, 2754 KiB  
Article
Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms
by Brian Bogue-Jimenez, Xiaolei Huang, Douglas Powell and Ana Doblas
Sensors 2022, 22(9), 3534; https://doi.org/10.3390/s22093534 - 6 May 2022
Cited by 9 | Viewed by 4766
Abstract
Glucose monitoring technologies allow users to monitor glycemic fluctuations (e.g., blood glucose levels). This is particularly important for individuals who have diabetes mellitus (DM). Traditional self-monitoring blood glucose (SMBG) devices require the user to prick their finger and extract a blood drop to [...] Read more.
Glucose monitoring technologies allow users to monitor glycemic fluctuations (e.g., blood glucose levels). This is particularly important for individuals who have diabetes mellitus (DM). Traditional self-monitoring blood glucose (SMBG) devices require the user to prick their finger and extract a blood drop to measure the blood glucose based on chemical reactions with the blood. Unlike traditional glucometer devices, noninvasive continuous glucose monitoring (NICGM) devices aim to solve these issues by consistently monitoring users’ blood glucose levels (BGLs) without invasively acquiring a sample. In this work, we investigated the feasibility of a novel approach to NICGM using multiple off-the-shelf wearable sensors and learning-based models (i.e., machine learning) to predict blood glucose. Two datasets were used for this study: (1) the OhioT1DM dataset, provided by the Ohio University; and (2) the UofM dataset, created by our research team. The UofM dataset consists of fourteen features provided by six sensors for studying possible relationships between glucose and noninvasive biometric measurements. Both datasets are passed through a machine learning (ML) pipeline that tests linear and nonlinear models to predict BGLs from the set of noninvasive features. The results of this pilot study show that the combination of fourteen noninvasive biometric measurements with ML algorithms could lead to accurate BGL predictions within the clinical range; however, a larger dataset is required to make conclusions about the feasibility of this approach. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
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10 pages, 2629 KiB  
Perspective
How to Build Live-Cell Sensor Microdevices
by Pelagia-Irene Gouma
Sensors 2023, 23(8), 3886; https://doi.org/10.3390/s23083886 - 11 Apr 2023
Viewed by 1575
Abstract
There is a lot of discussion on how viruses (such as influenza and SARS-CoV-2) are transmitted in air, potentially from aerosols and respiratory droplets, and thus it is important to monitor the environment for the presence of an active pathogen. Currently, the presence [...] Read more.
There is a lot of discussion on how viruses (such as influenza and SARS-CoV-2) are transmitted in air, potentially from aerosols and respiratory droplets, and thus it is important to monitor the environment for the presence of an active pathogen. Currently, the presence of viruses is being determined using primarily nucleic acid-based detection methods, such as reverse transcription- polymerase chain reaction (RT-PCR) tests. Antigen tests have also been developed for this purpose. However, most nucleic acid and antigen methods fail to discriminate between a viable and a non-viable virus. Therefore, we present an alternative, innovative, and disruptive approach involving a live-cell sensor microdevice that captures the viruses (and bacteria) from the air, becomes infected by them, and emits signals for an early warning of the presence of pathogens. This perspective outlines the processes and components required for living sensors to monitor the presence of pathogens in built environments and highlights the opportunity to use immune sentinels in the cells of normal human skin to produce monitors for indoor air pollutants. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
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