Dear Colleagues,

Sensors provide the primary environmental information necessary for the control of many processes. In the automation processes employed in Industry 4.0, many observations rely on information gained by sensors. Many sensor principles are well known, but their applicability depends on factors such as ease of use, robustness, accuracy, repeatability, wireless applicability, energy supply and consumption, and last but not least, cost.

This Special Issue focuses on state-of-the-art sensor principles which address those tasks. The topic is open; potential topics include, but are not limited to, the following research areas:

• Sensor network;
• Biomedical sensors;
• Biosensors;
• Wearable sensors;
• Chemical sensors;
• Physical sensors;
• Lab-on-a-chip;
• Electrochemical sensors;
• Optoelectronic sensors;
• Optical sensors;
• Thermal sensors;
• Magnetic sensors;
• Piezoelectric sensors;
• Gas sensors;
• Affinity sensors;
• Electronic nose and tongue;
• Impedance sensors;
• Conductometric sensors.

Prof. Dr. Abdellah Touhafi
Guest Editor

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.

Title: Kinocardiography acquired by smartphone Shows High Repeatability in Patients with various cardiovascular diseases
Authors: Amin Hossein, […], Philippe van de Borne
Affiliation: Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium
Abstract: BACKGROUND: Heart Failure (HF) has experienced a tremendous evolution in the past decade. For instance, many challenges associated with treating cardiac disease with pacemakers have already been successfully targeted. Today, the preventive side of developing solutions for HF is increasing in medical research. Thanks to new technological improvements in telemonitoring, there is in the medical research field a rise and interest in smartphone monitoring systems as well as in monitoring devices that can be used easily at home. METHODS: This study intends to compare a hardware thoracic sensor, considered the reference, and the acquisition performed with a mobile smartphone. The technique developed for the kinocardiographic consists of a device measuring the heart’s vibrations at two places in the body: on the torso (Seismocardiography) and on the lower back (Ballistocardiography). The first measurement, on the chest, reflects the local strength of the heartbeat and the transmission of blood to the body's main artery, the aorta. The second measurement, in the lower back, results from the contraction and movement of blood within the arterial system. Based on these two measurements, the energy produced by the heart and transmitted to the torso or the whole body is calculated. The energy over an entire cardiac cycle and the distribution of that energy within a heartbeat is used to characterize the mechanical function of the heart. kinocardiographic research devices use these characteristics technology to measure parameters that can aid in diagnosing heart failure. The mobile application is designed for heart screening and monitoring. When the smartphone is placed on the thorax of a resting patient, the intensity of mechanical cardiac activity can be measured for one minute. The mobile application uses the motion sensors (accelerometers and gyroscopes) found in most modern smartphones to measure the heart’s mechanical activity via vibrations recorded on the upper sternum. The amplitude of these vibrations allows us to compute kinetic energy which we demonstrated is directly proportional to blood velocity and cardiac motion. RESULTS: A mobile application prototype makes it possible to take measurements similar to the kinocardiographic thoracic sensor. Such a tool would allow anyone with a smartphone-type mobile phone to measure their cardiac mechanical function and make it available to the attending physician. [...] CONCLUSIONS: After major study advances for kinocardiography, especially for KINO device, the Heartemis study aims to determine the repeatability of kinocardiographic measurements when comparing a validated device to a modern smartphone.

Title: Anomaly Detection in Images for Industry 4.0: Implementing a Multi-Task Self-Supervised Learning Approach
Authors: Cools Aurélie, Belarbi Mohammed Amin, Mahmoudi Sidi Ahmed
Affiliation: Department of Informatics, Software and AI, University of Mons, Belgium
Abstract: This paper delves into the challenge of anomaly detection in images within the Industry 4.0 framework, focusing on data diversity, the rarity of anomalies, and the high cost of data collection. Confronted with the complexity of categorizing and labeling a variety of defects, and the infrequent occurrence of these anomalies, we propose our approach: a multi-task self-supervised learning method. Utilizing convolutional neural networks, this method effectively learns from limited or unlabeled data by focusing on multiple learning objectives simultaneously to enhance detection accuracy. This approach shows promising potential in terms of precision and error reduction, but it also brings to light challenges associated with anomaly variability and data collection costs. Our study makes a significant contribution to the field by addressing these issues and paving the way for new research avenues, underscoring the importance of multi-task self-supervised learning methods in anomaly detection for industrial environments.

Title: Multi-Camera 3D DIC: from lab samples to real-life structures
Authors: Davide Mastrodicasa (1,2) , Emilio Di Lorenzo (1), Simone Manzato (1), Bart Peeters (1) and Patrick Guillaume (2)
Affiliation: (1) Siemens Industry Software NV, Interleuvenlaan 68, Leuven, 3001, Belgium - (2) Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium
Abstract: Experimental characterization and validation of light and composite components in the aerospace and wind energy domain entails multiple evaluations (structural, aerodynamic, acoustic, etc.) and expensive measurement campaigns. Structural certifications are currently performed by using point-wise transducers. Placing sensors along the structure under test is a labor-intensive and time-consuming task and it could introduce electrical noise to the measured signals due to the extensive and unavoidable wiring. Vision-based techniques, such as Digital Image Correlation (DIC), can be employed to reduce instrumentation costs and improve efficiency. This is one of the reasons behind the development of image processing techniques to perform analysis of mechanical structures without contact and without having to instrument the specimen. However, due to geometrical constraints in a large testing facility, a single stereo vision-based system cannot efficiently cover the whole structure. Owing to a stereo-vision system’s limited field of view, large-scale test articles and structures with complex curvatures need to be measured using several camera systems. Additionally, reducing the distance from the object under test allows to reduce the Field of View (FOV), and the noise floor. As a consequence, this permits the detection of smaller displacements and the observation of higher frequencies in the structural dynamic field. This paper aims to better understand 3D DIC measurement capabilities by assessing the performance of multi-camera 3D DIC systems in which data from traditional DIC systems are stitched together in a universal coordinate system. The obtained results are compared with point-wise sensors placed on the structure under test.

Title: Development and Evaluation of Moisture Comfort-Enhanced Embroidered Textrode for sEMG Signal Monitoring
Authors: Bulcha Belay Etana 1,2,*,Benny Malengier 1,Janarthanan Krishnamoorthy 3 andLieva Van Langenhove 1
Affiliation: 1Department of Materials, Textiles and Chemical Engineering, Ghent University, 9000 Gent, Belgium 2School of Materials Science and Engineering, Jimma Institute of Technology, Jimma University, Jimma 378, Ethiopia 3School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma 378, Ethiopia
Abstract: This study aims to develop an innovative Textile electrode with enhanced moisture comfort and improved stability for monitoring sEMG Biosignals. The electrodes employ a sandwiched-layer embroidered design using satin stitch techniques to enhance the potential stability of the textile electrode. To achieve electrodes with the lowest contact impedance, a water-conducting electrode construction was implemented. This construction consists of embroidered polyamide-silver hybrid conductive thread, with filling textiles sandwiched between this threads and the support fabric along with bobbin yarn. The support fabric is an elastic textile band. Various designs and filling textiles were prepared for the embriodered textile electrode and tested for moisture permeability, contact angle, electrode thickness, moisture retention time, and electrical impedance stability. The overall performance of the developed electrodes was comprehensively evaluated. Indications were found that the preferred electrode construction to achieve the lowest contact impedance includes a circle shaped electrodes, comprised of embroidered a polyamide-silver hybrid conductive thread, with the 3Dknit filling textiles sandwiched between this yarn and the support fabric along with bobbin yarn in both dry and wet conditions. Wet electrodes exhibited significantly reduced contact impedance, making them preferable. Nevertheless, there are identified opportunities for enhancing the performance of dry electrodes by incorporating moisture comfort into the system. This approach mitigates the drawbacks associated with wet electrodes, including fluid administration, electrolyte drying-out, and discomfort by facilitating rapid absorption and adsorption of moisture. The comprehensive evaluation of the electrodes included an assessment of moisture retention performance and their ability to record long-term sEMG signals, contributing valuable insights to the advancement of Biosignals monitoring in smart textiles.