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Sensors
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Applications of Flexible and Printable Sensors
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Applications of Flexible and Printable Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 9586

Special Issue Editors

Prof. Dr. Emilio Sardini

E-Mail Website
Guest Editor
Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy
Interests: electronic instrumentation; sensors and signal conditioning electronics; development of autonomous sensors for biomedical applications
Dr. Mauro Serpelloni

E-Mail Website
Guest Editor
Department of Electronics for Automation, University of Brescia, V. Branze 38, 25123 Brescia, Italy
Interests: innovative fabrication technologies; printed sensor applications; flexible/stretchable electronics; printed sensor system; additive manufacturing; sensors for smart devices; innovative fabrication methods for sensors directly on objects; metrological characterization of sensors for biomedical and industrial applications; signal processing for printed sensors and smart objects; printed sensors integrated on wearable and IoT devices; hybrid printed electronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In previous decades, sensors were a hot research topic, as the interface between the real and the electronic instrumentation world. Nowadays, they are again one of the most important topics, and, in the close future, they will gain an essential part as a “smart object”.

The core of their importance dwells in their ability to measure the characteristics of the environments they are in, consequently opening up the possibility to apply the results of the artificial smartness in almost all application fields.

Sensors have recently evolved from single devices, connected to other devices and applied to an object, to a native part of a smart object. A smart object’s main characteristics are in the possibility to measure, to think, and to interact with the external world. New functions will be available thanks to this paradigm.

Smart objects will need to integrate a sensing element during its fabrication. This is the reason from which the sensors will need to be compatible with the production process of the object, acquiring new characteristics and being flexible and printable.

Dear colleagues, I invite you to participate to this new challenge and to shape the close future by sending a paper with your recent development in the field of printable and flexible sensors.

Prof. Dr. Emilio Sardini
Dr. Mauro Serpelloni
Guest Editors

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

  • Printable sensors
  • Flexible sensors
  • Smart object
  • IoT sensors
  • Stretchable sensors
  • Wearable sensors
  • Textile sensors

Published Papers (3 papers)

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Research

13 pages, 7053 KiB  
Article
Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor
by Dafydd Ravenscroft, Ioannis Prattis, Tharun Kandukuri, Yarjan Abdul Samad, Giorgio Mallia and Luigi G. Occhipinti
Sensors 2022, 22(1), 299; https://doi.org/10.3390/s22010299 - 31 Dec 2021
Cited by 14 | Viewed by 3074
Abstract
Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference [...] Read more.
Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene’s unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition. Full article
(This article belongs to the Special Issue Applications of Flexible and Printable Sensors)
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12 pages, 4253 KiB  
Article
3D Electrochemical Sensor and Microstructuration Using Aerosol Jet Printing
by Tiziano Fapanni, Emilio Sardini, Mauro Serpelloni and Sarah Tonello
Sensors 2021, 21(23), 7820; https://doi.org/10.3390/s21237820 - 24 Nov 2021
Cited by 6 | Viewed by 1759
Abstract
Electrochemical sensors are attracting great interest for their different applications. To improve their performances, basic research focuses on two main issues: improve their metrological characteristics (e.g., repeatability, reusability and sensitivity) and investigate innovative fabrication processes. In this work, we demonstrate an innovative microstructuration [...] Read more.
Electrochemical sensors are attracting great interest for their different applications. To improve their performances, basic research focuses on two main issues: improve their metrological characteristics (e.g., repeatability, reusability and sensitivity) and investigate innovative fabrication processes. In this work, we demonstrate an innovative microstructuration technique aimed at increasing electrochemical sensor sensitivity to improve electrode active area by an innovative fabrication technique. The process is empowered by aerosol jet printing (AJP), an additive-manufacturing and non-contact printing technique that allows depositing functional inks in precise patterns such as parallel lines and grids. The 3D printed microstructures increased the active surface area by up to 130% without changing the substrate occupancy. Further, electrochemical detection of ferro/ferri-cyanide was used to evaluate the sensitivity of the electrodes. This evaluation points out a sensitivity increase of 2.3-fold on average between bare and fully microstructured devices. The increase of surface area and sensitivity are well linearly correlated as expected, verifying the fitness of our production process. The proposed microstructuration is a viable solution for many applications that requires high sensitivity, and the proposed technique, since it does not require masks or complex procedures, turns out to be flexible and applicable to infinite construction geometries. Full article
(This article belongs to the Special Issue Applications of Flexible and Printable Sensors)
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18 pages, 31181 KiB  
Article
A Fast and Precise Tool for Multi-Layer Planar Coil Self-Inductance Calculation
by Andreia Faria, Luís Marques, Carlos Ferreira, Filipe Alves and Jorge Cabral
Sensors 2021, 21(14), 4864; https://doi.org/10.3390/s21144864 - 16 Jul 2021
Cited by 13 | Viewed by 3964
Abstract
An open-source tool that allows for a fast and precise analytical calculation of multi-layer planar coils self-inductance, without any geometry limitation is proposed here. The process of designing and simulating planar coils to achieve reliable results is commonly limited on accuracy and or [...] Read more.
An open-source tool that allows for a fast and precise analytical calculation of multi-layer planar coils self-inductance, without any geometry limitation is proposed here. The process of designing and simulating planar coils to achieve reliable results is commonly limited on accuracy and or geometry, or are too time-consuming and expensive, thus a tool to speed up this design process is desired. The model is based on Grover equations, valid for any geometry. The validation of the tool was performed through the comparison with experimental measurements, Finite Element Model (FEM) simulations, and the main analytical methods usually used in literature, with errors registered to be below 2.5%, when compared to standard FEM simulations, and when compared to experimental measurements they are below 10% in the case of the 1-layer coils, and below 5% in the 2-layer coils (without taking into consideration the coil connectors). The proposed model offers a new approach to the calculation of the self-inductance of planar coils of several layers that combines precision, speed, independence of geometry, easy interaction, and no need for extra resources. Full article
(This article belongs to the Special Issue Applications of Flexible and Printable Sensors)
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