Tactile Sensing Technology and Systems

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A:Physics".

Deadline for manuscript submissions: closed (31 August 2019) | Viewed by 37449

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor


E-Mail Website
Guest Editor
Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genova, Via Opera Pia 11A, I16145 Genova, Italy
Interests: electronic/artificial sensitive skin; tactile sensing systems for prosthetics and robotics; electronic embedded systems; internet of things; microelectronics and nanoelectronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Tactile sensors acquire tactile information through physical touch; measurands are, for example, temperature, vibration, softness, texture, shape, composition and shear and normal forces. Electronic/artificial skin comprises embedded electronic systems which integrate tactile sensing arrays, signal acquisition, data processing and decoding, and can transmit collated information. Such electronic/artificial skin will become one of the main sensing essentials in prosthetics, bionics, robotics, virtual reality, haptic devices, IoT, etc.

Tactile sensors are basically distributed sensors which translate mechanical and physical variables and pain stimuli into electrical variables. Contact information is further processed and conveyed to a supervising system. Tactile arrays ought to be mechanically flexible (i.e., conformable to the object it is applied to) and stretchable and tactile information decoding must be implemented in real time. The development of artificial tactile sensing is a big challenge as it involves numerous research areas. Application domains include humanoid and industrial robotics, prosthetics, biomedical instrumentation, health care, cyber physical systems, virtual reality, arts, to name but a few.

Recent and relevant achievements in materials and transducers have not yet successfully boosted system developments due to the challenging gaps which still need to be filled at many levels, e.g. data decoding and processing, miniaturization, mechanical compliance, robustness, among others. Tactile sensing has developed rapidly over the past three decades, but has yet to achieve high impact breakthroughs in application domains.

In this Special Issue, we focus on both insights and advancements in tactile sensing with the goal of bridging different research areas, e.g., material science, electronics, robotics, neuroscience, mechanics, sensors, MEMS/NEMS, addictive and 3D manufacturing, bio and neuro-engineering.

We would like to receive commentaries, perspectives and insightful reviews on related topics as well as technological breakthroughs of original works, civil and industrial applications in both short communications and full papers.

Prof. Maurizio Valle
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. Micromachines is an international peer-reviewed open access monthly 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

  • Innovative structural and sensing materials
  • Manufacturing technology
  • Additive and 3D manufacturing
  • Novel tactile sensors
  • Flexible, conformable and stretchable sensors and arrays
  • Electronic interface
  • Artificial and electronic skin
  • Tactile data processing and interpretation
  • Innovative applications
  • Haptic devices
  • Touch-based human–robot interaction
  • Tactile and visual sensing integration
  • Tactile Internet
  • Tactile sensing in prosthetics, neuro-rehabilitation, neuro and bio engineering, consumer goods, arts, IoT

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 (10 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

2 pages, 162 KiB  
Editorial
Editorial of Special Issue “Tactile Sensing Technology and Systems”
by Maurizio Valle
Micromachines 2020, 11(5), 506; https://doi.org/10.3390/mi11050506 - 16 May 2020
Cited by 1 | Viewed by 1691
Abstract
Human skin has remarkable features such as self-healing ability, flexibility, stretchability, high sensitivity and tactile sensing capability [...] Full article
(This article belongs to the Special Issue Tactile Sensing Technology and Systems)

Research

Jump to: Editorial

15 pages, 5269 KiB  
Article
Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array
by Weiting Liu, Binpeng Zhan, Chunxin Gu, Ping Yu, Guoshi Zhang, Xin Fu, Christian Cipriani and Liang Hu
Micromachines 2020, 11(6), 583; https://doi.org/10.3390/mi11060583 - 10 Jun 2020
Cited by 6 | Viewed by 2586
Abstract
Object curvature plays an important role in grasping and manipulation. To be more exact, local curvature is a more useful information for grasping practically. Vision and touch are the two main methods to extract surface curvature of an object, but vision is often [...] Read more.
Object curvature plays an important role in grasping and manipulation. To be more exact, local curvature is a more useful information for grasping practically. Vision and touch are the two main methods to extract surface curvature of an object, but vision is often limited since the complete contact area is invisible during manipulation. In this paper, the authors propose an object curvature estimation method based on an artificial neural network algorithm through a lab-developed sparse tactile sensor array. The compliant layer covering on the sensor is indispensable for fitting the curved surface. Three types (plane, convex sphere, and convex cylinder) of sample and each type of sample including 30 different radiuses (1 mm to 30 mm) were used in the experiment. The overall classification accuracy was 93.1%. The average curvature radius estimating error based on an artificial neural network (ANN) algorithm was 1.87 mm. When the radius of curvature was bigger than 5 mm, the average relative error was smaller than 20%. As a comparison, the sensor array density we used in this paper was less than 9/cm2, which was smaller than the density of human SAII receptors, but the discrimination result was close to the SAII receptors. Comparison with the curvature discrimination ability of the human body showed that this method has a promising application prospect. Full article
(This article belongs to the Special Issue Tactile Sensing Technology and Systems)
Show Figures

Figure 1

18 pages, 6770 KiB  
Article
Layout Transposition for Non-Visual Navigation of Web Pages by Tactile Feedback on Mobile Devices
by Fabrice Maurel, Gaël Dias, Waseem Safi, Jean-Marc Routoure and Pierre Beust
Micromachines 2020, 11(4), 376; https://doi.org/10.3390/mi11040376 - 3 Apr 2020
Cited by 3 | Viewed by 3406
Abstract
In this paper, we present the results of an empirical study that aims to evaluate the performance of sighted and blind people to discriminate web page structures using vibrotactile feedback. The proposed visuo-tactile substitution system is based on a portable and economical solution [...] Read more.
In this paper, we present the results of an empirical study that aims to evaluate the performance of sighted and blind people to discriminate web page structures using vibrotactile feedback. The proposed visuo-tactile substitution system is based on a portable and economical solution that can be used in noisy and public environments. It converts the visual structures of web pages into tactile landscapes that can be explored on any mobile touchscreen device. The light contrasts overflown by the fingers are dynamically captured, sent to a micro-controller, translated into vibrating patterns that vary in intensity, frequency and temperature, and then reproduced by our actuators on the skin at the location defined by the user. The performance of the proposed system is measured in terms of perception of frequency and intensity thresholds and qualitative understanding of the shapes displayed. Full article
(This article belongs to the Special Issue Tactile Sensing Technology and Systems)
Show Figures

Figure 1

12 pages, 910 KiB  
Article
Smart Tactile Sensing Systems Based on Embedded CNN Implementations
by Mohamad Alameh, Yahya Abbass, Ali Ibrahim and Maurizio Valle
Micromachines 2020, 11(1), 103; https://doi.org/10.3390/mi11010103 - 18 Jan 2020
Cited by 22 | Viewed by 4280
Abstract
Embedding machine learning methods into the data decoding units may enable the extraction of complex information making the tactile sensing systems intelligent. This paper presents and compares the implementations of a convolutional neural network model for tactile data decoding on various hardware platforms. [...] Read more.
Embedding machine learning methods into the data decoding units may enable the extraction of complex information making the tactile sensing systems intelligent. This paper presents and compares the implementations of a convolutional neural network model for tactile data decoding on various hardware platforms. Experimental results show comparable classification accuracy of 90.88% for Model 3, overcoming similar state-of-the-art solutions in terms of time inference. The proposed implementation achieves a time inference of 1.2 ms while consuming around 900 μ J. Such an embedded implementation of intelligent tactile data decoding algorithms enables tactile sensing systems in different application domains such as robotics and prosthetic devices. Full article
(This article belongs to the Special Issue Tactile Sensing Technology and Systems)
Show Figures

Figure 1

15 pages, 3529 KiB  
Article
Implementation of Hand Gesture Recognition Device Applicable to Smart Watch Based on Flexible Epidermal Tactile Sensor Array
by Sung-Woo Byun and Seok-Pil Lee
Micromachines 2019, 10(10), 692; https://doi.org/10.3390/mi10100692 - 12 Oct 2019
Cited by 28 | Viewed by 5594
Abstract
Ever since the development of digital devices, the recognition of human gestures has played an important role in many Human-Computer interface applications. Various wearable devices have been developed, and inertial sensors, magnetic sensors, gyro sensors, electromyography, force-sensitive resistors, and other types of sensors [...] Read more.
Ever since the development of digital devices, the recognition of human gestures has played an important role in many Human-Computer interface applications. Various wearable devices have been developed, and inertial sensors, magnetic sensors, gyro sensors, electromyography, force-sensitive resistors, and other types of sensors have been used to identify gestures. However, there are different drawbacks for each sensor, which affect the detection of gestures. In this paper, we present a new gesture recognition method using a Flexible Epidermal Tactile Sensor based on strain gauges to sense deformation. Such deformations are transduced to electric signals. By measuring the electric signals, the sensor can estimate the degree of deformation, including compression, tension, and twist, caused by movements of the wrist. The proposed sensor array was demonstrated to be capable of analyzing the eight motions of the wrist, and showed robustness, stability, and repeatability throughout a range of experiments aimed at testing the sensor array. We compared the performance of the prototype device with those of previous studies, under the same experimental conditions. The result shows our recognition method significantly outperformed existing methods. Full article
(This article belongs to the Special Issue Tactile Sensing Technology and Systems)
Show Figures

Figure 1

15 pages, 2887 KiB  
Article
Quasi Single Point Calibration Method for High-Speed Measurements of Resistive Sensors
by Jesús A. Botín-Córdoba, Óscar Oballe-Peinado, José A. Sánchez-Durán and José A. Hidalgo-López
Micromachines 2019, 10(10), 664; https://doi.org/10.3390/mi10100664 - 30 Sep 2019
Cited by 7 | Viewed by 2591
Abstract
Direct interface circuits are a simple, inexpensive alternative for the digital conversion of a sensor reading, and in some of these circuits only passive calibration elements are required in order to carry out this conversion. In the case of resistive sensors, the most [...] Read more.
Direct interface circuits are a simple, inexpensive alternative for the digital conversion of a sensor reading, and in some of these circuits only passive calibration elements are required in order to carry out this conversion. In the case of resistive sensors, the most accurate methods of calibration, namely two-point calibration method (TPCM) and fast calibration methods I and II (FCMs I and II), require two calibration resistors to estimate the value of a sensor. However, although FCMs I and II considerably reduce the time necessary to estimate the value of the sensor, this may still be excessive in certain applications, such as when making repetitive readings of a sensor or readings of a large series of sensors. For these situations, this paper proposes a series of calibration methods that decrease the mean estimation time. Some of the proposed methods (quasi single-point calibration methods) are based on the TPCM, while others (fast quasi single-point calibration methods) make the most of the advantages of FCM. In general, the proposed methods significantly reduce estimation times in exchange for a small increase in errors. To validate the proposal, a circuit with a Xilinx XC3S50AN-4TQG144C FPGA has been designed and resistors in the range (267.56 Ω, 7464.5 Ω) have been measured. For 20 repetitive measurements, the proposed methods achieve time reductions of up to 61% with a relative error increase of only 0.1%. Full article
(This article belongs to the Special Issue Tactile Sensing Technology and Systems)
Show Figures

Figure 1

17 pages, 6376 KiB  
Article
Assessment of Stickiness with Pressure Distribution Sensor Using Offset Magnetic Force
by Takayuki Kameoka, Akifumi Takahashi, Vibol Yem, Hiroyuki Kajimoto, Kohei Matsumori, Naoki Saito and Naomi Arakawa
Micromachines 2019, 10(10), 652; https://doi.org/10.3390/mi10100652 - 27 Sep 2019
Cited by 2 | Viewed by 3873
Abstract
The quantification of stickiness experienced upon touching a sticky or adhesive substance has attracted intense research attention, particularly for application to haptics, virtual reality, and human–computer interactions. Here, we develop and evaluate a device that quantifies the feeling of stickiness experienced upon touching [...] Read more.
The quantification of stickiness experienced upon touching a sticky or adhesive substance has attracted intense research attention, particularly for application to haptics, virtual reality, and human–computer interactions. Here, we develop and evaluate a device that quantifies the feeling of stickiness experienced upon touching an adhesive substance. Keeping in mind that a typical pressure distribution sensor can only measure a pressing force, but not a tensile force, in our setup, we apply an offset pressure to a pressure distribution sensor and measure the tensile force generated by an adhesive substance as the difference from the offset pressure. We propose a method of using a magnetic force to generate the offset pressure and develop a measuring device using a magnet that attracts magnetic pin arrays and pin magnets; the feasibility of the method is verified with a first prototype. We develop a second prototype that overcomes the noise problems of the first, arising from the misalignment of the pins owing to the bending of the magnetic force lines at the sensor edges. We also obtain measurement results for actual samples and standard viscosity liquids. Our findings indicate the feasibility of our setup as a suitable device for measuring stickiness. Full article
(This article belongs to the Special Issue Tactile Sensing Technology and Systems)
Show Figures

Graphical abstract

14 pages, 16147 KiB  
Article
Biomimetic Tactile Sensors with Bilayer Fingerprint Ridges Demonstrating Texture Recognition
by Eunsuk Choi, Onejae Sul, Jusin Lee, Hojun Seo, Sunjin Kim, Seongoh Yeom, Gunwoo Ryu, Heewon Yang, Yoonsoo Shin and Seung-Beck Lee
Micromachines 2019, 10(10), 642; https://doi.org/10.3390/mi10100642 - 25 Sep 2019
Cited by 16 | Viewed by 3740
Abstract
In this article, we report on a biomimetic tactile sensor that has a surface kinetic interface (SKIN) that imitates human epidermal fingerprint ridges and the epidermis. The SKIN is composed of a bilayer polymer structure with different elastic moduli. We improved the tactile [...] Read more.
In this article, we report on a biomimetic tactile sensor that has a surface kinetic interface (SKIN) that imitates human epidermal fingerprint ridges and the epidermis. The SKIN is composed of a bilayer polymer structure with different elastic moduli. We improved the tactile sensitivity of the SKIN by using a hard epidermal fingerprint ridge and a soft epidermal board. We also evaluated the effectiveness of the SKIN layer in shear transfer characteristics while varying the elasticity and geometrical factors of the epidermal fingerprint ridges and the epidermal board. The biomimetic tactile sensor with the SKIN layer showed a detection capability for surface structures under 100 μm with only 20-μm height differences. Our sensor could distinguish various textures that can be easily accessed in everyday life, demonstrating that the sensor may be used for texture recognition in future artificial and robotic fingers. Full article
(This article belongs to the Special Issue Tactile Sensing Technology and Systems)
Show Figures

Figure 1

16 pages, 6440 KiB  
Article
Flexible Tactile Sensor Array for Slippage and Grooved Surface Recognition in Sliding Movement
by Yancheng Wang, Jianing Chen and Deqing Mei
Micromachines 2019, 10(9), 579; https://doi.org/10.3390/mi10090579 - 30 Aug 2019
Cited by 20 | Viewed by 5291
Abstract
Flexible tactile sensor with contact force sensing and surface texture recognition abilities is crucial for robotic dexterous grasping and manipulation in daily usage. Different from force sensing, surface texture discrimination is more challenging in the development of tactile sensors because of limited discriminative [...] Read more.
Flexible tactile sensor with contact force sensing and surface texture recognition abilities is crucial for robotic dexterous grasping and manipulation in daily usage. Different from force sensing, surface texture discrimination is more challenging in the development of tactile sensors because of limited discriminative information. This paper presents a novel method using the finite element modeling (FEM) and phase delay algorithm to investigate the flexible tactile sensor array for slippage and grooved surfaces discrimination when sliding over an object. For FEM modeling, a 3 × 3 tactile sensor array with a multi-layer structure is utilized. For sensor array sliding over a plate surface, the initial slippage occurrence can be identified by sudden changes in normal forces based on wavelet transform analysis. For the sensor array sliding over pre-defined grooved surfaces, an algorithm based on phase delay between different sensing units is established and then utilized to discriminate between periodic roughness and the inclined angle of the grooved surfaces. Results show that the proposed tactile sensor array and surface texture recognition method is anticipated to be useful in applications involving human-robotic interactions. Full article
(This article belongs to the Special Issue Tactile Sensing Technology and Systems)
Show Figures

Figure 1

13 pages, 2269 KiB  
Article
How the Skin Thickness and Thermal Contact Resistance Influence Thermal Tactile Perception
by Congyan Chen and Shichen Ding
Micromachines 2019, 10(2), 87; https://doi.org/10.3390/mi10020087 - 25 Jan 2019
Cited by 9 | Viewed by 3648
Abstract
A few experimental studies on thermal tactile perception have shown the influence of the thermal contact resistance which relates to contact surface roughness and pressure. In this paper, the theoretical influence of the skin thickness and the thermal contact resistance is studied on [...] Read more.
A few experimental studies on thermal tactile perception have shown the influence of the thermal contact resistance which relates to contact surface roughness and pressure. In this paper, the theoretical influence of the skin thickness and the thermal contact resistance is studied on the thermal model describing the temperature evolution in skin and materials when they come in contact. The thermal theoretical profile for reproducing a thermal cue for given contact thermal resistance is also presented. Compared to existing models of thermal simulation, the method proposed here has the advantage that the parameters of skin structure and thermal contact resistance in target temperature profiles can be adjusted in thermal perception simulation according to different skin features or surface roughness if necessary. The experimental results of surface roughness recognition were also presented. Full article
(This article belongs to the Special Issue Tactile Sensing Technology and Systems)
Show Figures

Figure 1

Back to TopTop