Editorial

2 pages, 162 KiB

Open AccessEditorial

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

Viewed by 1446

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

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15 pages, 5269 KiB

Open AccessArticle

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 5 | Viewed by 2187

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)

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18 pages, 6770 KiB

Open AccessArticle

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 - 03 Apr 2020

Cited by 3 | Viewed by 2938

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)

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12 pages, 910 KiB

Open AccessArticle

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 21 | Viewed by 3732

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)

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15 pages, 3529 KiB

Open AccessArticle

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 23 | Viewed by 4763

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)

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15 pages, 2887 KiB

Open AccessArticle

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 2157

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)

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17 pages, 6376 KiB

Open AccessArticle

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 3458

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)

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14 pages, 16147 KiB

Open AccessArticle

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 3293

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)

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16 pages, 6440 KiB

Open AccessArticle

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 16 | Viewed by 4708

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)

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13 pages, 2269 KiB

Open AccessArticle

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 8 | Viewed by 3178

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)

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