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Recent Advances in Tactile Sensors
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Recent Advances in Tactile Sensors

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

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 26848

Special Issue Editor

Prof. Dr. Maurizio Valle

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

Special Issue Information

Dear Colleagues,

Tactile sensors acquire information through touch; measurands are, e.g., temperature, vibration, softness, texture, shape, composition and shear and normal forces. Tactile sensors are basically distributed sensors which translate mechanical and physical variables and pain stimuli into electrical variables. Contact data is further processed to decoding high level information such as object size, surface contact features. Tactile arrays ought to be mechanically flexible (i.e., conformable to the object they are mounted on ) 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.

Tactile sensors have been receiving an increasing and unprecedented interest by the scientific community since some seminal works published in the nineties; since then, roboticists and researchers in the biomedical and health care domain started publishing their results in tactile sensors with an increasing pace. New research areas and application domains such as prosthetics, soft robotics, haptics, electronic skin, virtual reality received a boost by the advancements in technology, devices, systems and applications. In this special issue we aim to consolidate recent achievements and findings in tactile sensors and make a comprehensive assessment of meaningful and relevant results over the last years. The Special Issue ought to pave the way of future research directions and also open new perspectives enabled by recent achievements. Special attention will be given to novel applications in ( but not limited to ) health care, human machine interaction, virtual/augmented reality, arts and tactile internet.

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, computer 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. Dr. 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. 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

  • Materials 
  • Models
  • 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 
  • Haptics 
  • Soft robotics
  • Prosthetics
  • Neuro-rehabilitation 
  • neuro and bio engineering
  • Touch-based human–robot interaction
  • Human-machine interaction 
  • Touch and vision sensing integration 
  • Tactile Internet
  • Touch sensors in consumer goods
  • Virtual/augmented reality 
  • Touch sensors in arts
  • Touch sensors in IoT

Published Papers (4 papers)

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Research

17 pages, 8337 KiB  
Article
Video-Based Stress Detection through Deep Learning
by Huijun Zhang, Ling Feng, Ningyun Li, Zhanyu Jin and Lei Cao
Sensors 2020, 20(19), 5552; https://doi.org/10.3390/s20195552 - 28 Sep 2020
Cited by 29 | Viewed by 6506
Abstract
Stress has become an increasingly serious problem in the current society, threatening mankind’s well-beings. With the ubiquitous deployment of video cameras in surroundings, detecting stress based on the contact-free camera sensors becomes a cost-effective and mass-reaching way without interference of artificial traits and [...] Read more.
Stress has become an increasingly serious problem in the current society, threatening mankind’s well-beings. With the ubiquitous deployment of video cameras in surroundings, detecting stress based on the contact-free camera sensors becomes a cost-effective and mass-reaching way without interference of artificial traits and factors. In this study, we leverage users’ facial expressions and action motions in the video and present a two-leveled stress detection network (TSDNet). TSDNet firstly learns face- and action-level representations separately, and then fuses the results through a stream weighted integrator with local and global attention for stress identification. To evaluate the performance of TSDNet, we constructed a video dataset containing 2092 labeled video clips, and the experimental results on the built dataset show that: (1) TSDNet outperformed the hand-crafted feature engineering approaches with detection accuracy 85.42% and F1-Score 85.28%, demonstrating the feasibility and effectiveness of using deep learning to analyze one’s face and action motions; and (2) considering both facial expressions and action motions could improve detection accuracy and F1-Score of that considering only face or action method by over 7%. Full article
(This article belongs to the Special Issue Recent Advances in Tactile Sensors)
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25 pages, 8494 KiB  
Article
Validation of Screen-Printed Electronic Skin Based on Piezoelectric Polymer Sensors
by Hoda Fares, Yahya Abbass, Maurizio Valle and Lucia Seminara
Sensors 2020, 20(4), 1160; https://doi.org/10.3390/s20041160 - 20 Feb 2020
Cited by 15 | Viewed by 3165
Abstract
This paper proposes a validation method of the fabrication technology of a screen-printed electronic skin based on polyvinylidene fluoride-trifluoroethylene P(VDF-TrFE) piezoelectric polymer sensors. This required researchers to insure, through non-direct sensor characterization, that printed sensors were working as expected. For that, we adapted [...] Read more.
This paper proposes a validation method of the fabrication technology of a screen-printed electronic skin based on polyvinylidene fluoride-trifluoroethylene P(VDF-TrFE) piezoelectric polymer sensors. This required researchers to insure, through non-direct sensor characterization, that printed sensors were working as expected. For that, we adapted an existing model to non-destructively extract sensor behavior in pure compression (i.e., the d33 piezocoefficient) by indentation tests over the skin surface. Different skin patches, designed to sensorize a glove and a prosthetic hand (11 skin patches, 104 sensors), have been tested. Reproducibility of the sensor response and its dependence upon sensor position on the fabrication substrate were examined, highlighting the drawbacks of employing large A3-sized substrates. The average value of d33 for all sensors was measured at incremental preloads (1–3 N). A systematic decrease has been checked for patches located at positions not affected by substrate shrinkage. In turn, sensor reproducibility and d33 adherence to literature values validated the e-skin fabrication technology. To extend the predictable behavior to all skin patches and thus increase the number of working sensors, the size of the fabrication substrate is to be decreased in future skin fabrication. The tests also demonstrated the efficiency of the proposed method to characterize embedded sensors which are no more accessible for direct validation. Full article
(This article belongs to the Special Issue Recent Advances in Tactile Sensors)
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21 pages, 1671 KiB  
Article
Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization
by Zhen Deng, Yannick Jonetzko, Liwei Zhang and Jianwei Zhang
Sensors 2020, 20(4), 1050; https://doi.org/10.3390/s20041050 - 14 Feb 2020
Cited by 43 | Viewed by 6056
Abstract
Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively [...] Read more.
Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively unexplored. In this paper, we make use of tactile sensing for multi-fingered robot hands to adjust the grasping force to stabilize unknown objects without prior knowledge of their shape or physical properties. In particular, an online detection module based on Deep Neural Network (DNN) is designed to detect contact events and object material simultaneously from tactile data. In addition, a force estimation method based on Gaussian Mixture Model (GMM) is proposed to compute the contact information (i.e., contact force and contact location) from tactile data. According to the results of tactile sensing, an object stabilization controller is then employed for a robotic hand to adjust the contact configuration for object stabilization. The spatio-temporal property of tactile data is exploited during tactile sensing. Finally, the effectiveness of the proposed framework is evaluated in a real-world experiment with a five-fingered Shadow Dexterous Hand equipped with BioTac sensors. Full article
(This article belongs to the Special Issue Recent Advances in Tactile Sensors)
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20 pages, 17031 KiB  
Article
Dynamic Interface Pressure Monitoring System for the Morphological Pressure Mapping of Intermittent Pneumatic Compression Therapy
by Shumi Zhao, Rong Liu, Chengwei Fei and Dong Guan
Sensors 2019, 19(13), 2881; https://doi.org/10.3390/s19132881 - 28 Jun 2019
Cited by 14 | Viewed by 10625
Abstract
Intermittent pneumatic compression (IPC) is a proactive compression therapeutic technique in the prophylaxis of deep vein thrombosis, reduction of limb edema, and treatment of chronic venous ulcers. To appropriately detect and analyze biomechanical pressure profiles delivered by IPC in treatment, a dynamic interface [...] Read more.
Intermittent pneumatic compression (IPC) is a proactive compression therapeutic technique in the prophylaxis of deep vein thrombosis, reduction of limb edema, and treatment of chronic venous ulcers. To appropriately detect and analyze biomechanical pressure profiles delivered by IPC in treatment, a dynamic interface pressure monitoring system was developed to visualize and quantify morphological pressure mapping in the spatial and temporal domains in real time. The system comprises matrix soft sensors, a smart IPC device, a monitoring and analysis software, and a display unit. The developed soft sensor fabricated by an advanced screen printing technology was used to detect intermitted pressure by an IPC device. The pneumatic pressure signals inside the bladders of the IPC were also transiently collected by a data acquisition system and then transmitted to the computer through Bluetooth. The experimental results reveal that the developed pressure monitoring system can perform the real-time detection of dynamic pressures by IPC and display the morphological pressure mapping multi-dimensionally. This new system provides a novel modality to assist in the effective evaluation of proactive compression therapy in practice. The study results contribute to understanding the working mechanisms of IPC and improving its functional design based on intuitive biomechanical characteristics of compression delivery profiles. Full article
(This article belongs to the Special Issue Recent Advances in Tactile Sensors)
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