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MEMS Technology Based Sensors for Human Centered Applications
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MEMS Technology Based Sensors for Human Centered Applications

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

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 26602

Special Issue Editors

Prof. Dr. Nicola Pio Belfiore

E-Mail Website
Guest Editor
Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy
Interests: functional design; MEMS/NEMS; dynamic simulation of multi-body systems; robotics; topology; tribology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pietro Fanghella

E-Mail Website
Guest Editor
Department of Mechanical, Energy, Management and Transportation Engineering, University of Genoa, 16145 Genoa, Italy
Interests: kinematics; dynamics and control of machines; robot and mechatronic systems; mechanical design; dynamics of multibody systems; engineering education
Prof. Matteo Verotti

E-Mail Website
Guest Editor
Department of Mechanical, Energy, Management and Transportation Engineering, University of Genova, Via all'Opera Pia, 15 - 16145 Genoa, Italy
Interests: MEMS/NEMS; compliant mechanisms; smart materials; functional design; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last decades, MEMS (microelectromechanical systems) technology has changed the way sensors are designed, developed, and fabricated, leading to an increasing offer of microsystems for novel human-centered applications, such as

  • Health Monitoring,
  • Environmental Monitoring,
  • Disability Aids for Independent Life,
  • Diagnostics,
  • Smart Surgical Tools,
  • Minimally Invasive Surgery,
  • Locomotion Tracking,
  • Wellness,
  • Comfort, Safety, and Efficiency in Automotive and Transport Systems,
  • Workplace Safety,
  • Education,
  • Internet of Things.

Original or review papers will be welcomed from the major technical areas of research that support, or are specifically dedicated to, the abovementioned applications, including, but not limited to:

  • MEMS Technology-Based Sensors,
  • MEMS Design, Materials, Fabrication, Testing, and Packaging Technologies,
  • Nanoscale Devices and Nanomaterials,
  • Micro-Transducers with Soft, Flexible or Composite Materials,
  • Measurements in Microsystems,
  • Control of Microsystems,
  • ASIC (Application Specific Integrated Circuit) for Microsystems,
  • Microfluidics,
  • Wearable Devices,
  • MEMS for Energy Harvesting,
  • Acoustic and RF (Radio Frequency) MEMS,
  • Integrated Photonics and Optical MEMS.

Dr. Nicola Pio Belfiore
Prof. Dr. Pietro Fanghella
Dr. Matteo Verotti
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

  • MEMS
  • NEMS
  • Design
  • Fabrication
  • Technology
  • Health
  • Wearable
  • Disability
  • Diagnostics
  • Minimally Invasive Surgery
  • Wellness
  • Comfort
  • Safety
  • IoT

Published Papers (2 papers)

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Research

21 pages, 3201 KiB  
Article
A Convolutional Neural Network for Compound Micro-Expression Recognition
by Yue Zhao and Jiancheng Xu
Sensors 2019, 19(24), 5553; https://0-doi-org.brum.beds.ac.uk/10.3390/s19245553 - 16 Dec 2019
Cited by 18 | Viewed by 21779
Abstract
Human beings are particularly inclined to express real emotions through micro-expressions with subtle amplitude and short duration. Though people regularly recognize many distinct emotions, for the most part, research studies have been limited to six basic categories: happiness, surprise, sadness, anger, fear, and [...] Read more.
Human beings are particularly inclined to express real emotions through micro-expressions with subtle amplitude and short duration. Though people regularly recognize many distinct emotions, for the most part, research studies have been limited to six basic categories: happiness, surprise, sadness, anger, fear, and disgust. Like normal expressions (i.e., macro-expressions), most current research into micro-expression recognition focuses on these six basic emotions. This paper describes an important group of micro-expressions, which we call compound emotion categories. Compound micro-expressions are constructed by combining two basic micro-expressions but reflect more complex mental states and more abundant human facial emotions. In this study, we firstly synthesized a Compound Micro-expression Database (CMED) based on existing spontaneous micro-expression datasets. These subtle feature of micro-expression makes it difficult to observe its motion track and characteristics. Consequently, there are many challenges and limitations to synthetic compound micro-expression images. The proposed method firstly implemented Eulerian Video Magnification (EVM) method to enhance facial motion features of basic micro-expressions for generating compound images. The consistent and differential facial muscle articulations (typically referred to as action units) associated with each emotion category have been labeled to become the foundation of generating compound micro-expression. Secondly, we extracted the apex frames of CMED by 3D Fast Fourier Transform (3D-FFT). Moreover, the proposed method calculated the optical flow information between the onset frame and apex frame to produce an optical flow feature map. Finally, we designed a shallow network to extract high-level features of these optical flow maps. In this study, we synthesized four existing databases of spontaneous micro-expressions (CASME I, CASME II, CAS(ME)2, SAMM) to generate the CMED and test the validity of our network. Therefore, the deep network framework designed in this study can well recognize the emotional information of basic micro-expressions and compound micro-expressions. Full article
(This article belongs to the Special Issue MEMS Technology Based Sensors for Human Centered Applications)
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20 pages, 4069 KiB  
Article
Design of a 1-bit MEMS Gyroscope using the Model Predictive Control Approach
by Xiaofeng Wu, Zhicheng Xie, Xueliang Bai and Trevor Kwan
Sensors 2019, 19(3), 730; https://0-doi-org.brum.beds.ac.uk/10.3390/s19030730 - 11 Feb 2019
Cited by 6 | Viewed by 4115
Abstract
In this paper, a bi-level Delta-Sigma modulator-based MEMS gyroscope design is presented based on a Model Predictive Control (MPC) approach. The MPC is popular because of its capability of handling hard constraints. In this work, we propose to combine the 1-bit nature of [...] Read more.
In this paper, a bi-level Delta-Sigma modulator-based MEMS gyroscope design is presented based on a Model Predictive Control (MPC) approach. The MPC is popular because of its capability of handling hard constraints. In this work, we propose to combine the 1-bit nature of the bi-level Delta-Sigma modulator output with the MPC to develop a 1-bit processing-based MPC (OBMPC). This paper will focus on the affine relationship between the 1-bit feedback and the in-loop MPC controller, as this can potentially remove the multipliers from the controller. In doing so, the computational requirement of the MPC control is significantly alleviated, which makes the 1-bit MEMS Gyroscope feasible for implementation. In addition, a stable constrained MPC is designed, so that the input will not overload the quantizer while maintaining a higher Signal-to-Noise Ratio (SNR). Full article
(This article belongs to the Special Issue MEMS Technology Based Sensors for Human Centered Applications)
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