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Emerging Smart and Intelligent Wearable/Implantable Sensors for IoT and Biomedical Applications

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 12992

Special Issue Editors

Prof. Dr. Syed Kamrul Islam

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, The University of Missouri-Columbia (MIZZOU), Columbia, MO 65211, USA
Interests: analog/mixed-signal integrated circuits; monolithic sensors; semiconductor devices
Special Issues, Collections and Topics in MDPI journals
Prof. Bashir I. Morshed

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Guest Editor
Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN, USA
Interests: body-worn sensors, edge computing, inkjet-printed flexible electronics, machine learning, mobile health, real-time signal processing, wearables
Dr. Ifana Mahbub

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
Interests: low-power CMOS analog and RF integrated circuit design; antennas and wireless interfaces for biomedical sensors
Special Issues, Collections and Topics in MDPI journals
Prof. Khaza Anuarul Hoque

E-Mail Website
Guest Editor
Department of Electrical Engineering & Computer Science at the University of Missouri-Columbia
Interests: formal verification, cybersecurity, cyber-physical systems, internet-of-things, and fault-tolerant embedded systems

Special Issue Information

Dear colleagues,

Rapid advancements in sensor and integrated circuit technologies in the recent years have facilitated widespread use of smart and intelligent sensor instruments in wide range of applications including wearable and implantable biomedical sensors and Internet of Things (IoT). Smart networks architectures and technologies have been adopted in mobile device technologies facilitating a revolution of IoT. In addition, incorporation of machine learning/artificial intelligence on-chip have led to the realization of smart and intelligence sensors. The purpose of this special issue is to address on-going research activities in the design of smart and intelligent sensors focusing on the applications in the areas of IoT and biomedical implantable and wearable sensor instruments.

Potential topics include but are not limited to the following:

  • Emerging smart wearable and implantable sensors and devices
  • Novel design, manufacturing, technology, and architecture for smart sensors
  • Machine Learning, deep Learning, and artificial intelligence on-chip
  • Smart sensor network architectures
  • Real-time and low-power signal processing for smart sensors
  • Flexible, printable, and biocompatible sensors and systems
  • Smart organic, inorganic and hybrid electronic sensors
  • Nanotechnology based smart sensors for IoT and biomedical applications
  • Secure, energy-aware, and self-powered energy-harvesting smart sensors
  • Smart sensor applications in IoT and healthcare
  • Trends in smart sensor technologies
  • Wearable computing and wireless communication system
  • Sensor-based threats to IOT devices for industrial and biomedical applications

Prof. Dr. Syed K. Islam,
Prof. Bashir I. Morshed,
Prof. Ifana Mahbub
Prof. Khaza Anuarul Hoque
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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

23 pages, 4936 KiB  
Article
A Real-Time, Automatic, and Dynamic Scheduling and Control System for PET Patients Based on Wearable Sensors
by Shin-Yan Chiou, Kun-Ju Lin and Ya-Xin Dong
Sensors 2021, 21(4), 1104; https://doi.org/10.3390/s21041104 - 5 Feb 2021
Cited by 2 | Viewed by 2508
Abstract
Positron emission tomography (PET) is one of the commonly used scanning techniques. Medical staff manually calculate the estimated scan time for each PET device. However, the number of PET scanning devices is small, the number of patients is large, and there are many [...] Read more.
Positron emission tomography (PET) is one of the commonly used scanning techniques. Medical staff manually calculate the estimated scan time for each PET device. However, the number of PET scanning devices is small, the number of patients is large, and there are many changes including rescanning requirements, which makes it very error-prone, puts pressure on staff, and causes trouble for patients and their families. Although previous studies proposed algorithms for specific inspections, there is currently no research on improving the PET process. This paper proposes a real-time automatic scheduling and control system for PET patients with wearable sensors. The system can automatically schedule, estimate and instantly update the time of various tasks, and automatically allocate beds and announce schedule information in real time. We implemented this system, collected time data of 200 actual patients, and put these data into the implementation program for simulation and comparison. The average time difference between manual and automatic scheduling was 7.32 min, and it could reduce the average examination time of 82% of patients by 6.14 ± 4.61 min. This convinces us the system is correct and can improve time efficiency, while avoiding human error and staff pressure, and avoiding trouble for patients and their families. Full article
(This article belongs to the Special Issue Emerging Smart and Intelligent Wearable/Implantable Sensors for IoT and Biomedical Applications)
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14 pages, 564 KiB  
Article
Using Machine Learning to Train a Wearable Device for Measuring Students’ Cognitive Load during Problem-Solving Activities Based on Electrodermal Activity, Body Temperature, and Heart Rate: Development of a Cognitive Load Tracker for Both Personal and Classroom Use
by William L. Romine, Noah L. Schroeder, Josephine Graft, Fan Yang, Reza Sadeghi, Mahdieh Zabihimayvan, Dipesh Kadariya and Tanvi Banerjee
Sensors 2020, 20(17), 4833; https://doi.org/10.3390/s20174833 - 27 Aug 2020
Cited by 32 | Viewed by 6189
Abstract
Automated tracking of physical fitness has sparked a health revolution by allowing individuals to track their own physical activity and health in real time. This concept is beginning to be applied to tracking of cognitive load. It is well known that activity in [...] Read more.
Automated tracking of physical fitness has sparked a health revolution by allowing individuals to track their own physical activity and health in real time. This concept is beginning to be applied to tracking of cognitive load. It is well known that activity in the brain can be measured through changes in the body’s physiology, but current real-time measures tend to be unimodal and invasive. We therefore propose the concept of a wearable educational fitness (EduFit) tracker. We use machine learning with physiological data to understand how to develop a wearable device that tracks cognitive load accurately in real time. In an initial study, we found that body temperature, skin conductance, and heart rate were able to distinguish between (i) a problem solving activity (high cognitive load), (ii) a leisure activity (moderate cognitive load), and (iii) daydreaming (low cognitive load) with high accuracy in the test dataset. In a second study, we found that these physiological features can be used to predict accurately user-reported mental focus in the test dataset, even when relatively small numbers of training data were used. We explain how these findings inform the development and implementation of a wearable device for temporal tracking and logging a user’s learning activities and cognitive load. Full article
(This article belongs to the Special Issue Emerging Smart and Intelligent Wearable/Implantable Sensors for IoT and Biomedical Applications)
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13 pages, 1938 KiB  
Article
Modeling and Characterization of Scaling Factor of Flexible Spiral Coils for Wirelessly Powered Wearable Sensors
by Dipon K. Biswas, Melissa Sinclair, Tien Le, Salvatore Andrea Pullano, Antonino S. Fiorillo and Ifana Mahbub
Sensors 2020, 20(8), 2282; https://doi.org/10.3390/s20082282 - 17 Apr 2020
Cited by 7 | Viewed by 3392
Abstract
Wearable sensors are a topic of interest in medical healthcare monitoring due to their compact size and portability. However, providing power to the wearable sensors for continuous health monitoring applications is a great challenge. As the batteries are bulky and require frequent charging, [...] Read more.
Wearable sensors are a topic of interest in medical healthcare monitoring due to their compact size and portability. However, providing power to the wearable sensors for continuous health monitoring applications is a great challenge. As the batteries are bulky and require frequent charging, the integration of the wireless power transfer (WPT) module into wearable and implantable sensors is a popular alternative. The flexible sensors benefit by being wirelessly powered, as it not only expands an individual’s range of motion, but also reduces the overall size and the energy needs. This paper presents the design, modeling, and experimental characterization of flexible square-shaped spiral coils with different scaling factors for WPT systems. The effects of coil scaling factor on inductance, capacitance, resistance, and the quality factor (Q-factor) are modeled, simulated, and experimentally validated for the case of flexible planar coils. The proposed analytical modeling is helpful to estimate the coil parameters without using the time-consuming Finite Element Method (FEM) simulation. The analytical modeling is presented in terms of the scaling factor to find the best-optimized coil dimensions with the maximum Q-factor. This paper also presents the effect of skin contact with the flexible coil in terms of the power transfer efficiency (PTE) to validate the suitability as a wearable sensor. The measurement results at 405 MHz show that when in contact with the skin, the 20 mm× 20 mm receiver (RX) coil achieves a 42% efficiency through the air media for a 10 mm distance between the transmitter (TX) and RX coils. Full article
(This article belongs to the Special Issue Emerging Smart and Intelligent Wearable/Implantable Sensors for IoT and Biomedical Applications)
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