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Electronics
Special Issues
Wearable and Implantable Sensors in Healthcare
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Wearable and Implantable Sensors in Healthcare

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 8580

Special Issue Editors

Dr. Chi Cuong Vu

E-Mail Website
Guest Editor
Department of Information Technology Specialization, FPT University, D1 Street, Saigon Hi-Tech Park, Long Thanh My Ward, Thu Duc City, Ho Chi Minh City, Vietnam
Interests: tiny machine learning; e-textiles; flexible wearable sensors; human motion and healthcare monitoring; applied artificial intelligent
Prof. Dr. Jooyong Kim

E-Mail Website
Guest Editor
Department of Organic Materials and Fiber Engineering, Soongsil University, Seoul, Korea
Interests: electronic textiles; flexible wearable sensors; nanotechnologies; fiber technologies

Special Issue Information

Dear Colleagues,

Wearable electronics, as an essential branch of new-generation electronic devices, have attracted the attention of many scientists owing to the outstanding results of healthcare monitoring and human–machine interface applications. This interest stems from the massive demand for unique electrical performances and small dimensions, which can apply to clothing or skin to track changes in the human body quickly.

Moreover, the advanced achievements from artificial intelligence and IoT have redefined the intelligent healthcare system based on wearable electronics.

Recent studies on materials, geometrical designs, manufacturing strategies, and new technologies are expected to contribute to dramatic changes in this field.

This Special Issue aims to showcase the articles and reviews in wearable sensors in healthcare systems. Research areas may include (but are not limited to) the following:

  • Wearable integrated systems;
  • Flexible electronics for healthcare applications;
  • Artificial intelligence for healthcare systems;
  • Internet of Things for healthcare systems;
  • Wearable sensors;
  • Implantable electronics;
  • Actuators;
  • Wearable electronic materials;
  • Human motion monitoring.

Dr. Chi Cuong Vu
Prof. Dr. Jooyong Kim
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. Electronics 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 2400 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

  • wearable intergrated systems
  • wearable sensors
  • implantable sensors
  • flexible electronic materials
  • actuators
  • AI for wearable healthcare systems
  • IoT for wearable healthcare systems
  • human motion monitoring

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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

16 pages, 8832 KiB  
Article
A New Smartphone-Based Method for Remote Health Monitoring: Assessment of Respiratory Kinematics
by Emanuele Vignali, Emanuele Gasparotti, Luca Miglior, Vincenzo Gervasi, Lorenzo Simone, Dorela Haxhiademi, Lara Frediani, Gabriele Borelli, Sergio Berti and Simona Celi
Electronics 2024, 13(6), 1132; https://doi.org/10.3390/electronics13061132 - 20 Mar 2024
Cited by 1 | Viewed by 1370
Abstract
The remote monitoring of clinical parameters plays a fundamental role in different situations, like pandemic health emergencies and post-surgery conditions. In these situations, the patients might be impeded in their movements, and it could be difficult to have specific health monitoring. In recent [...] Read more.
The remote monitoring of clinical parameters plays a fundamental role in different situations, like pandemic health emergencies and post-surgery conditions. In these situations, the patients might be impeded in their movements, and it could be difficult to have specific health monitoring. In recent years, technological advances in smartphones have opened up new possibilities in this landscape. The present work aims to propose a new method for respiratory kinematics monitoring via smartphone sensors. In particular, a specific application was developed to register inertial measurement unit (IMU) sensor data from the smartphone for respiratory kinematics measurement and to guide the user through a specific acquisition session. The session was defined to allow the monitoring of the respiratory movement in five prescribed positions. The application and the sequence were successfully tested on a given population of 77 healthy volunteers. The resulting accelerometers and gyroscope signals were processed to evaluate the significance of differences according to participants’ sex, vector components, and smartphone positioning and, finally, to estimate the respiratory rate. The statistical differences that emerged revealed the significance of information in the different acquisition positions. Full article
(This article belongs to the Special Issue Wearable and Implantable Sensors in Healthcare)
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24 pages, 9811 KiB  
Article
A Hybrid CNN-LSTM-Based Approach for Pedestrian Dead Reckoning Using Multi-Sensor-Equipped Backpack
by Feyissa Woyano, Sangjoon Park, Vladimirov Blagovest Iordanov and Soyeon Lee
Electronics 2023, 12(13), 2957; https://doi.org/10.3390/electronics12132957 - 5 Jul 2023
Cited by 2 | Viewed by 2466
Abstract
Researchers in academics and companies working on location-based services (LBS) are paying close attention to indoor localization based on pedestrian dead reckoning (PDR) because of its infrastructure-free localization method. PDR is the fundamental localization technique that utilize human motion to perform localization in [...] Read more.
Researchers in academics and companies working on location-based services (LBS) are paying close attention to indoor localization based on pedestrian dead reckoning (PDR) because of its infrastructure-free localization method. PDR is the fundamental localization technique that utilize human motion to perform localization in a relative sense with respect to the initial position. The size, weight, and power consumption of micromechanical systems (MEMS) embedded into smartphones are remarkably low, making them appropriate for localization and positioning. Traditional pedestrian PDR methods predict position and orientation using stride length and continuous integration of acceleration in step and heading system (SHS)-based PDR and inertial navigation system (INS)-PDR, respectively. However, these two approaches provide accumulations of error and do not effectively leverage the inertial measurement unit (IMU) sequences. The PDR navigation solution relays on the standard of the MEMS, which yields PDR with the acceleration and angular velocity from the accelerometer and gyroscope, respectively. However, low-cost small MEMSs endure enormous error sources such as bias and noise. Hence, MEMS assessments lead to navigation solution drifts when utilized as inputs to the PDR. As a consequence, numerous methods have been proposed to mitigate and model the errors related to MEMS. Deep learning-based dead reckoning algorithms are provided to address aforementioned issues owing to the end-to-end learning framework. This paper proposes a hybrid convolutional neural network (CNN) and long short-term memory network (LSTM)-based inertial PDR system that extracts inertial measurement units (IMU) sequence features. The end-to-end learning framework is introduced to leverage the efficiency of low-cost MEMS because data-driven solutions provide more complete knowledge of the ever-increasing data volume and computational power over the filtering model approach. A CNN-LSTM model was employed to capture local spatial and temporal features. Experiments conducted on odometry datasets collected from multi-sensor backpack devices demonstrated that the proposed architecture outperformed previous traditional PDR methods, demonstrating that the root mean square error (RMSE) for the best user was 0.52 m. On the handheld smartphone-only dataset the best achieved R2 metric was 0.49. Full article
(This article belongs to the Special Issue Wearable and Implantable Sensors in Healthcare)
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19 pages, 3837 KiB  
Article
From Bioimpedance to Volume Estimation: A Model for Edema Calculus in Human Legs
by Santiago F. Scaliusi, Luis Gimenez, Pablo Pérez, Daniel Martín, Alberto Olmo, Gloria Huertas, F. Javier Medrano and Alberto Yúfera
Electronics 2023, 12(6), 1383; https://doi.org/10.3390/electronics12061383 - 14 Mar 2023
Cited by 6 | Viewed by 3621
Abstract
Heart failure (HF) is a severe disease and one of the most important causes of death in our society nowadays. A significant percentage of patients hospitalized for decompensation of heart failure are readmitted after some weeks or months due to an expected bad [...] Read more.
Heart failure (HF) is a severe disease and one of the most important causes of death in our society nowadays. A significant percentage of patients hospitalized for decompensation of heart failure are readmitted after some weeks or months due to an expected bad and uncontrolled HF evolution due to the lack of the patient supervision in real time. Herein is presented a straightforward electric model useful for volume leg section calculus based on the bioimpedance test as a way to assist with the acute HF patient’s supervision. The method has been developed for time-evolution edema evaluation in patients’ corresponding legs. The data are picked up with a wearable device specifically developed for acute heart failure patients. As an initial step, a calibration method is proposed to extract the extracellular volume component from bioimpedance measurements done in healthy subjects, and then applied to unhealthy ones. The intra- and extracellular resistance components are calculated from fitted Cole–Cole model parameters derived from BI spectroscopy measurements. Results obtained in a pilot assay, with healthy subjects and heart failure subjects, show sensitivities in leg volume [mL/Ω], with much lower values for healthy than in unhealthy people, being an excellent biomarker to discriminate between both. Finally, circadian cycle evolution for leg volume has been measured from the bioimpedance test as an extension of the work, enabling an alternative parameter for the characterization of one day of human activity for any person. Full article
(This article belongs to the Special Issue Wearable and Implantable Sensors in Healthcare)
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