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Implanted and Wearable Body Sensors Network
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Implanted and Wearable Body Sensors Network

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

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 4035

Special Issue Editors

Dr. Somdip Dey

E-Mail Website
Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: embedded systems; information security and privacy; MPSoC; DVFS; computer systems engineering; heterogeneous architecture; artificial intelligence; machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Delaram Jarchi

E-Mail Website
Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: biomedical signal processing; machine learning and wearable sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaojun Zhai

E-Mail Website
Guest Editor
School of Computer Science and Electronic Engineering (CSEE), University of Essex, Colchester CO4 3SQ, UK
Interests: embedded systems; system-on-chip (SoC) architecture, with focus on intelligence, performance, security, power, and reliability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Implantable and wearable body sensors network systems are an important development in the emerging research field at the intersection of computer science and healthcare industry. Such body sensor networks are used in the healthcare industry to minimize the need for caregivers and help chronically ill and elderly people to live an independent life, besides providing people with quality care.

I cordially invite you to contribute to this Special Issue, which will present studies addressed to all types of implanted and wearable body sensor networks and related topics.

Topics of interest include but are not limited to:

  • Body area networks
  • Wireless sensor networks
  • Wearable sensors
  • Implanted sensors
  • Healthcare applications
  • Biosensors
  • Nanotechnology
  • Nanorobotics
  • Implanted communication
  • Wearable Computing
  • Privacy and security of body area networks
  • Privacy and security of implanted sensors

Dr. Somdip Dey
Dr. Delaram Jarchi
Dr. Xiaojun Zhai
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

  • Body area networks
  • Implanted sensors
  • Wearable computing
  • Wireless body sensor networks
  • Nanotechnology
  • Nanorobotics
  • Implanted communication

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Published Papers (2 papers)

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Research

14 pages, 4745 KiB  
Article
Embedded Machine Learning System for Muscle Patterns Detection in a Patient with Shoulder Disarticulation
by Erick Guzmán-Quezada, Claudia Mancilla-Jiménez, Fernanda Rosas-Agraz, Rebeca Romo-Vázquez and Hugo Vélez-Pérez
Sensors 2024, 24(11), 3264; https://doi.org/10.3390/s24113264 - 21 May 2024
Viewed by 907
Abstract
The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable system that classifies the electrical activity of three shoulder [...] Read more.
The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable system that classifies the electrical activity of three shoulder muscles in real time for actuator control, marking a milestone in the autonomy of prosthetic devices. Utilizing low-power microcontrollers, the system ensures continuous EMG signal recording, enhancing user mobility. Focusing on a case study—a 42-year-old man with left shoulder disarticulation—EMG activity was recorded over two days using a specifically designed electronic board. Data processing was performed using the Edge Impulse platform, renowned for its effectiveness in implementing AI on edge devices. The first day was dedicated to a training session with 150 repetitions spread across 30 trials and three different movements. Based on these data, the second day tested the AI model’s ability to classify EMG signals in new movement executions in real time. The results demonstrate the potential of portable AI-based systems for prosthetic control, offering accurate and swift EMG signal classification that enhances prosthetic user functionality and experience. This study not only underscores the feasibility of real-time EMG signal classification but also paves the way for future research on practical applications and improvements in the quality of life for prosthetic users. Full article
(This article belongs to the Special Issue Implanted and Wearable Body Sensors Network)
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12 pages, 9329 KiB  
Article
A Variable-Volume Heart Model for Galvanic Coupling-Based Conductive Intracardiac Communication
by Yiming Liu, Yueming Gao, Liting Chen, Tao Liu, Jiejie Yang, Siohang Pun, Mangi Vai and Min Du
Sensors 2022, 22(12), 4455; https://doi.org/10.3390/s22124455 - 12 Jun 2022
Cited by 1 | Viewed by 1738
Abstract
Conductive intracardiac communication (CIC) has become one of the most promising technologies in multisite leadless pacemakers for cardiac resynchronization therapy. Existing studies have shown that cardiac pulsation has a significant impact on the attenuation of intracardiac communication channels. In this study, a novel [...] Read more.
Conductive intracardiac communication (CIC) has become one of the most promising technologies in multisite leadless pacemakers for cardiac resynchronization therapy. Existing studies have shown that cardiac pulsation has a significant impact on the attenuation of intracardiac communication channels. In this study, a novel variable-volume circuit-coupled electrical field heart model, which contains blood and myocardium, is proposed to verify the phenomenon. The influence of measurements was combined with the model as the equivalent circuit. Dynamic intracardiac channel characteristics were obtained by simulating models with varying volumes of the four chambers according to the actual cardiac cycle. Subsequently, in vitro experiments were carried out to verify the model’s correctness. Among the dependences of intracardiac communication channels, the distance between pacemakers exerted the most substantial influence on attenuation. In the simulation and measurement, the relationship between channel attenuation and pulsation was found through the variable-volume heart model and a porcine heart. The CIC channel attenuation had a variation of less than 3 dB. Full article
(This article belongs to the Special Issue Implanted and Wearable Body Sensors Network)
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