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Human Body Communication
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Human Body Communication

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 687

Special Issue Editor

Dr. Pietro Savazzi

E-Mail Website
Guest Editor
Department of Electrical, Biomedical and Computer Engineering, University of Pavia, Pavia, Italy
Interests: wireless communications; wireless sensor networks; intrabody communications and networks; digital signal processing

Special Issue Information

Dear Colleagues,

This call-for-papers seeks original contributions on human body communication (HBC), covering both body area and intrabody communication paradigms. The aim is to gather high-quality research that advances the understanding and practical implementations of HBC systems. We invite papers addressing communication technologies utilizing the human body as a transmission medium, including galvanic coupling, capacitive coupling, electro-quasistatic, molecular, and electromagnetic methods. Studies may focus on communication within the body (e.g., between implants or wearable sensors), as well as between devices distributed on or near the body.

We encourage submissions that present experimental results, prototypes, testbed implementations, or simulation-based evaluations, alongside theoretical models and channel characterization studies. Topics of interest include (but are not limited to) physical layer design, signal propagation analysis, interference management, energy efficiency, safety assessments, and innovative application scenarios in healthcare, personal security, sports, and human–computer interaction.

This call aims to stimulate multidisciplinary contributions, spanning electrical engineering, biomedical engineering, signal processing, and applied physics, to advance the frontier of HBC research and technology. All submitted papers will undergo rigorous peer review to ensure scientific quality and relevance.

Dr. Pietro Savazzi
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

  • human body communication
  • body area communication
  • intrabody communication

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Published Papers (1 paper)

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Research

26 pages, 1432 KB  
Article
Generalizable Hybrid Wavelet–Deep Learning Architecture for Robust Arrhythmia Detection in Wearable ECG Monitoring
by Ukesh Thapa, Bipun Man Pati, Attaphongse Taparugssanagorn and Lorenzo Mucchi
Sensors 2025, 25(21), 6590; https://doi.org/10.3390/s25216590 - 26 Oct 2025
Viewed by 593
Abstract
This paper investigates Electrocardiogram (ECG) rhythm classification using a progressive deep learning framework that combines time–frequency representations with complementary hand-crafted features. In the first stage, ECG signals from the PhysioNet Challenge 2017 dataset are transformed into scalograms and input to diverse architectures, including [...] Read more.
This paper investigates Electrocardiogram (ECG) rhythm classification using a progressive deep learning framework that combines time–frequency representations with complementary hand-crafted features. In the first stage, ECG signals from the PhysioNet Challenge 2017 dataset are transformed into scalograms and input to diverse architectures, including Simple Convolutional Neural Network (SimpleCNN), Residual Network with 18 Layers (ResNet-18), Convolutional Neural Network-Transformer (CNNTransformer), and Vision Transformer (ViT). ViT achieved the highest accuracy (0.8590) and F1-score (0.8524), demonstrating the feasibility of pure image-based ECG analysis, although scalograms alone showed variability across folds. In the second stage, scalograms were fused with scattering and statistical features, enhancing robustness and interpretability. FusionViT without dimensionality reduction achieved the best performance (accuracy = 0.8623, F1-score = 0.8528), while Fusion ResNet-18 offered a favorable trade-off between accuracy (0.8321) and inference efficiency (0.016 s per sample). The application of Principal Component Analysis (PCA) reduced the dimensionality of the feature from 509 to 27, reducing the computational cost while maintaining competitive performance (FusionViT precision = 0.8590). The results highlight a trade-off between efficiency and fine-grained temporal resolution. Training-time augmentations mitigated class imbalance, enabling lightweight inference (0.006–0.043 s per sample). For real-world use, the framework can run on wearable ECG devices or mobile health apps. Scalogram transformation and feature extraction occur on-device or at the edge, with efficient models like ResNet-18 enabling near real-time monitoring. Abnormal rhythm alerts can be sent instantly to users or clinicians. By combining visual and statistical signal features, optionally reduced with PCA, the framework achieves high accuracy, robustness, and efficiency for practical deployment. Full article
(This article belongs to the Special Issue Human Body Communication)
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