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Bioengineering
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Monitoring and Analysis of Human Biosignals, 3rd Edition
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Monitoring and Analysis of Human Biosignals, 3rd Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 3048

Special Issue Editor

Dr. Antonio Lanata

E-Mail Website
Guest Editor
Department of Information Engineering, Università degli Studi di Firenze, Firenze, Italy
Interests: wearable system for non-invasive physiological monitoring; statistical and nonlinear biomedical signal processing; affective computing; mood/mental/neurological disorders; human–animal–robot interaction; autonomic nervous system investigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the third edition of the previous release of “Monitoring and Analysis of Human Biosignals” (https://www.mdpi.com/journal/bioengineering/special_issues/monitoring_analysis_human_biosignals) and “Monitoring and Analysis of Human Biosignals, 2nd Edition” (https://www.mdpi.com/journal/bioengineering/special_issues/OT70E6A244).

Biosignals are the evidence that biosystems communicate and are our primary source of information on their behavior, playing a pivotal role in health care monitoring and clinical diagnosis. Among the best-known biosignals are the following: ECG, EEG, EMG, EOG, ERG and GSR. Biosignals also refer to non-electrical signals, such as acoustic signals and optical signals. Recent advances in artificial intelligence (AI) and machine learning (ML) make it possible to gather more information on biosignals, and this may lead to a deeper understanding of various pathophysiological states. 

This Special Issue, titled "Monitoring and Analysis of Human Biosignals, 3rd Edition", aims to provide a collection of contributions showing new advancements and applications of biosignal monitoring and analysis. Topics may include, but are not limited to, the following:

  • Biosignal acquisition;
  • Biosignal quality analysis;
  • Biosignal processing and analysis;
  • Deep learning for biosignal analysis;
  • Human body sensing;
  • Biomedical image processing and analysis;
  • Computational neuroscience;
  • Emotion analysis;
  • Speech analysis.

Dr. Antonio Lanata
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • biosignal
  • human body sensing
  • ECG
  • EEG
  • EMG
  • artificial intelligence

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Related Special Issues

Published Papers (3 papers)

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Research

21 pages, 9501 KiB  
Article
A Deep Convolution Method for Hypertension Detection from Ballistocardiogram Signals with Heat-Map-Guided Data Augmentation
by Renjie Cheng, Yi Huang, Wei Hu, Ken Chen and Yaoqin Xie
Bioengineering 2025, 12(3), 221; https://doi.org/10.3390/bioengineering12030221 - 21 Feb 2025
Viewed by 622
Abstract
Hypertension (HPT) is a chronic disease characterized by the consistent elevation of arterial blood pressure, which is considered to be a significant risk factor for conditions such as stroke, coronary artery disease, and heart failure. The detection and continuous monitoring of HPT can [...] Read more.
Hypertension (HPT) is a chronic disease characterized by the consistent elevation of arterial blood pressure, which is considered to be a significant risk factor for conditions such as stroke, coronary artery disease, and heart failure. The detection and continuous monitoring of HPT can be a demanding process. As a non-contact measuring method, the ballistocardiography (BCG) signal characterizes the repetitive body motion resulting from the forceful ejection of blood into the major blood vessels during each heartbeat. Therefore, it can be applied for HPT detection. HPT detection with BCG signals remains a challenging task. In this study, we propose an end-to-end deep convolutional model BH-Net for HPT detection through BCG signals. We also propose a data augmentation scheme by selecting the J-peak neighborhoods from the BCG time sequences for hypertension detection. Rigorously evaluated via a public data-set, we report an average accuracy of 97.93% and an average F1-score of 97.62%, outperforming the comparative state-of-the-art methods. We also report that the performance of the traditional machine learning methods and the comparative deep learning models was improved with the proposed data augmentation scheme. Full article
(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, 3rd Edition)
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18 pages, 21891 KiB  
Article
Multi-Exoskeleton Performance Evaluation: Integrated Muscle Energy Indices to Determine the Quality and Quantity of Assistance
by Vasco Fanti, Sergio Leggieri, Tommaso Poliero, Matteo Sposito, Darwin G. Caldwell and Christian Di Natali
Bioengineering 2024, 11(12), 1231; https://doi.org/10.3390/bioengineering11121231 - 5 Dec 2024
Cited by 1 | Viewed by 944
Abstract
The assessment of realistic work tasks is a critical aspect of introducing exoskeletons to work environments. However, as the experimental task’s complexity increases, the analysis of muscle activity becomes increasingly challenging. Thus, it is essential to use metrics that adequately represent the physical [...] Read more.
The assessment of realistic work tasks is a critical aspect of introducing exoskeletons to work environments. However, as the experimental task’s complexity increases, the analysis of muscle activity becomes increasingly challenging. Thus, it is essential to use metrics that adequately represent the physical human–exoskeleton interaction (pHEI). Muscle activity analysis is usually reduced to a comparison of point values (average or maximum muscle contraction), neglecting the signals’ trend. Metrics based on single values, however, lack information about the dynamism of the task and its duration. Their meaning can be uncertain, especially when analyzing complex movements or temporally extended activities, and it is reduced to an overall assessment of the interaction on the whole task. This work proposes a method based on integrated EMGs (iEMGs) to evaluate the pHEI by considering task dynamism, temporal duration, and the neural energy associated with muscle activity. The resulting signal highlights the task phases in which the exoskeleton reduces or increases the effort required to accomplish the task, allowing the calculation of specific indices that quantify the energy exchange in terms of assistance (AII), resistance (RII), and overall interaction (OII). The method provides an analysis tool that enables developers and controller designers to receive insights into the exoskeleton performances and the quality of the user-robot interaction. The application of this method is provided for passive and two active back support exoskeletons: the Laevo, XoTrunk, and StreamEXO. Full article
(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, 3rd Edition)
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17 pages, 4587 KiB  
Article
Improving Brain Metabolite Detection with a Combined Low-Rank Approximation and Denoising Diffusion Probabilistic Model Approach
by Yeong-Jae Jeon, Kyung Min Nam, Shin-Eui Park and Hyeon-Man Baek
Bioengineering 2024, 11(11), 1170; https://doi.org/10.3390/bioengineering11111170 - 20 Nov 2024
Viewed by 929
Abstract
In vivo proton magnetic resonance spectroscopy (MRS) is a noninvasive technique for monitoring brain metabolites. However, it is challenged by a low signal-to-noise ratio (SNR), often necessitating extended scan times to compensate. One of the conventional techniques for noise reduction is signal averaging, [...] Read more.
In vivo proton magnetic resonance spectroscopy (MRS) is a noninvasive technique for monitoring brain metabolites. However, it is challenged by a low signal-to-noise ratio (SNR), often necessitating extended scan times to compensate. One of the conventional techniques for noise reduction is signal averaging, which is inherently time-consuming and can lead to participant discomfort, thus posing limitations in clinical settings. This study aimed to develop a hybrid denoising strategy that integrates low-rank approximation and denoising diffusion probabilistic model (DDPM) to enhance MRS data quality and shorten scan times. Using publicly available 1H MRS datasets from 15 subjects, we applied the Casorati SVD and DDPM to obtain baseline and functional data during a pain stimulation task. This method significantly improved SNR, resulting in outcomes comparable to or better than averaging over 32 signals. It also provided the most consistent metabolite measurements and adequately tracked temporal changes in glutamate levels, correlating with pain intensity ratings after heating. These findings demonstrate that our approach enhances MRS data quality, offering a more efficient alternative to conventional methods and expanding the potential for the real-time monitoring of neurochemical changes. This contribution has the potential to advance MRS techniques by integrating advanced denoising methods to increase the acquisition speed and enhance the precision of brain metabolite analyses. Full article
(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, 3rd Edition)
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