Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Data Processing in Biomedical Devices and Sensors
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Data Processing in Biomedical Devices and Sensors

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 2975

Special Issue Editor

Dr. Emi Yuda

E-Mail Website
Guest Editor
1. Innovation Center For Semiconductor And Digital Future, Mie University, Tsu 514-8507, Japan
2. Center for Data-driven Science and Artificial Intelligence Tohoku University, Sendai 980-8579, Japan
Interests: bio-signal processing; big data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in biomedical sensors and wearable devices have significantly enhanced the ability to monitor and assess human physiological and behavioral states in real-time. However, raw biomedical signals are often affected by various noise sources, motion artifacts, and individual variability, making accurate and meaningful interpretation a challenge. This Special Issue focuses on innovative data processing techniques—including signal denoising, feature extraction, machine learning, and real-time analytics—that enhance the performance and reliability of biomedical devices. We welcome original research and review papers that contribute to the development of robust algorithms, efficient signal processing methods, and integrated systems for clinical and home healthcare applications.

Dr. Emi Yuda
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences 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

  • biomedical signal processing
  • wearable sensors
  • noise reduction
  • feature extraction
  • machine learning
  • real-time monitoring
  • physiological signal analysis
  • healthcare technologies
  • sensor fusion
  • biomedical devices

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 807 KB  
Article
Validation of a Low-Cost Open-Source Surface Electromyography System for Muscle Activation Assessment in Sports and Rehabilitation
by Diego Perez-Rodes, Edgar Aljaro-Arevalo, Jose M. Jimenez-Olmedo and Basilio Pueo
Appl. Sci. 2026, 16(3), 1295; https://doi.org/10.3390/app16031295 - 27 Jan 2026
Viewed by 473
Abstract
Surface electromyography (sEMG) is widely used for neuromuscular assessment, but the high cost of commercial systems limits accessibility in sports and rehabilitation settings. This study validated a low-cost open-source sEMG device (OLI) against a commercial field reference (SHI) during dynamic and isometric knee [...] Read more.
Surface electromyography (sEMG) is widely used for neuromuscular assessment, but the high cost of commercial systems limits accessibility in sports and rehabilitation settings. This study validated a low-cost open-source sEMG device (OLI) against a commercial field reference (SHI) during dynamic and isometric knee extensions in 36 healthy adults. Three preprocessing pipelines were tested for OLI signals: RAW, global root mean square (RMS), and cycle-centered RMS. Waveform similarity was assessed using the coefficient of multiple correlation (CMC), retaining repetitions with CMC ≥ 0.80. For valid repetitions, a calibration model (SHI = a + b × OLI) and Bland–Altman analysis were applied to min–max normalized RMS and area-under-the-curve (AUC) metrics. The global RMS pipeline showed the best overall performance, retaining 81.9% of repetitions with high shape similarity (CMC = 0.92 ± 0.04). It exhibited minimal bias in RMS (−0.69; 95% CI −1.11 to −0.27), limits of agreement of approximately ±10 normalized units, and a moderate-to-high correlation (r = 0.73; 95% CI 0.69–0.77). The calibration slope (b = 0.16; 95% CI 0.15–0.17) showed moderate within-session consistency (ICC(2,1) = 0.45). These findings indicate that, with appropriate preprocessing, the open-source system provides practically acceptable agreement with a commercial reference for characterizing relative muscle activation patterns, supporting its use in applied sports and rehabilitation contexts. Full article
(This article belongs to the Special Issue Data Processing in Biomedical Devices and Sensors)
►▼ Show Figures

Figure 1

21 pages, 5197 KB  
Article
Unveiling the Extremely Low Frequency Component of Heart Rate Variability
by Krzysztof Adamczyk and Adam G. Polak
Appl. Sci. 2026, 16(1), 426; https://doi.org/10.3390/app16010426 - 30 Dec 2025
Viewed by 901
Abstract
Heart rate variability (HRV) comprises several components driven by various internal processes, the least understood of which is the ultra-low frequency (ULF) one. Recently published research has shown that the HRV frequency distribution in this range is bimodal. The main aims of this [...] Read more.
Heart rate variability (HRV) comprises several components driven by various internal processes, the least understood of which is the ultra-low frequency (ULF) one. Recently published research has shown that the HRV frequency distribution in this range is bimodal. The main aims of this work were to verify this finding, to determine the basic characteristics of these two components and to analyze their potential physiological couplings. For this purpose, two components within the conventional ULF band (below 4 mHz) were extracted from HRVs of 25 patients with apnea using adaptive variational mode decomposition (AVMD) and continuous wavelet transform (CWT), and then analyzed with the Hilbert transform (HT), Savitzky–Golay filter, and empirical distributions of instantaneous amplitudes and frequencies. These studies have demonstrated the existence of both components in HRVs of all subjects and apnea groups: extremely low frequencies (ELFs) in the range of 0.01–0.4 mHz and narrowed ultra-low frequencies (nULFs) in the range of 0.1–4 mHz. The independence of both components is also shown. Concluding, heart rate variability is separately regulated by circadian rhythms (ELF bound) and ultradian fluctuations (nULF bound), which can be assessed by decomposing HRV, and the obtained components may be helpful to better understand the underlying homeostatic mechanisms, as well as in the long-term monitoring of patients. Full article
(This article belongs to the Special Issue Data Processing in Biomedical Devices and Sensors)
►▼ Show Figures

Figure 1

18 pages, 2564 KB  
Article
Electromyographic Identification of the Recurrent Laryngeal Nerve Using an Integrated Hardware–Software System During Thyroid Surgery
by Mykola Dyvak, Andriy Melnyk, Volodymyr Tymets, Andriy Dyvak, Arkadiusz Banasik, Karol Piotrowski and Marcin Wawryszczuk
Appl. Sci. 2025, 15(18), 10009; https://doi.org/10.3390/app151810009 - 12 Sep 2025
Viewed by 1205
Abstract
The paper presents a hardware and software complex for monitoring the recurrent laryngeal nerve (RLN) with electromyography (EMG) as the primary tool, observing the RLN’s response to stimulation during intraoperative nerve monitoring (IONM). As a result of the analysis of available IONM tools [...] Read more.
The paper presents a hardware and software complex for monitoring the recurrent laryngeal nerve (RLN) with electromyography (EMG) as the primary tool, observing the RLN’s response to stimulation during intraoperative nerve monitoring (IONM). As a result of the analysis of available IONM tools using EMG, it was found that electromyography is an accurate and safe method for monitoring the bioelectric activity of the vocal cords. The article proposes a concept for monitoring the recurrent laryngeal nerve (RLN) by observing changes in the bioelectric activity of the vocal cords during RLN stimulation. A hardware and software complex was developed in accordance with the concept. The article presents the architecture of the hardware and software of this complex. A detailed description of all hardware parts, their purpose, and their interaction is given. Features of the software and tools used in its development are described. The results of the approval of the complex during thyroid surgery at the VITASANA Medical Center in the city of Ternopil are given. The complex could successfully register and record the change in the biometric potential of the vocal cord at the moment of stimulation of the RLN. Full article
(This article belongs to the Special Issue Data Processing in Biomedical Devices and Sensors)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop