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Signals
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Emerging Trends in Deep Learning and Signal Processing for Wearable...
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Emerging Trends in Deep Learning and Signal Processing for Wearable Biomedical Signal Analysis

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: 30 July 2025 | Viewed by 1632

Special Issue Editors

Dr. Hugo F. Posada-Quintero

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: electro-physiological signals; electrodermal activity; heart rate variability; electromyography; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Yedukondala Rao Veeranki

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: nonlinear signal processing; electrodermal activity; electromyography; Electroencephalogram; machine learning; deep learning.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the domain of physiological signal processing, the integration of advanced signal processing methodologies across time, frequency, time-frequency, and non-linear domains has emerged as a pivotal area of research. This Special Issue aims to offer an interdisciplinary platform for the dissemination of innovative research, methodologies, and applications related to the analysis of complex physiological signals. The Issue is designed to explore and elucidate the application of cutting-edge signal processing techniques in the analysis of a spectrum of biomedical signals, encompassing electrodermal activity (EDA), electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), photoplethysmogram (PPG), as well as wearable sensor data and associated imaging modalities.

Moreover, the burgeoning synergy between deep learning algorithms in the domain of physiological signal classification, feature extraction, and predictive modeling has catalyzed advancements in terms of diagnostic and monitoring capabilities. This Special Issue aims to spotlight the advancements and challenges in the development and implementation of deep learning methodologies tailored for the analysis of physiological signals, with a specific emphasis on wearable sensor data. We warmly invite researchers, academics, and professionals to contribute their original research articles, comprehensive reviews, and concise communications, focusing on the latest innovations, methodological advancements, and future directions in the integration of advanced signal processing and deep learning paradigms for biomedical signal analysis, particularly in the context of wearable sensor technologies.

Dr. Hugo F. Posada-Quintero
Dr. Yedukondala Rao Veeranki
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. Signals is an international peer-reviewed open access quarterly 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 1000 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

  • physiological signal processing
  • wearable sensors
  • deep learning
  • electrodermal activity
  • electrocardiogram
  • electromyogram
  • electroencephalogram photoplethysmogram

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Published Papers (1 paper)

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Research

12 pages, 470 KiB  
Article
Effects of Inertial Measurement Unit Location on the Validity of Vertical Acceleration Time-Series Data and Jump Height in Countermovement Jumping
by Dianne Althouse, Cassidy Weeks, Steven B. Spencer, Joonsun Park, Brennan J. Thompson and Talin Louder
Signals 2025, 6(1), 11; https://doi.org/10.3390/signals6010011 - 3 Mar 2025
Viewed by 851
Abstract
Inertial measurement units (IMUs) are an example of practical technology for measuring countermovement jump (CMJ) performance, but there is a need to enhance their validity. One potential strategy to achieve this is advancing the literature on IMU placement. Many studies opt to position [...] Read more.
Inertial measurement units (IMUs) are an example of practical technology for measuring countermovement jump (CMJ) performance, but there is a need to enhance their validity. One potential strategy to achieve this is advancing the literature on IMU placement. Many studies opt to position a single IMU on anatomical landmarks rather than determining placement based on anthropometric principles, despite the knowledge that linear mechanics act through the segmental centers of mass of the human body. The purpose of this study was to evaluate the impact of positioning IMU sensors to approximate the trunk and lower-extremity segmental centers of mass on the validity of vertical acceleration measurements and jump height (JH) estimation during CMJs. Thirty young adults (female n = 10, 21.3 (3.8) years, 166.1 (4.1) cm, 67.6 (11.3) kg; male n = 20, 22.0 (2.6) years, 179.2 (6.4) cm, 83.5 (17.1) kg) from a university setting participated in the study. Seven IMUs were positioned at the approximate centers of mass of the trunk, thighs, shanks, and feet. Using data from these sensors, 15 whole-body center of mass models were developed, including 1-, 2-, 3-, and 4-segment configurations derived from the trunk and three lower-body segments. The root mean square error (RMSE) of vertical acceleration was calculated for each IMU model by comparing its data against vertical acceleration measurements obtained from a force platform. JH estimates were calculated using the take-off velocity method and compared across IMU models and the force platform to evaluate for systematic bias. RMSE and JH values from the best-performing 1-, 2-, 3-, and 4-segment IMU models were analyzed for main effects using one-way analyses of variance. The best performing 2-segment (trunk and shanks; RMSE = 2.1 ± 1.3 m × s−2) and 3-segment (trunk, thighs, and feet; RMSE = 2.0 ± 1.2 m × s−2) IMU models returned significantly lower RMSE values compared to the 1- segment (trunk; RMSE = 3.0 ± 1.4 m × s−2) model (p = 0.021–0.041). No systematic bias was detected between the JH estimates derived from the best-performing IMU models and those obtained from the force platform (p = 0.91–0.99). Positioning multiple IMU sensors to approximate segmental centers of mass significantly improved the validity of vertical acceleration time-series data from CMJs. The findings highlight the importance of anthropometric-based IMU placement for enhancing measurement accuracy without introducing systematic bias. Full article
(This article belongs to the Special Issue Emerging Trends in Deep Learning and Signal Processing for Wearable Biomedical Signal Analysis)
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