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Advances in Wearable technology for Biomedical Monitoring
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Advances in Wearable technology for Biomedical Monitoring

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2494

Special Issue Editors

Prof. Dr. Colin K. Drummond

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Guest Editor
Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA
Interests: practical application of simulation and healthcare information technology to support clinical decision making; including advances in understanding wearable analytics for human performance assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Dhruv R. Seshadri

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Guest Editor
Sports Medicine Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
Interests: wearable tech; digital health; bioelectronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The convergence of novel detection capabilities, edge computing, and remote monitoring has ushered in a new era for wearable technologies. The ability to non-invasively monitor and detect digital biomarkers from a biomechanical, physiological, and biochemical perspective has the potential to provide key internal and external continuous data to complement clinical decision making. At the forefront lies the need to assess the efficacy of wearable technologies before their adoption by patients. Our goals with this Special Issue are the following:

  1. Highlight critical path elements required to translate wearable technologies to improve patient satisfaction and outcomes, increase adherence in clinical trials, and decrease nurse burden.
  2. Review clinical trials involving the use of wearable technology as a complementary diagnostic to augment clinical decision making.
  3. To provide insight into and forecast how wearable technology and pharmaceutical companies can collaborate towards quantifying the efficacy of therapeutics.

In this Special Issue, we solicit opinion articles, editorials, case studies, primary data, systematic reviews, meta analyses, and narrative reviews that discuss how wearable technology can inform clinical decision making.

Prof. Dr. Colin K. Drummond
Dr. Dhruv R. Seshadri
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

  • wearable technology
  • digital biomarkers
  • companion diagnostics
  • sensors
  • clinical decision making
  • patient outcomes
  • remote monitoring

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

Published Papers (2 papers)

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Research

13 pages, 1759 KiB  
Article
Potential of a New, Flexible Electrode sEMG System in Detecting Electromyographic Activation in Low Back Muscles during Clinical Tests: A Pilot Study on Wearables for Pain Management
by Antoine Frasie, Hugo Massé-Alarie, Mathieu Bielmann, Nicolas Gauthier, Mourad Roudjane, Isabelle Pagé, Benoit Gosselin, Jean-Sébastien Roy, Younes Messaddeq and Laurent J. Bouyer
Sensors 2024, 24(14), 4510; https://doi.org/10.3390/s24144510 - 12 Jul 2024
Viewed by 630
Abstract
Background: While low back pain (LBP) is the leading cause of disability worldwide, its clinical objective assessment is currently limited. Part of this syndrome arises from the abnormal sensorimotor control of back muscles, involving increased muscle fatigability (i.e., assessed with the Biering–Sorensen test) [...] Read more.
Background: While low back pain (LBP) is the leading cause of disability worldwide, its clinical objective assessment is currently limited. Part of this syndrome arises from the abnormal sensorimotor control of back muscles, involving increased muscle fatigability (i.e., assessed with the Biering–Sorensen test) and abnormal muscle activation patterns (i.e., the flexion–extension test). Surface electromyography (sEMG) provides objective measures of muscle fatigue development (median frequency drop, MDF) and activation patterns (RMS amplitude change). This study therefore assessed the sensitivity and validity of a novel and flexible sEMG system (NSS) based on PEVA electrodes and potentially embeddable in textiles, as a tool for objective clinical LBP assessment. Methods: Twelve participants wearing NSS and a commercial laboratory sEMG system (CSS) performed two clinical tests used in LBP assessment (Biering–Sorensen and flexion–extension). Erector spinae muscle activity was recorded at T12-L1 and L4-L5. Results: NSS showed sensitivity to sEMG changes associated with fatigue development and muscle activations during flexion–extension movements (p < 0.05) that were similar to CSS (p > 0.05). Raw signals showed moderate cross-correlations (MDF: 0.60–0.68; RMS: 0.53–0.62). Adding conductive gel to the PEVA electrodes did not influence sEMG signal interpretation (p > 0.05). Conclusions: This novel sEMG system is promising for assessing electrophysiological indicators of LBP during clinical tests. Full article
(This article belongs to the Special Issue Advances in Wearable technology for Biomedical Monitoring)
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18 pages, 1047 KiB  
Article
A Comprehensive Study on Pain Assessment from Multimodal Sensor Data
by Manuel Benavent-Lledo, David Mulero-Pérez, David Ortiz-Perez, Javier Rodriguez-Juan, Adrian Berenguer-Agullo, Alexandra Psarrou and Jose Garcia-Rodriguez
Sensors 2023, 23(24), 9675; https://doi.org/10.3390/s23249675 - 7 Dec 2023
Cited by 1 | Viewed by 1332
Abstract
Pain assessment is a critical aspect of healthcare, influencing timely interventions and patient well-being. Traditional pain evaluation methods often rely on subjective patient reports, leading to inaccuracies and disparities in treatment, especially for patients who present difficulties to communicate due to cognitive impairments. [...] Read more.
Pain assessment is a critical aspect of healthcare, influencing timely interventions and patient well-being. Traditional pain evaluation methods often rely on subjective patient reports, leading to inaccuracies and disparities in treatment, especially for patients who present difficulties to communicate due to cognitive impairments. Our contributions are three-fold. Firstly, we analyze the correlations of the data extracted from biomedical sensors. Then, we use state-of-the-art computer vision techniques to analyze videos focusing on the facial expressions of the patients, both per-frame and using the temporal context. We compare them and provide a baseline for pain assessment methods using two popular benchmarks: UNBC-McMaster Shoulder Pain Expression Archive Database and BioVid Heat Pain Database. We achieved an accuracy of over 96% and over 94% for the F1 Score, recall and precision metrics in pain estimation using single frames with the UNBC-McMaster dataset, employing state-of-the-art computer vision techniques such as Transformer-based architectures for vision tasks. In addition, from the conclusions drawn from the study, future lines of work in this area are discussed. Full article
(This article belongs to the Special Issue Advances in Wearable technology for Biomedical Monitoring)
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