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Innovation on Wearable Sensors and Algorithms for Physiological Monitoring

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 8954

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Special Issue Editor

Dr. Antonis A. Armoundas

E-Mail Website
Guest Editor

1. Medicine, Harvard Medical School, Boston, MA 02115, USA
2. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02129, USA
Interests: biomedical signal processing; machine learning algorithms; wearable sensors; physiological monitoring

Special Issue Information

Dear Colleagues,

The rapidly evolving digital healthcare ecosystem requires evidence-based digital healthcare innovations that can effectively, efficiently, and safely be deployed at the point of care. The prolific emergence of new technological innovations (e.g., in wearable technologies, biomedical informatics and data science, drawing upon mathematics, statistics, information science, computer science and engineering, and social/behavioral sciences), both in wearable sensors as well as in their supporting algorithms, employed by both the healthcare team and patients, is transforming care delivery models and impacting care delivery and patient care experiences, having the potential to revolutionize the way care is delivered. The use of these solutions can improve productivity, reduce errors, enhance safety, and increase opportunities for patient engagement and shared decision making. Wearable-sensor-based bioengineering approaches that integrate principles from diverse technical and biomedical fields, may provide new understanding, innovative technologies, and new products that improve basic knowledge, human health, and quality of life, of patients with chronic diseases. As patient self-management requires that patients take progressively more responsibility for their day-to-day care, thus becoming a lifelong task in situations of chronic conditions, the development of new patient-friendly and accepted wearable monitoring technologies increases the acceptance and uptake of patient monitoring of chronic conditions and self-management and eventually becomes essential in improving patient outcomes and lowering health care costs.

Fit with the scope of Sensors: The proposed thematic series of papers on “Innovation on Wearable Sensors and Algorithms for Physiological Monitoring” aims to present the state of the art on novel wearable sensor technology and algorithms, opening new possibilities in improving patient monitoring and outcomes.

Dr. Antonis A. Armoundas
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.

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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

digital healthcare
wearable
ambulatory
monitoring
chronic conditions
algorithms
biomedical informatics
data science

Published Papers (3 papers)

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Research

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23 pages, 12172 KiB

Open AccessArticle

Open-Source Strain Gauge System for Monitoring Pressure Distribution of Runner’s Feet

by Klaudia Kromołowska, Krzysztof Kluza, Eliasz Kańtoch and Piotr Sulikowski

Sensors 2023, 23(4), 2323; https://doi.org/10.3390/s23042323 - 19 Feb 2023

Cited by 2 | Viewed by 2415

Abstract

The objective of the research presented in this paper was to provide a novel open-source strain gauge system that shall enable the measurement of the pressure of a runner’s feet on the ground and the presentation of the results of that measurement to the user. The system based on electronic shoe inserts with 16 built-in pressure sensors laminated in a transparent film was created, consisting of two parts: a mobile application and a wearable device. The developed system provides a number of advantages in comparison with existing solutions, including no need for calibration, an accurate and frequent measurement of pressure distribution, placement of electronics on the outside of a shoe, low cost, and an open-source approach to encourage enhancements and open collaboration. Full article

(This article belongs to the Special Issue Innovation on Wearable Sensors and Algorithms for Physiological Monitoring)

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21 pages, 2135 KiB

Open AccessArticle

Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors

by Sarah Alhassan, Adel Soudani and Manan Almusallam

Sensors 2023, 23(4), 2228; https://doi.org/10.3390/s23042228 - 16 Feb 2023

Cited by 5 | Viewed by 2452

Abstract

The deployment of wearable wireless systems that collect physiological indicators to aid in diagnosing neurological disorders represents a potential solution for the new generation of e-health systems. Electroencephalography (EEG), a recording of the brain’s electrical activity, is a promising physiological test for the diagnosis of autism spectrum disorders. It can identify the abnormalities of the neural system that are associated with autism spectrum disorders. However, streaming EEG samples remotely for classification can reduce the wireless sensor’s lifespan and creates doubt regarding the application’s feasibility. Therefore, decreasing data transmission may conserve sensor energy and extend the lifespan of wireless sensor networks. This paper suggests the development of a sensor-based scheme for early age autism detection. The proposed scheme implements an energy-efficient method for signal transformation allowing relevant feature extraction for accurate classification using machine learning algorithms. The experimental results indicate an accuracy of 96%, a sensitivity of 100%, and around 95% of F1 score for all used machine learning models. The results also show that our scheme energy consumption is 97% lower than streaming the raw EEG samples. Full article

(This article belongs to the Special Issue Innovation on Wearable Sensors and Algorithms for Physiological Monitoring)

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Review

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15 pages, 1740 KiB

Open AccessReview

Wearables in Nephrology: Fanciful Gadgetry or Prêt-à-Porter?

by Madelena Stauss, Htay Htay, Jeroen P. Kooman, Thomas Lindsay and Alexander Woywodt

Sensors 2023, 23(3), 1361; https://doi.org/10.3390/s23031361 - 26 Jan 2023

Cited by 1 | Viewed by 3437

Abstract

Telemedicine and digitalised healthcare have recently seen exponential growth, led, in part, by increasing efforts to improve patient flexibility and autonomy, as well as drivers from financial austerity and concerns over climate change. Nephrology is no exception, and daily innovations are underway to provide digitalised alternatives to current models of healthcare provision. Wearable technology already exists commercially, and advances in nanotechnology and miniaturisation mean interest is also garnering clinically. Here, we outline the current existing wearable technology pertaining to the diagnosis and monitoring of patients with a spectrum of kidney disease, give an overview of wearable dialysis technology, and explore wearables that do not yet exist but would be of great interest. Finally, we discuss challenges and potential pitfalls with utilising wearable technology and the factors associated with successful implementation. Full article

(This article belongs to the Special Issue Innovation on Wearable Sensors and Algorithms for Physiological Monitoring)

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