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Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition

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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 746

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Special Issue Information

Dear Colleagues,

Wearable and unobtrusive technologies are revolutionizing personal care services, as well as the screening, prevention, and management of chronic diseases. A range of patients and users may benefit from wearable and unobtrusive technologies for monitoring the progression of pathologies, facilitating early detection and diagnosis of life-threatening diseases and stress levels, assessing the efficacy of administered therapies, providing low-cost and non-invasive diagnoses, and monitoring relevant or vital signals, even remotely.

This Special Issue is focused on wearable sensors and devices, unobtrusive technologies, and applications in the healthcare/wellness fields to improve the safety, effectiveness, and efficiency of healthcare services in acute and chronic conditions, but also for prevention with the aim of a healthy life and active aging. We strongly encourage the submission of papers focusing on the keywords below, but works on related topics may also be considered.

Dr. Carlo Massaroni
Dr. Emiliano Schena
Prof. Dr. Domenico Formica
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 sensors and technologies for medical applications
wearable sensors and technologies for physiological parameter monitoring
wearable and technologies sensors for applications in neuroscience
implantable sensors and devices
environmental sensors and devices for healthcare applications
body area sensor networks for medical applications
sensors for continuous patient monitoring
sensors for remote healthcare applications
metrological assessment of wearable and unobtrusive sensors

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Published Papers (1 paper)

Research

17 pages, 2527 KiB

Open AccessArticle

Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson’s Disease Using Machine Learning

by Rene Peter Bremm, Lukas Pavelka, Maria Moscardo Garcia, Laurent Mombaerts, Rejko Krüger and Frank Hertel

Sensors 2024, 24(7), 2195; https://doi.org/10.3390/s24072195 - 29 Mar 2024

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Abstract

Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson’s disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)

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