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Advanced Wearable Sensors Technologies for Healthcare Monitoring: 2nd Edition

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Special Issue Editor

Special Issue Information

Dear Colleagues,

Wearable sensor technologies are rapidly evolving and expanding to critical applications of wellness and healthcare, being driven by advances in sensor technology, computing, wireless communications, signal processing, and pattern recognition. Wearable technologies allow the extension of health monitoring into the community and have been used in many research and clinical applications, including monitoring of healthy, elderly and frail individuals, individuals with neurological disorders (stroke, Parkinson’s disease, etc.), measuring levels of physical activity in disease association studies, and developing behavioral interventions.

The goal of this Special Issue is to highlight state-of-the-art applications of wearable sensors with a focus on wellness and healthcare applications of the technology. We welcome submissions of any original unpublished work on the listed topics below.

Topics may include, but are not limited to, the following:

new sensor materials and technologies for medical applications;
wearable and implantable sensors for biomedical applications;
sensing systems for healthcare;
sensors and Systems for Physical Rehabilitation;
wearable sensors;
connected sensors for the Internet of Medical Things and Health Things;
physical activity;
activity monitoring;
emotion prediction;
stress detection;
fatigue detection;
fall detection;
sport-related activity monitoring;
health monitoring;
pervasive healthcare;
non-contact physiological sensors.

Prof. Dr. Toshiyo Tamura
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. 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

new sensor materials and technologies for medical applications
wearable and implantable sensors for biomedical applications
sensing systems for healthcare
sensors and systems for physical rehabilitation
wearable sensors
connected sensors for the Internet of Medical Things and Health Things
physical activity
activity monitoring
emotion prediction
stress detection
fatigue detection
fall detection
sport-related activity monitoring
health monitoring
pervasive healthcare
non-contact physiological sensors

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Research

21 pages, 7867 KB

Open AccessArticle

Electromyography (EMG) Signal Processing to Evaluate Low-Frequency Tremors

by Samantha O’Sullivan, Mark Daly, Niall Murray and Thiago Braga Rodrigues

Sensors 2026, 26(1), 157; https://doi.org/10.3390/s26010157 - 25 Dec 2025

Viewed by 872

Abstract

Objective quantification of tremor remains a challenge in Parkinson’s disease (PD) assessment, with current clinical assessments relying largely on subjective scale ratings. This study evaluates the feasibility and signal behaviour of integrating surface electromyography (sEMG) with MDS-UPDRS-aligned tasks in a healthy adult cohort, with the aim of establishing normative low-frequency muscle activation profiles. Thirty-two healthy participants (mean age 27.6 ± 5.3 years) completed seven upper-limb tasks derived from the MDS-UPDRS while sEMG data were recorded from antagonistic forearm muscles. Signals were normalised using maximum voluntary contraction, filtered at 14 Hz, and analysed using frequency-domain (FFT) and time-frequency (STFT) methods. Significant task-dependent differences were observed in both frequency occurrence and magnitude (p < 0.05), particularly within the 3.5–9 Hz range. Finger tapping elicited increased low-frequency activity compared to baseline, while pronation–supination produced the most stable and consistent muscle activation across participants. Frequencies above 12 Hz showed minimal task discrimination. These findings demonstrate that low-frequency tremor-like activity can occur during specific MDS-UPDRS tasks in healthy individuals and may require further validation before being considered suitable for PD staging. This work establishes normative sEMG benchmarks to support future clinical validation and PD cohort comparisons. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring: 2nd Edition)

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