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Wearable Sensors and Signal Processing Technology for Digital Health Applications

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 6275

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Prof. Dr. Shing-Hong Liu

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Guest Editor

Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
Interests: cardiovascular measurement system; biomedical signal process; deep learning; machine learning; wearable device for digital health
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xin Zhu

E-Mail Website
Guest Editor

Department of AI Technology Development, M&D Data Science Center, Institute of Integrated Research, Institute of Science Tokyo, Tokyo, Japan
Interests: medical image and signal processing; cardiac modeling and simulation; medical instrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past decade, wearable devices that can be employed in digital health to measure physiological signals or parameters, such as in electrocardiograms, photoplethysmograms, blood pressure, and the saturation of peripheral oxygen, have been developed to monitor the physical or mental conditions of users in real time. However, these measurement environments are more complex than clinical practices. Moreover, users may be moving or active. Therefore, processing these signals remains challenging. Deep learning and machine learning have proven advantageous in speech processing, and ARM microprocessor cores have advanced significantly. This Special Issue focuses on the application of deep learning or machine learning to process the physiological signals measured by wearable devices and enhance their performance. Moreover, the proposed methods can be implemented on edge devices.

Prof. Dr. Shing-Hong Liu
Prof. Dr. Xin Zhu
Guest Editors

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Keywords

digital health
wearable device
physiological signal and parameter
deep learning
machine learning
edge computing

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Published Papers (5 papers)

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Research

22 pages, 8609 KB

Open AccessArticle

Upper Limb Tremors Classification for Parkinson’s Disease Using W-Band (76–81 GHz) Doppler Millimeter-Wave Sensing and Deep-Learning-Based Classifier

by Pi-Yun Chen, Chun-Yu Lin, Neng-Sheng Pai, Ping-Tzan Huang, Chao-Lin Kuo, Chien-Ming Li and Chia-Hung Lin

Sensors 2026, 26(12), 3955; https://doi.org/10.3390/s26123955 - 22 Jun 2026

Viewed by 386

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder with an increasing incidence rate that significantly affects patients’ motor functions and quality of life. Involuntary upper limb tremors (ULTs) commonly manifest unilaterally, affecting either the left or right upper limb. Clinically, ULT frequencies can be categorized into three distinct classes: low-frequency (<4.0 Hz), mid-frequency (4.0–7.0 Hz), and high-frequency (>7.0 Hz) tremors. These tremor motions are characterized by oscillatory or rotational (angular displacement) movements, commonly referred to as the micro-Doppler effect (mDE). This study aims to develop a short-range (<1.0 m) and contactless sensing method for ULT detection based on Doppler millimeter-wave (mm-Wave) radar. The reflected electromagnetic waves indicate time-varying frequency characteristics, which can be analyzed by using time–frequency transform (TFT) methods, such as the Wigner–Ville distribution (WVD) and smoothed pseudo WVD (SPWVD). These TFT methods are employed to extract mDE features, which are subsequently visualized as color-coded spectrograms for ULT classification. Then, a two-dimensional (2D) convolutional neural network (CNN) is employed to automatically recognize the visual feature patterns for ULTs classification based on frequency and amplitude information. In the experimental setup, the W-band (76–81 GHz) Doppler mm-Wave biosensor is implemented for sensing and extracting feature patterns. The proposed classifiers based on “WVD + 2D CNN” and “SPWVD + 2D CNN” are trained and validated by using the collected datasets, with 60% randomly selected for training datasets and 40% for testing datasets in each fold validation. A 10-fold cross-validation method is applied to evaluate the classifier’s performances, achieving an average precision of 95.92 ± 0.60%, average recall of 95.89 ± 0.62%, average F1-score of 0.9588 ± 0.0060, and average accuracy of 95.89 ± 0.62%, respectively. The experimental results demonstrate the feasibility of the proposed classifier for real-time ULTs classification in PD patients using short-range (<1.0 m) and contactless sensing. Full article

(This article belongs to the Special Issue Wearable Sensors and Signal Processing Technology for Digital Health Applications)

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13 pages, 233 KB

Open AccessArticle

Wearable-Measured Physical Activity Goal Adherence and Body Composition Change in a 12-Month mHealth Weight Loss Trial

by Zhadyra Bizhanova, Lora E. Burke, Maria M. Brooks, Bonny Rockette-Wagner, Jacob K. Kariuki and Susan M. Sereika

Sensors 2026, 26(10), 3256; https://doi.org/10.3390/s26103256 - 21 May 2026

Viewed by 468

Abstract

Background: Wearable activity trackers are commonly used in mHealth weight loss interventions, but evidence linking adherence to moderate-to-vigorous physical activity (MVPA) goals with changes in body composition is limited. We examined associations between adherence to study-prescribed MVPA goals and changes in percent body fat and sex-specific waist circumference (WC) over 12 months in the SMARTER trial. Methods: Participants (N = 502, 79.5% female; mean age 45 years; mean BMI 33.7 kg/m²) were randomized to self-monitoring of diet, PA, and weight (SM) or SM plus daily tailored feedback messages (SM + FB). Weekly adherence to ≥300 min/week of MVPA was quantified using Fitbit-derived equivalents. Associations between MVPA adherence and changes in percent body fat and sex-specific WC over 12 months were examined using linear mixed models. Results: Among the full sample, greater MVPA adherence was associated with reductions in body fat (b = −0.01; 95% CI: −0.02, −0.005), but not in WC (women: b = −0.01; −0.03, 0.01; men: b = −0.03; −0.05, 0.0002). Among the completers, higher adherence was associated with decreases in body fat (b = −0.01; −0.02, −0.004) and WC (women: b = −0.02; −0.04, −0.004; men: b = −0.04; −0.08, −0.003). Conclusions: Higher MVPA adherence was associated with favorable changes in adiposity over 12 months, supporting the use of wearable-derived PA measures in long-term mHealth behavioral interventions. Full article

(This article belongs to the Special Issue Wearable Sensors and Signal Processing Technology for Digital Health Applications)

21 pages, 11945 KB

Open AccessArticle

Denoising Respiratory Sinus Arrhythmia of Pulse-to-Pulse Interval Signals Extracted from Photoplethysmogram with an Autoregressive Moving Average Model

by Shing-Hong Liu, Chien-Kai Lin, Xin Zhu, Jia-Jung Wang, Yu-Lun Hsu and Kuo-Li Pan

Sensors 2026, 26(10), 3048; https://doi.org/10.3390/s26103048 - 12 May 2026

Viewed by 527

Abstract

Background: Pulse rate variability (PRV), a critical biomarker of autonomic nervous system (ANS) function, is typically evaluated using the pulse-to-pulse interval (PPI) signal extracted from a photoplethysmogram (PPG). Although PPGs have been widely used in wearable devices, the PPI signal is easily affected by motion artifacts or respiratory sinus arrhythmias (RSAs). These disturbances affect the accuracy of PRV for evaluating ANS function. The aim of this study was to remove the respiratory signals from raw PPI signals with an autoregressive moving average (ARMA) model. Methods: An R-wave to R-wave interval (RRI) sequence was extracted from the electrocardiogram (ECG). A self-made measurement system was used to record PPG, ECG, and respiratory signals. Nineteen healthy adults were recruited and requested to breathe with a spontaneous breathing rate (SBR) and control breathing rates (CBRs) (6, 18, and 30 breathing rate per minute, BRPM). Their ECG, PPG, and breathing signals were recorded for 6 min under different CBRs. The measurement was performed twice, i.e., eight measurements were performed. The raw RRI(t) and PPI(t) signals of 4 Hz were segmented into samples of one minute and shifted by 30 s. Thus, a subject had 80 samples, and there were 10 samples for each BRPM. RSA-free RRI signals were generated by a spectral method to filter RSA from raw RRI(t) to produce the target RRI(t). We proposed the individual subject ARMA models trained by samples with the maximum mean absolute errors between the target RRI(t) and raw PPI(t) (MAE_RAWs) of each subject, and the general model trained by samples with all maximum MAE_RAWs of all 19 subjects. Results: The mean absolute errors between the target RRI(t) and

\tilde{P P I} (t)

predicted by the individual subject ARMA models (MAE_{Subject-Models}) and general ARMA model (MAE_{General-Model}) were used to evaluate the performance of the two models. The results for the MAE_{Subject-Models} and MAE_{General-Model} were 132.5 ± 59.1 ms and 137.8 ± 67.8 ms, respectively, with no significant difference. MAE_{Subject-Models} and MAE_{General-Model} were compared with MAE_RAWs, whose attenuations (ATTs) were 28.5 ± 13.1% and 27.8 ± 12.6%, respectively. Conclusions: The two proposed models are capable of removing the RSA energy coupled in the raw PPI signals. Full article

(This article belongs to the Special Issue Wearable Sensors and Signal Processing Technology for Digital Health Applications)

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23 pages, 2836 KB

Open AccessArticle

Ergo4Workers: A User-Centred App for Tracking Posture and Workload in Healthcare Professionals

by Inês Sabino, Maria do Carmo Fernandes, Ana Antunes, António Monteny, Bruno Mendes, Carlos Caldeira, Isabel Guimarães, Nidia Grazina, Phillip Probst, Cátia Cepeda, Cláudia Quaresma, Hugo Gamboa, Isabel L. Nunes and Ana Teresa Gabriel

Sensors 2025, 25(18), 5854; https://doi.org/10.3390/s25185854 - 19 Sep 2025

Viewed by 1481

Abstract

Healthcare professionals (namely, occupational therapists) face ergonomic risk factors that may lead to work-related musculoskeletal disorders (WRMSD). Ergonomic assessments are crucial to mitigate this occupational issue. Wearable devices are a potential solution for such assessments, providing continuous measurement of biomechanical and physiological parameters. Ergo4workers (E4W) is a mobile application designed to integrate data from independent wearable sensors—motion capture system, surface electromyography, force platform, and smartwatch—to provide an overview of the posture and workload of occupational therapists. It can help identify poor work practices and raise awareness about ergonomic risk factors. This paper describes the development of E4W by following a User-Centred Design (UCD) approach. The initial stage focused on specifying the context of use in collaboration with six occupational therapists. Then the app was implemented using WordPress. Three iterations of the UCD cycle were performed. The usability test of prototype 1 was carried out in a laboratory environment, while the others were tested in a real healthcare work environment. The Cognitive Walkthrough was applied in the usability tests of prototypes 1 and 2. The System Usability Scale evaluated prototype 3. Results evidenced positive feedback, reflecting an easy-to-use and intuitive smartphone app that does not interfere with daily work activities. Full article

(This article belongs to the Special Issue Wearable Sensors and Signal Processing Technology for Digital Health Applications)

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20 pages, 1981 KB

Open AccessArticle

Compact Colocated Bimodal EEG/fNIRS Multi-Distance Sensor

by Frédéric Hameau, Anne Planat-Chrétien, Sadok Gharbi, Robinson Prada-Mejia, Simon Thomas, Stéphane Bonnet and Angélique Rascle

Sensors 2025, 25(17), 5520; https://doi.org/10.3390/s25175520 - 4 Sep 2025

Cited by 2 | Viewed by 2426

Abstract

At present, it is a real challenge to measure brain signals outside of the lab with portable systems that are robust, comfortable and easy to use. We propose in this article a bimodal electroencephalography–functional near-infrared spectroscopy (EEG-fNIRS) sensor whose spatial geometry allows the robust estimation of colocated electrical and hemodynamic brain activity. The geometry allows for the correction of extra-cerebral activity (short-channel distance) as well as the computation of the spatial gradient of absorbance required in the spatially resolved spectroscopy (SRS) method. The complete system is described, detailing the technical solutions implemented to provide signals at 250 Hz for both synchronized modalities and without crosstalk. The system performances are validated during an N-Back mental workload protocol. Full article

(This article belongs to the Special Issue Wearable Sensors and Signal Processing Technology for Digital Health Applications)

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