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Sensors for Physiological Monitoring and Digital Health: 2nd Edition
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Sensors for Physiological Monitoring and Digital Health: 2nd Edition

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

Deadline for manuscript submissions: 25 September 2026 | Viewed by 6669

Special Issue Editors

Dr. Ganesh R. Naik

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Guest Editor
College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia
Interests: biomedical engineering; signal processing; sleep; cardio respiratory research; BCI; wearables
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Elena Pirogova

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Guest Editor
School of Engineering, STEM College, RMIT University, Melbourne 3000, Australia
Interests: biomedical engineering; bioelectromagnetics; peptide-based therapeutics; signal processing; bioengineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Margaret Lech

E-Mail Website
Guest Editor
School of Engineering, STEM College, RMIT University, Melbourne 3000, Australia
Interests: machine learning; signal processing; speech, image and biomedical signal processing and optimisation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Health monitoring that measures and evaluates physiological signals generated by the human body can provide detailed information about human wellness, thus presenting significant potential for personalized healthcare. There is a great need for the long-term monitoring of human vital physiological parameters, such as EEG, ECG, heart rate, etc., for elderly and chronic patients to take care of their health (effectively) and provide treatment during emergencies. Wearable sensors present an exciting opportunity to measure human physiologic parameters in a continuous, real-time, and nonintrusive manner. The market for wearable medical devices has experienced unprecedented growth, with an increase from USD 8.9 billion in 2018 to USD 29.9 billion in 2023. The fast market growth along with advancements in microfabrication, microelectronics, flexible electronics, nanomaterials, wireless communication, and machine learning techniques have led to the evolution of various biosensors and textile-based wearable technologies.

Physiological monitoring with digital health platforms using artificial intelligence (AI) can provide detailed information about health conditions, therefore presenting great potential for personalized healthcare. Digital health monitoring redefines healthcare in multiple ways. It plays a vital role in this transformation, allowing easy access to relevant data, improving quality of care, and delivering value to patients, healthcare practitioners, hospitals, and governments.

In this Special Issue, we want to build a bridge between different scientific disciplines and offer highly innovative researchers in various fields a platform to exchange research in this exciting and emerging field: “Sensors for Physiological Monitoring and Digital Health: 2nd Edition”.

We, the Guest Editors of this Special Issue, represent research backgrounds in biomedical signal processing, health informatics, artificial intelligence, mobility research, and bioinformatics, focusing on biomedical applications and sports science. We stand for the highly interdisciplinary approach that is essential in research in this emerging scientific field and highly anticipate submissions from a broad range of specialties to this Special Issue.

Dr. Ganesh R. Naik
Prof. Dr. Elena Pirogova
Prof. Dr. Margaret Lech
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 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

  • wearables
  • physiological monitoring
  • digital health
  • healthcare
  • artificial intelligence
  • biomedical signal processing

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Related Special Issue

Published Papers (5 papers)

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Research

16 pages, 53570 KB  
Article
A Multimodal In-Ear Audio and Physiological Dataset for Swallowing and Non-Verbal Event Classification
by Elyes Ben Cheikh, Yassine Mrabet, Catherine Laporte and Rachel E. Bouserhal
Sensors 2026, 26(7), 2019; https://doi.org/10.3390/s26072019 - 24 Mar 2026
Viewed by 712
Abstract
Swallowing is a critical marker of neurological and emotional health. The ability to monitor it continuously and non-invasively, especially through smart ear-worn devices, holds significant promise for clinical applications. Despite this potential, no public audio datasets currently support reliable swallowing sound detection. Existing [...] Read more.
Swallowing is a critical marker of neurological and emotional health. The ability to monitor it continuously and non-invasively, especially through smart ear-worn devices, holds significant promise for clinical applications. Despite this potential, no public audio datasets currently support reliable swallowing sound detection. Existing datasets focus primarily on speech and breathing, offering limited coverage and lacking detailed annotations for swallowing events. To address this gap, we introduce an in-ear audio dataset specifically designed to capture a wide range of verbal and non-verbal sounds. It includes comprehensive labeling focused on swallowing. The dataset was collected from 34 healthy adults (14 females and 20 males) between the ages of 20 and 29. Each participant performed a series of predefined tasks involving both non-verbal and verbal events. Non-verbal tasks included swallowing, clicking, forceful blinking, touching the scalp, and physical movements such as squatting or walking in place. Verbal tasks consisted of speaking (e.g., describing an image). Recordings were conducted in both quiet and noisy environments to better reflect real-world conditions. Data were captured using a combination of in-/outer-ear microphones, a chest belt to record electrocardiogram (ECG), respiration and acceleration signals, and an ultrasound probe to track tongue movement, which served as a reference for swallowing annotation. All signals were precisely synchronized. To ensure high data quality, the recordings were reviewed using both algorithmic analysis and manual inspection. Swallowing events were identified based on ultrasound signals and validated by an expert to guarantee accurate labeling. As a proof of concept that in-ear audio supports swallow classification, we fine-tune a fully connected neural network on YAMNet embeddings plus zero-crossing rate (ZCR) features. Across the completed folds, the model reaches an F1 score of 0.875 ± 0.013. Full article
(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health: 2nd Edition)
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14 pages, 773 KB  
Article
Smartphone-Based Markerless Motion Capture for Spatiotemporal Gait Assessment: Applied Within-Session Reliability and Comparability of OpenCap Versus OptoGait
by Christopher James Keating, Matteo Vitarelli and Domenico Cherubini
Sensors 2026, 26(4), 1234; https://doi.org/10.3390/s26041234 - 13 Feb 2026
Viewed by 723
Abstract
Objective gait assessment is increasingly needed beyond specialized laboratories, and 3D markerless motion capture is emerging as a viable option; however, evidence regarding its applied repeatability and practical use for spatiotemporal gait outcomes in scalable clinical and field settings remains limited. This study [...] Read more.
Objective gait assessment is increasingly needed beyond specialized laboratories, and 3D markerless motion capture is emerging as a viable option; however, evidence regarding its applied repeatability and practical use for spatiotemporal gait outcomes in scalable clinical and field settings remains limited. This study evaluated the applied repeatability and practical comparability of OpenCap (camera-based; CM) versus a commonly accepted photoelectric walkway (OptoGait; OPT). Thirty-nine healthy adults completed three 10-m overground trials at self-selected speed. CM parameters were derived from OpenCap’s Advanced Overground Gait Analysis. Within-device reliability was good-to-excellent for gait speed, stride length, and cadence (ICC (3,1) = 0.734–0.920 OPT; 0.791–0.917 CM) and excellent when averaging three trials (ICC (3,3) = 0.892–0.972 OPT; 0.919–0.971 CM); double support showed lower reliability (ICC (3,1) = 0.527 OPT; 0.647 CM). Between devices, CM showed higher mean speed (+0.110 m/s), stride length (+0.127 m), and double support (+3.17% of the gait cycle), while cadence was very similar (−0.59 spm). Correlations were high for speed (r = 0.951), stride length (r = 0.864), and cadence (r = 0.983) but moderate for double support (r = 0.405); absolute-agreement ICCs were highest for cadence (0.980) and lowest for double support (0.271). OpenCap provides reliable within-session estimates for key spatiotemporal measures, but systematic bias indicates it should be used consistently as a standalone tool rather than interchangeably with OptoGait without device-specific correction or reference values. Full article
(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health: 2nd Edition)
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16 pages, 374 KB  
Article
Trajectories of Adherence to Study-Prescribed Physical Activity Goals in a mHealth Weight Loss Intervention
by Zhadyra Bizhanova, Lora E. Burke, Maria M. Brooks, Bonny Rockette-Wagner, Jacob K. Kariuki and Susan M. Sereika
Sensors 2025, 25(24), 7595; https://doi.org/10.3390/s25247595 - 15 Dec 2025
Viewed by 1535
Abstract
Introduction: Engaging in ≥300 min/week of moderate-to-vigorous physical activity (MVPA) is recommended for weight management. This study identified MVPA goal-adherence trajectories and associated predictors and weight outcomes in a 12-month mHealth weight-loss trial. Materials and Methods: This was a secondary analysis [...] Read more.
Introduction: Engaging in ≥300 min/week of moderate-to-vigorous physical activity (MVPA) is recommended for weight management. This study identified MVPA goal-adherence trajectories and associated predictors and weight outcomes in a 12-month mHealth weight-loss trial. Materials and Methods: This was a secondary analysis of valid PA data (≥4 days/week with ≥500 steps/day) from participants (age ≥ 18 years, BMI 27–43 kg/m2) randomized 1:1 to self-monitoring with tailored feedback or self-monitoring only. Both groups received Fitbit trackers. Group-based trajectory modeling identified adherence trajectories and baseline predictors. Analysis of variance was used to estimate associations between trajectory group membership and 12-month weight change. Results: Among 502 participants (79% female, 82% White, mean age of 45.0 ±14.4 years), four MVPA goal-adherence trajectories were identified: lower stable (34.5%), moderate (39.8%), increasing (19.3%), and high (6.4%). A graded association was observed with better adherence trajectories being associated with greater 12-month weight loss (p < 0.0001). Older age, male sex, being unpartnered, and higher first-week MVPA predicted membership in higher adherence trajectory groups (p < 0.05). Conclusions: Higher MVPA goal-adherence was related to greater weight loss. Early MVPA levels predicted long-term adherence, supporting the importance of personalized, technology-supported strategies to promote long-term PA adherence and inform targeted interventions to prevent chronic diseases. Full article
(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health: 2nd Edition)
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22 pages, 1326 KB  
Article
Data-Driven Phenotyping from Foot-Mounted IMU Waveforms: Elucidating Phenotype-Specific Fall Mechanisms
by Ryusei Sato and Takashi Watanabe
Sensors 2025, 25(24), 7503; https://doi.org/10.3390/s25247503 - 10 Dec 2025
Cited by 1 | Viewed by 1188
Abstract
A one-size-fits-all approach to fall risk assessment in older adults has critical limitations. This study aimed to overcome this by identifying distinct gait phenotypes and their specific fall mechanisms using foot-mounted IMU waveform data from 146 older adults (mean age 82.6 ± 6.2 [...] Read more.
A one-size-fits-all approach to fall risk assessment in older adults has critical limitations. This study aimed to overcome this by identifying distinct gait phenotypes and their specific fall mechanisms using foot-mounted IMU waveform data from 146 older adults (mean age 82.6 ± 6.2 years). A data-driven clustering algorithm identified four phenotypes (Robust, High-cadence, Intermediate, and Cautious), each with different fall prevalence rates (27–68%). Interpretable machine learning (SHAP) revealed that fall trajectories were phenotype-dependent. While physiological declines such as gait speed were the primary cause of falls in the Cautious group, fear of falling (FES-I) was the primary cause in the physically healthy Robust group, suggesting a psychological pathway. Consequently, the optimal Timed Up and Go (TUG) test screening cutoff varied across phenotypes, ranging from 11.95 s to 14.00 s, demonstrating the limitations of a one-size-fits-all approach. These findings demonstrate that fall mechanisms are phenotype-dependent, underscoring the necessity of a personalized assessment strategy to improve fall prevention. Full article
(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health: 2nd Edition)
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18 pages, 2578 KB  
Article
Emotion Recognition Using Temporal Facial Skin Temperature and Eye-Opening Degree During Digital Content Viewing for Japanese Older Adults
by Rio Tanabe, Ryota Kikuchi, Min Zou, Kenji Suehiro, Nobuaki Takahashi, Hiroki Saito, Takuya Kobayashi, Hisami Satake, Naoko Sato and Yoichi Kageyama
Sensors 2025, 25(21), 6545; https://doi.org/10.3390/s25216545 - 24 Oct 2025
Cited by 2 | Viewed by 2001
Abstract
Electroencephalography is a widely used method for emotion recognition. However, it requires specialized equipment, leading to high costs. Additionally, attaching devices to the body during such procedures may cause physical and psychological stress to participants. These issues are addressed in this study by [...] Read more.
Electroencephalography is a widely used method for emotion recognition. However, it requires specialized equipment, leading to high costs. Additionally, attaching devices to the body during such procedures may cause physical and psychological stress to participants. These issues are addressed in this study by focusing on physiological signals that are noninvasive and contact-free, and a generalized method for estimating emotions is developed. Specifically, the facial skin temperature and eye-opening degree of participants captured via infrared thermography and visible cameras are utilized, and emotional states are estimated while Japanese older adults view digital content. Emotional responses while viewing digital content are often subtle and dynamic. Additionally, various emotions occur during such situations, both positive and negative. Fluctuations in facial skin temperature and eye-opening degree reflect activities in the autonomic nervous system. In particular, expressing emotions through facial expressions is difficult for older adults; as such, emotional estimation using such ecological information is required. Our study results demonstrated that focusing on skin temperature changes and eye movements during emotional arousal and non-arousal using bidirectional long short-term memory yields an F1 score of 92.21%. The findings of this study can enhance emotion recognition in digital content, improving user experience and the evaluation of digital content. Full article
(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health: 2nd Edition)
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