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Wearable Physiological Sensors for Smart Healthcare
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Wearable Physiological Sensors for Smart Healthcare

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

Deadline for manuscript submissions: 31 January 2027 | Viewed by 9944

Special Issue Editor

Dr. Yuki Hashimoto

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Institute of Science Tokyo, Tokyo 1528552, Japan
Interests: wearable sensing; sweat sensor; core body temperature sensor; ECG sensor; heat-related illness; IoT; smart healthcare

Special Issue Information

Dear Colleagues,

Wearable physiological sensors are gaining increasing attention for their potential to continuously monitor endogenous biosignals—such as electrocardiograms (ECG), heart rate, body temperature, sweat composition, and blood pressure—in real time and in daily life settings. These technologies are essential components of smart healthcare systems, enabling early detection, personalized intervention, and long-term health management in both clinical and non-clinical environments.

This Special Issue aims to gather original research and review articles on recent advances in wearable sensors dedicated to monitoring physiological signals, with a particular focus on non-invasive, real-time, and robust sensing technologies. We especially encourage contributions addressing sensor design, signal processing, integration with IoT or mobile platforms, and clinical validation.

Dr. Yuki Hashimoto
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

  • wearable physiological sensors
  • vital sign monitoring
  • smart healthcare
  • electrocardiogram
  • heart rate
  • body temperature
  • blood pressure
  • sweat

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Published Papers (6 papers)

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Research

Jump to: Review

17 pages, 1768 KB  
Article
Multimodal Detection of Pain and Anticipation Anxiety from Ultra-Short Duration Wearable Sensors Measurements
by Andrew G. Peitzsch, Katie Geary, Youngsun Kong, Hugo Posada-Quintero, Drew Havard, William R. D’Angelo and Ki H. Chon
Sensors 2026, 26(10), 3181; https://doi.org/10.3390/s26103181 - 18 May 2026
Viewed by 243
Abstract
With the continued rise in outpatient surgical procedures, modern medicine requires more advanced tools for pain and anxiety monitoring and management. The current standard of care requires patient responses on visual analog scales, which may be subjective and are difficult to assess when [...] Read more.
With the continued rise in outpatient surgical procedures, modern medicine requires more advanced tools for pain and anxiety monitoring and management. The current standard of care requires patient responses on visual analog scales, which may be subjective and are difficult to assess when a subject is unresponsive. Electrodermal activity (EDA) and pulse rate variability (PRV), two non-invasive, wearable, and objective measurements of sympathetic nervous system activity, can help provide insight into a patient’s psychological or emotional state without user input, allowing for continued monitoring even when a patient is unable to respond. However, methods based on these measurements have largely been relegated to longer duration (>60 s) or post hoc analysis, which does not suit the needs of medical care environments. Here we propose new methods for handling ultra-short (<10 s) signals to allow rapid evaluation of pain and anxiety state. We show how machine learning models trained on these signals can obtain high degrees of classification performance (AUC > 0.88) between no pain or anxiety and medium or higher pain and anxiety on signals obtained during two different forms of painful stimulation. We also show how these signals can measure the degree of stimulation irrespective of perceived pain from the patient. Further development of these algorithms will allow for greater monitoring and control of patient comfort in a clinical setting. Full article
(This article belongs to the Special Issue Wearable Physiological Sensors for Smart Healthcare)
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20 pages, 1354 KB  
Article
Comparison of Point-and-Click Performance Between the Brainfingers BCI and the Mouse
by Alexandros Pino, Dimitrios Vrailas and Georgios Kouroupetroglou
Sensors 2026, 26(9), 2777; https://doi.org/10.3390/s26092777 - 29 Apr 2026
Viewed by 819
Abstract
This study quantitatively evaluates the performance of a non-invasive hybrid brain–computer interface (BCI) compared to a conventional mouse in pointing (point-and-click) tasks. A commercial wearable BCI (Brainfingers), based on electromyography (EMG) and electrooculography (EOG) signals with low-level electroencephalography (EEG) components, was assessed against [...] Read more.
This study quantitatively evaluates the performance of a non-invasive hybrid brain–computer interface (BCI) compared to a conventional mouse in pointing (point-and-click) tasks. A commercial wearable BCI (Brainfingers), based on electromyography (EMG) and electrooculography (EOG) signals with low-level electroencephalography (EEG) components, was assessed against a Microsoft Optical Mouse using ISO/TS 9241-411-based one-dimensional (1D) and two-dimensional (2D) target acquisition tasks. Pointer coordinates were recorded and analyzed using Fitts’ law metrics. A total of 48 non-disabled participants completed the experiments. The results reveal significant performance differences between the two input devices. The BCI device exhibits substantially lower performance than the mouse across the reported Fitts’ law measures. Mean throughput was 0.35 bits/s for the BCI and 6.03 bits/s for the mouse in the 1D tests and 0.43 bits/s for the BCI and 5.17 bits/s for the mouse in the 2D tests. Despite the BCI’s low performance and although the present experiments involved non-disabled participants, the findings, considered alongside the prior literature on Brainfingers and non-invasive BCIs for computer access, suggest that the device may still have assistive technology value for users with severe motor impairments. Full article
(This article belongs to the Special Issue Wearable Physiological Sensors for Smart Healthcare)
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27 pages, 6994 KB  
Article
A Wearable System for Knee Osteoarthritis: Based on Multimodal Physiological Signal Assessment and Intelligent Rehabilitation
by Jingyi Hu, Shuyi Wang, Yichun Shen and Xinrong Miao
Sensors 2025, 25(23), 7334; https://doi.org/10.3390/s25237334 - 2 Dec 2025
Cited by 1 | Viewed by 2594
Abstract
Knee osteoarthritis (KOA), a common degenerative joint disease, affects a large patient population and poses significant challenges in early diagnosis and rehabilitation. Achieving precise assessment of knee function and efficient home-based intelligent rehabilitation is crucial for alleviating pain, slowing disease progression, and improving [...] Read more.
Knee osteoarthritis (KOA), a common degenerative joint disease, affects a large patient population and poses significant challenges in early diagnosis and rehabilitation. Achieving precise assessment of knee function and efficient home-based intelligent rehabilitation is crucial for alleviating pain, slowing disease progression, and improving patients’ quality of life. This study proposes a smart wearable knee function assessment based on multimodal physiological signals and a rehabilitation system. The system integrates surface electromyography (sEMG), pressure sensors, and an inertial measurement unit (IMU) to synchronously capture gait, posture, and muscle activity. It quantifies knee function by extracting gait and EMG features. Additionally, a wearable massage device driven by airbags was designed and implemented to simulate the traditional Chinese medicine “seated knee-adjustment method” and deliver precise intelligent rehabilitation interventions. Experimental results validated the system’s accuracy in functional assessment and reliability in rehabilitation assistance. The average relative error in gait feature extraction was below 8%, while the massage head displacement error remained within clinically acceptable ranges. By integrating multimodal sensing technology with intelligent rehabilitation devices, this system offers KOA patients a convenient, efficient, and sustainable home-based rehabilitation solution with strong clinical application potential and promotional value. Full article
(This article belongs to the Special Issue Wearable Physiological Sensors for Smart Healthcare)
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21 pages, 4496 KB  
Article
Butterworth Filtering at 500 Hz Optimizes PPG-Based Heart Rate Variability Analysis for Wearable Devices: A Comparative Study
by Nagima Abdrasulova, Milana Aleksanyan, Min Ju Kim and Jae Mok Ahn
Sensors 2025, 25(22), 7091; https://doi.org/10.3390/s25227091 - 20 Nov 2025
Viewed by 1706
Abstract
Photoplethysmography (PPG)-based heart rate variability (HRV) offers a cost-effective alternative to electrocardiography (ECG) for autonomic monitoring in wearable devices. We optimized signal processing on a 16-bit microcontroller by comparing 4th-order equivalent Butterworth and Elliptic IIR bandpass filters (0.8–20 Hz, zero-phase) at 1000, 500, [...] Read more.
Photoplethysmography (PPG)-based heart rate variability (HRV) offers a cost-effective alternative to electrocardiography (ECG) for autonomic monitoring in wearable devices. We optimized signal processing on a 16-bit microcontroller by comparing 4th-order equivalent Butterworth and Elliptic IIR bandpass filters (0.8–20 Hz, zero-phase) at 1000, 500, and 250 Hz. Paired PPG–ECG recordings from 10 healthy adults were analyzed for ln HF, ln LF, and ln VLF using Lin’s concordance correlation coefficient (CCC), ±5% equivalence testing (TOST), and Passing–Bablok regression (PBR). Butterworth at 500 Hz preserved near-identity with ECG standard (CCC ≥0.94; TOST met equivalence; PBR slopes/intercepts: ln HF = 0.97x + 0.10, ln LF = 1.02x − 0.07, ln VLF = 1.01x − 0.03), while halving computational load. In contrast, Elliptic at 250 Hz degraded concordance (CCC ≈ 0.64) and failed equivalence, with greater bias from nonlinear phase and ripple-induced distortion. Elliptic performance improved at higher sampling but offered no benefit over Butterworth. These results support zero-phase Butterworth filtering at ≥500 Hz as the optimal balance of fidelity, robustness, and efficiency, enabling reliable PPG-HRV monitoring on low-power devices. As a pilot investigation (n = 10), this study establishes preliminary design parameters and optimal configurations to guide subsequent large-scale clinical validation. Full article
(This article belongs to the Special Issue Wearable Physiological Sensors for Smart Healthcare)
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23 pages, 14657 KB  
Article
An Annular CMUT Array and Acquisition Strategy for Continuous Monitoring
by María José Almario Escorcia, Amir Gholampour, Rob van Schaijk, Willem-Jan de Wijs, Andre Immink, Vincent Henneken, Richard Lopata and Hans-Martin Schwab
Sensors 2025, 25(21), 6637; https://doi.org/10.3390/s25216637 - 29 Oct 2025
Cited by 1 | Viewed by 1325
Abstract
In many monitoring scenarios, repeated and operator-independent assessments are needed. Wearable ultrasound technology has the potential to continuously provide the vital information traditionally obtained from conventional ultrasound scanners, such as in fetal monitoring for high-risk pregnancies. This work is an engineering study motivated [...] Read more.
In many monitoring scenarios, repeated and operator-independent assessments are needed. Wearable ultrasound technology has the potential to continuously provide the vital information traditionally obtained from conventional ultrasound scanners, such as in fetal monitoring for high-risk pregnancies. This work is an engineering study motivated by that setting. A 144-element annular capacitive micromachined ultrasonic transducer (CMUT) is hereby proposed for 3-D ultrasound imaging. The array is characterized by its compact size and cost-effectiveness, with a geometry and low-voltage operation that make it a candidate for future wearable integration. To enhance the imaging performance, we propose the utilization of a Fermat’s spiral virtual source (VS) pattern for diverging wave transmission and conduct a performance comparison with other VS patterns and standard techniques, such as focused and plane waves. To facilitate this analysis, a simplified and versatile simulation framework, enhanced by GPU acceleration, has been developed. The validation of the simulation framework aligned closely with expected values (0.002 ≤ MAE ≤ 0.089). VSs following a Fermat’s spiral led to a balanced outcome across metrics, outperforming focused wave transmissions for this specific aperture. The proposed transducer presents imaging limitations that could be improved in future developments, but it establishes a foundational framework for the design and fabrication of cost-effective, compact 2-D transducers suitable for 3-D ultrasound imaging, with potential for future integration into wearable devices. Full article
(This article belongs to the Special Issue Wearable Physiological Sensors for Smart Healthcare)
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Review

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30 pages, 1169 KB  
Review
A Comprehensive Review of Non-Invasive Core Body Temperature Measurement Techniques
by Yuki Hashimoto
Sensors 2026, 26(3), 972; https://doi.org/10.3390/s26030972 - 2 Feb 2026
Cited by 3 | Viewed by 2457
Abstract
Core body temperature (CBT) is a fundamental physiological parameter tightly regulated by thermoregulatory mechanisms and is critically important for heat stress assessment, clinical management, and circadian rhythm research. Although invasive measurements such as pulmonary artery, esophageal, and rectal temperatures provide high accuracy, their [...] Read more.
Core body temperature (CBT) is a fundamental physiological parameter tightly regulated by thermoregulatory mechanisms and is critically important for heat stress assessment, clinical management, and circadian rhythm research. Although invasive measurements such as pulmonary artery, esophageal, and rectal temperatures provide high accuracy, their practical use is limited by invasiveness, discomfort, and restricted feasibility for continuous monitoring in daily-life or field environments. Consequently, extensive efforts have been devoted to developing non-invasive CBT measurement and estimation techniques. This review provides an application-oriented synthesis of invasive reference methods and representative non-invasive approaches, including in-ear sensors, infrared thermography, ingestible telemetric sensors, heat-flux-based techniques, and model-based estimation using wearable physiological signals. For each approach, measurement principles, accuracy, invasiveness, usability, and application domains are comparatively examined, with particular emphasis on trade-offs between measurement fidelity and real-world implementability. Rather than ranking methods by absolute performance, this review highlights their relative positioning across clinical, occupational, and daily-life contexts. While no single non-invasive technique can universally replace invasive gold standards, recent advances in wearable sensing, heat-flux modeling, and multimodal estimation demonstrate growing potential for practical CBT monitoring. Overall, the findings suggest that future CBT assessment will increasingly rely on hybrid and context-aware systems that integrate complementary methods to enable reliable monitoring under real-world conditions. This review is intended for researchers and practitioners who need to select or design CBT monitoring systems. Full article
(This article belongs to the Special Issue Wearable Physiological Sensors for Smart Healthcare)
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