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State of the Art in Wearable Sensors for Health Monitoring

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

Deadline for manuscript submissions: 29 July 2026 | Viewed by 11005

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Special Issue Editors

Dr. Yang Liu

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Guest Editor

Department of Computer Science, Florida State University, Tallahassee, FL, USA
Interests: mobile health; earable and wearable sensing; signal processing; on-device learning

Dr. Stephen Xia

E-Mail Website
Guest Editor

Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208-3109, USA
Interests: cyber–physical systems; edge computing; signal processing; machine learning; data science; wearables; robotics; health

Dr. Ting Dang

E-Mail Website
Guest Editor

School of Computing and Information Systems, University of Melbourne, Carlton, VIC 3052, Australia
Interests: mobile health; audio and speech processing; deep learning; affective computing; time series modeling

Special Issue Information

Dear Colleagues,

The rapid evolution of wearable sensors is revolutionizing health monitoring and personal wellness. By enabling the continuous and real-time tracking of a wide range of health and behavioral metrics; these devices provide valuable insights that can inform preventive measures; guide rehabilitation strategies; and support personalized healthcare. Beyond personal health; they also expand our capacity to understand and respond to environmental factors that affect our overall well-being.

This Special Issue will showcase the state of the art in wearable sensor technologies for health monitoring; highlighting recent advances in sensing materials; device engineering; and data analytics. We welcome contributions that explore novel sensor designs; groundbreaking applications; and multidisciplinary approaches that push the boundaries of wearable technologies and their role in healthcare.

We invite submissions on a variety of topics, including, but not limited to, the following:

Next-generation wearable sensors for health monitoring;
New sensing materials for health applications;
Innovations in physical rehabilitation using wearable devices;
Continuous activity tracking and physiological sensing;
Wearable solutions for personalized medicine and telehealth;
Environmental and lifestyle monitoring for preventive healthcare;
Advanced data analytics and machine learning for wearable health;
AI-driven innovations in wearable healthcare;
Security and privacy in wearable health platforms.

Dr. Yang Liu
Dr. Stephen Xia
Dr. Ting Dang
Guest Editors

Manuscript Submission Information

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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
health monitoring
physical rehabilitation
activity tracking
physiological sensing
sensing materials
personalized medicine
telehealth
artificial intelligence
machine learning
data analytics
security and privacy
lifestyle monitoring
preventive healthcare

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Published Papers (7 papers)

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Research

14 pages, 1760 KB

Open AccessArticle

Development and Validation of Accelerometer-Based Machine Learning Models for Classifying Walking, Running, and Jumping Activities

by Lucas Veras, Florêncio Diniz-Sousa, Giorjines Boppre, Ana Resende-Coelho, José Oliveira and Hélder Fonseca

Sensors 2026, 26(9), 2810; https://doi.org/10.3390/s26092810 - 30 Apr 2026

Viewed by 727

Abstract

Quantifying mechanical loading during daily physical activities is essential for designing and evaluating bone health interventions. Accelerometers are a promising tool for estimating these loads under free-living conditions, yet existing prediction models depend on prior knowledge of the activity being performed. This study developed and validated machine learning models to automatically distinguish between walking, running, and jumping using accelerometer data. Forty-eight healthy adults completed a protocol of walking, running, and jumping tasks while wearing ActiGraph GT9X Link accelerometers at the ankle, lower back, and hip. Three algorithms (Random Forest, Support Vector Machine, and K-Nearest Neighbors) were trained and evaluated through multiple performance metrics. All models achieved excellent classification accuracy across sensor placements, with percent agreement between 93.8% and 97.7%, receiver operating characteristic area under the curve values consistently above 0.97, and Kappa coefficients exceeding 0.89. These results demonstrate that accelerometer-based activity classification can reliably differentiate walking, running, and jumping, establishing a practical framework for applying activity-specific mechanical loading prediction equations under free-living conditions. Full article

(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)

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13 pages, 1939 KB

Open AccessArticle

Effects of Sleepwear Incorporating a DPV576 Functional Polyester Fabric on Wearable ECG-Derived Sleep Consolidation: A Randomized Two-Period Crossover Study Under Free-Living Conditions

by Hideki Katano, Masaaki Sugita, Shinichi Tokuno, Yumi Nomura, Naoya Nishino, Masakazu Higuchi, Yusuke Iwai, Yuki Matsuki, Pengyu Deng and Seiji Nishino

Sensors 2026, 26(7), 2247; https://doi.org/10.3390/s26072247 - 5 Apr 2026

Viewed by 809

Abstract

Sleep quality is essential for maintaining physical health and psychological resilience. Because sleepwear remains in direct contact with the skin throughout the night, it may affect thermoregulation and comfort and, thereby, influence sleep. This randomized two-period, two-sequence crossover study investigated whether sleepwear infused with nanodiamond and nanoplatinum particles (DPV576) could improve sleep quality and promote fatigue recovery under free-living conditions. Fourteen healthy men (23.9 ± 1.7 years) wore DPV576 sleepwear and visually indistinguishable standard polyester sleepwear for one week each, separated by a one-week washout. Sleep was assessed using a wearable ECG-based actigraphy device; trained researchers additionally performed manual rescoring to verify automated outputs, including independent determination of sleep onset latency. Subjective sleep was assessed daily using the Sleep Quality Index of Daily Sleep and a visual analog scale; exploratory outcomes included voice-derived biomarkers and pre-/post-sleep grip strength. In manual rescoring, DPV576 was associated with higher sleep efficiency (93.0 ± 0.9% vs. 89.5 ± 1.5%, p < 0.05), fewer awakenings (8.4 ± 1.3 vs. 10.7 ± 1.4, p < 0.01), and shorter wake after sleep onset (30.4 ± 4.7 vs. 41.6 ± 6.0 min, p < 0.01), whereas total sleep time did not differ significantly (p = 0.096). These findings suggest that one-week use of DPV576 sleepwear may improve wearable ECG-derived sleep consolidation in young men, supporting a nonpharmacological wearable strategy to enhance sleep efficiency in everyday settings. Full article

(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)

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11 pages, 868 KB

Open AccessArticle

Physiological Effects of Far-Infrared-Emitting Garments on Sleep, Thermoregulation, and Autonomic Function Assessed Using Wearable Sensors

by Masaki Nishida, Taku Nishii, Shutaro Suyama and Sumi Youn

Sensors 2026, 26(2), 550; https://doi.org/10.3390/s26020550 - 14 Jan 2026

Viewed by 1577

Abstract

Far-infrared (FIR)-emitting textiles are increasingly used in sleepwear; however, their influence on sleep physiology has not been comprehensively evaluated with multi-modal wearable sensing. This randomized, double-blind, placebo-controlled crossover study examined whether FIR-emitting garments modulate nocturnal thermoregulation, autonomic activity, and sleep architecture. Fifteen healthy young men completed two overnight laboratory sleep sessions wearing either FIR-emitting garments or visually matched polyester controls. Tympanic membrane temperature (TMT), sweating rate, skin temperature, and humidity were continuously monitored using wearable sensors, and sleep stages and heart rate variability (HRV) were assessed using validated portable systems. Compared with control garments, FIR garments produced consistently lower TMT across the night (p = 0.004) and reduced mid-sleep sweating (condition × time interaction: p = 0.026). The proportion of rapid eye movement (REM) sleep was higher in the FIR condition (22.2% ± 6.5% vs. 18.6% ± 6.5%, p = 0.027), despite no changes in total sleep time or sleep efficiency. A transient increase in low-frequency power during early sleep (p = 0.027) suggested baroreflex-related thermal adjustments without sympathetic activation. These findings indicate that FIR-emitting garments facilitate mild nocturnal heat dissipation and support REM expression, demonstrating their potential as a passive intervention to improve sleep-related thermal environments. Full article

(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)

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22 pages, 4041 KB

Open AccessArticle

Machine Learning-Based Image Reconstruction in Wearable CC-EIT of the Thorax: Robustness to Electrode Displacement

by Jan Jeschke, Mikhail Ivanenko, Waldemar T. Smolik, Damian Wanta, Mateusz Midura and Przemysław Wróblewski

Sensors 2025, 25(21), 6543; https://doi.org/10.3390/s25216543 - 23 Oct 2025

Cited by 1 | Viewed by 1392

Abstract

This study investigates the influence of variable electrode positions on image reconstruction in capacitively coupled electrical impedance tomography (CC-EIT) of the human thorax. Images were reconstructed by an adversarial neural network trained on a synthetic dataset generated using a tomographic model that included a wearable elastic band with 32 electrodes attached. Dataset generation was conducted using a previously developed numerical phantom of the thorax, combined with a newly developed algorithm for random selection of electrode positions based on physical limitations resulting from the elasticity of the band and possible position inaccuracies while putting the band on the patient’s chest. The thorax phantom included the heart, lungs, aorta, and spine. Four training and four testing datasets were generated using four different levels of electrode displacement. Reconstruction was conducted using four versions of neural networks trained on the datasets, with random ellipses included and noise added to achieve an SNR of 30 dB. The quality was assessed using pixel-to-pixel metrics such as the root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. The results showed a strong negative influence of electrode displacement on reconstruction quality when no samples with displaced electrodes were present in the training dataset. Training the network on the dataset containing samples with electrode displacement allowed us to significantly improve the quality of the reconstructed images. Introducing samples with misplaced electrodes increased neural network robustness to electrode displacement while testing. Full article

(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)

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19 pages, 4805 KB

Open AccessArticle

Comparative Analysis of Passive Movement During Robot-Assisted and Therapist-Led Rehabilitation Exercises

by Iwona Chuchnowska, Jolanta Mikulska, Michał Burkacki, Marta Chmura, Miłosz Chrzan, Jan Kalinowski, Sławomir Suchoń, Marek Ples, Mariusz Sobiech, Piotr Szaflik, Hanna Zadoń and Beniamin Watoła

Sensors 2025, 25(17), 5334; https://doi.org/10.3390/s25175334 - 28 Aug 2025

Cited by 2 | Viewed by 1516

Abstract

The growing number of patients in need of rehabilitation, largely due to an aging population and the increasing incidence of strokes, drives the search for more effective therapeutic methods. Stroke remains a leading cause of adult disability, increasing demand for rehabilitation services. Robotic-assisted therapy presents a promising solution by offering precision and repeatability, complementing traditional methods. This study compared traditional rehabilitation led by a physiotherapist with robotic-assisted therapy using the UR10e robot. The research consisted of two stages: in the first, a physiotherapist guided passive upper limb movements, and in the second, the same movements were replicated by the UR10e robot with a specialized adapter for arm positioning. Movements were measured using the Noraxon Ultium Motion system, analyzing flexion, extension, and rotation angles at the shoulder and elbow joints. Full article

(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)

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13 pages, 8086 KB

Open AccessArticle

Flexible FLIG-Based Temperature Sensor Enabled by Femtosecond Laser Direct Writing for Thermal Monitoring in Health Systems

by Huansheng Wu, Cong Wang, Linpeng Liu and Ji’an Duan

Sensors 2025, 25(15), 4643; https://doi.org/10.3390/s25154643 - 26 Jul 2025

Viewed by 1710

Abstract

In this study, a facile and mask-free femtosecond laser direct writing (FLDW) approach is proposed to fabricate porous graphene (FLIG) patterns directly on polyimide (PI) substrates. By systematically adjusting the laser scanning spacing (10–25 μm), denser and more continuous microstructures are obtained, resulting in significantly enhanced thermal sensitivity. The optimized sensor demonstrated a temperature coefficient of 0.698% °C⁻¹ within the range of 40–120 °C, with response and recovery times of 10.3 s and 20.9 s, respectively. Furthermore, it exhibits remarkable signal stability across multiple thermal cycles, a testament to its reliability in extreme conditions. Moreover, the sensor was successfully integrated into a 3D-printed robotic platform, achieving both contact and non-contact temperature detection. These results underscore the sensor’s practical adaptability for real-time thermal sensing. This work presents a viable and scalable methodology for fabricating high-performance FLIG-based flexible temperature sensors, with extensive application prospects in wearable electronics, electronic skin, and intelligent human–machine interfaces. Full article

(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)

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15 pages, 4940 KB

Open AccessArticle

Consistency Is Key: A Secondary Analysis of Wearable Motion Sensor Accuracy Measuring Knee Angles Across Activities of Daily Living Before and After Knee Arthroplasty

by Robert C. Marchand, Kelly B. Taylor, Emily C. Kaczynski, Skye Richards, Jayson B. Hutchinson, Shayan Khodabakhsh and Ryan M. Chapman

Sensors 2025, 25(13), 3942; https://doi.org/10.3390/s25133942 - 25 Jun 2025

Cited by 2 | Viewed by 1944

Abstract

Background: Monitoring knee range of motion (ROM) after total knee arthroplasty (TKA) via clinically deployed wearable motion sensors is increasingly common. Prior work from our own lab showed promising results in one wearable motion sensor system; however, we did not investigate errors across different activities. Accordingly, herein we conducted secondary analyses of error using wearable inertial measurement units (IMUs) quantifying sagittal knee angles across activities in TKA patients. Methods: After Institutional Review Board (IRB) approval, TKA patients were recruited for participation in two visits (n = 20 enrolled, n = 5 lost to follow-up). Following a sensor tutorial (MotionSense, Stryker, Mahwah, NJ, USA), sensors and motion capture (MOCAP) markers were applied for data capture before surgery. One surgeon then performed TKA. An identical data capture was then completed postoperatively. MOCAP and wearable motion sensor knee angles were computed during a series of activities and compared. Two-way ANOVA evaluated the impact of time (pre- vs. post-TKA) and activity on average error. Another two-way ANOVA was completed, assessing if error at local maxima was different than at local minima and if either was different across activities. Results: Pre-TKA/post-TKA errors were not different. No differences were noted across activities. On average, the errors were under clinically acceptable thresholds (i.e., 4.9 ± 2.6° vs. ≤5°). Conclusions: With average error ≤ 5°, these specific sensors accurately quantify knee angles before/after surgical intervention. Future investigations should explore leveraging this type of technology to evaluate preoperative function decline and postoperative function recovery. Full article

(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)

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