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Wearable Sensors for Continuous Health Monitoring and Analysis

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

Deadline for manuscript submissions: 30 January 2025 | Viewed by 12933

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Prof. Dr. Juan Antonio Holgado-Terriza

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Guest Editor

Software Engineering Department, Higher Technical School of Computer and Telecommunications Engineering, Aynadamar Campus, University of Granada, 18071 Granada, Spain
Interests: Internet of Things (IoT); edge computing, smart home; embedded and wearable systems; internet of agents (IoA); cyber-physical and IIoT systems; real-time systems
Special Issues, Collections and Topics in MDPI journals

Dr. Pablo Pico-Valencia

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Guest Editor

School of Systems and Computing, Pontificia Universidad Católica del Ecuador, Esmeraldas, Ecuador
Interests: agent-based Internet of Things (IoT); machine and deep learning in both, general and of IoT context; human computer interaction; data analytics using data science

Dr. Zilu Liang

E-Mail Website
Guest Editor

Faculty of Engineering, Kyoto University of Advanced Science, Kyoto 621-8555, Japan
Interests: ubiquitous computing; wearable computing; human-computer interaction; data science; health informatics; machine learning; natural language processing; serious game; eye-tracking; fNIRS brain imaging; VR/XR/oculus
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Improvements in wearable technology and the ability to embed sensors in wearable devices in recent years have had a significant impact on a wide range of fields, including healthcare, sports, entertainment and fitness, among others. Indeed, wearable devices incorporate multiple sensing elements, such as accelerometers, gyroscopes, temperature sensors, microelectromechanical systems (MEMS), and biosensors, which are designed to be energy-efficient, wireless, and non-invasive; they thus provide a more comprehensive view of a user's health and wellness. These sensors enable the monitoring of dynamic and non-invasive measurements, acquire biomedical and biomechanical signals, and provide continuous and real-time functional tracking information. They have the advantages of being small in size, easy to install, light in weight, portable, high efficient and low in cost.

The data collected by wearable sensors can be transmitted directly to a smartphone or any other device, which then forwards the data to a smart IoT system for their storage, analysis and interpretation. This allows a wide range of healthcare activities, including disease prevention, diagnosis, treatment, and management. In addition, they can be applied in order to monitor athletic performance, track movements, prevent injuries, detect falls and alert caregivers or emergency services by using Artificial Intelligence.

This Special Issue aims to attract the latest research and findings in the design, development and validation of wearable sensors and devices, as well as their integration into the IoT ecosystem to be used for analysis and continuous monitoring in applications of health, wellness, and physical activity, among others. We accept original, technical, or critical papers on (but not limited to) the following topics:

Wireless body sensor networks;
Gesture recognition with wearable sensors;
Novel sensors for monitoring health data and physical activity;
Cloud/Edge/Fog computing for healthcare and wellness systems;
Vital Signal continuous monitoring;
Multi-sensor data fusion;
Human activity recognition;
IoT Applications in healthcare and sports activity;
Wearable sensors for m-health and u-health;
New algorithms and methods for physical activity analysis;
Real-time artificial intelligent based health monitoring;
Patterns recognition in routines for high performance athletes;
Data analytics and machine learning for wearable sensor data in health analysis;
Ethical and privacy issues in wearable sensor-based health monitoring.

Prof. Dr. Juan Antonio Holgado-Terriza
Dr. Pablo Pico-Valencia
Dr. Zilu Liang
Guest Editors

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Published Papers (9 papers)

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Research

14 pages, 3811 KiB

Open AccessArticle

Signal Quality in Continuous Transcutaneous Bilirubinometry

by Fernando Crivellaro, Anselmo Costa and Pedro Vieira

Sensors 2024, 24(18), 6154; https://doi.org/10.3390/s24186154 - 23 Sep 2024

Viewed by 751

Abstract

Bilirubin is a product of the metabolism of hemoglobin from red blood cells. Higher levels of bilirubin are a sign that either there is an unusual breaking down rate of red blood cells or the liver is not able to eliminate bilirubin, through bile, into the gastrointestinal tract. For adults, bilirubin is occasionally monitored through urine or invasive blood sampling, whilst all newborns are routinely monitored visually, or non-invasively with transcutaneous measurements (TcBs), due to their biological immaturity to conjugate bilirubin. Neonatal jaundice is a common condition, with higher levels of unconjugated bilirubin concentration having neurotoxic effects. Actual devices used in TcBs are focused on newborn populations, are hand-held, and, in some cases, operate in only two wavelengths, which does not necessarily guarantee reliable results over all skin tones. The same occurs with visual inspections. Based on that, a continuous bilirubin monitoring device for newborns is being developed to overcome visual inspection errors and to reduce invasive procedures. This device, operating optically with a mini-spectrometer in the visible range, is susceptible to patient movements and, consequently, to situations with a lower signal quality for reliable bilirubin concentration estimates on different types of skin. Therefore, as an intermediate development step and, based on skin spectra measurements from adults, this work addresses the device’s placement status prediction as a signal quality indication index. This was implemented by using machine learning (ML), with the best performances being achieved by support vector machine (SVM) models, based on the spectra acquired on the arm and forehead areas. Full article

(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)

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14 pages, 1507 KiB

Open AccessArticle

Enhanced 2D Hand Pose Estimation for Gloved Medical Applications: A Preliminary Model

by Adam W. Kiefer, Dominic Willoughby, Ryan P. MacPherson, Robert Hubal and Stephen F. Eckel

Sensors 2024, 24(18), 6005; https://doi.org/10.3390/s24186005 - 17 Sep 2024

Viewed by 835

Abstract

(1) Background: As digital health technology evolves, the role of accurate medical-gloved hand tracking is becoming more important for the assessment and training of practitioners to reduce procedural errors in clinical settings. (2) Method: This study utilized computer vision for hand pose estimation to model skeletal hand movements during in situ aseptic drug compounding procedures. High-definition video cameras recorded hand movements while practitioners wore medical gloves of different colors. Hand poses were manually annotated, and machine learning models were developed and trained using the DeepLabCut interface via an 80/20 training/testing split. (3) Results: The developed model achieved an average root mean square error (RMSE) of 5.89 pixels across the training data set and 10.06 pixels across the test set. When excluding keypoints with a confidence value below 60%, the test set RMSE improved to 7.48 pixels, reflecting high accuracy in hand pose tracking. (4) Conclusions: The developed hand pose estimation model effectively tracks hand movements across both controlled and in situ drug compounding contexts, offering a first-of-its-kind medical glove hand tracking method. This model holds potential for enhancing clinical training and ensuring procedural safety, particularly in tasks requiring high precision such as drug compounding. Full article

(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)

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20 pages, 2919 KiB

Open AccessArticle

Evaluating the Electroencephalographic Signal Quality of an In-Ear Wearable Device

by Jeremy Pazuelo, Jose Yesith Juez, Hanane Moumane, Jan Pyrzowski, Liliana Mayor, Fredy Enrique Segura-Quijano, Mario Valderrama and Michel Le Van Quyen

Sensors 2024, 24(12), 3973; https://doi.org/10.3390/s24123973 - 19 Jun 2024

Cited by 2 | Viewed by 1584

Abstract

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for advancing brain monitoring technologies into everyday applications. However, despite the current availability of several in-ear EEG devices in the market, there remains a critical need for robust validation against established clinical-grade systems. In this study, we carried out a detailed examination of the signal performance of a mobile in-ear EEG device from Naox Technologies. Our investigation had two main goals: firstly, evaluating the hardware circuit’s reliability through simulated EEG signal experiments and, secondly, conducting a thorough comparison between the in-ear EEG device and gold-standard EEG monitoring equipment. This comparison assesses correlation coefficients with recognized physiological patterns during wakefulness and sleep, including alpha rhythms, eye artifacts, slow waves, spindles, and sleep stages. Our findings support the feasibility of using this in-ear EEG device for brain activity monitoring, particularly in scenarios requiring enhanced comfort and user-friendliness in various clinical and research settings. Full article

(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)

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15 pages, 3822 KiB

Open AccessArticle

Data-Fusion-Based Quality Enhancement for HR Measurements Collected by Wearable Sensors

by Shenghao Xia, Shu-Fen Wung, Chang-Chun Chen, Jude Larbi Kwesi Coompson, Janet Roveda and Jian Liu

Sensors 2024, 24(10), 2970; https://doi.org/10.3390/s24102970 - 7 May 2024

Cited by 2 | Viewed by 949

Abstract

The advancements of Internet of Things (IoT) technologies have enabled the implementation of smart and wearable sensors, which can be employed to provide older adults with affordable and accessible continuous biophysiological status monitoring. The quality of such monitoring data, however, is unsatisfactory due to excessive noise induced by various disturbances, such as motion artifacts. Existing methods take advantage of summary statistics, such as mean or median values, for denoising, without taking into account the biophysiological patterns embedded in data. In this research, a functional data analysis modeling method was proposed to enhance the data quality by learning individual subjects’ diurnal heart rate (HR) patterns from historical data, which were further improved by fusing newly collected data. This proposed data-fusion approach was developed based on a Bayesian inference framework. Its effectiveness was demonstrated in an HR analysis from a prospective study involving older adults residing in assisted living or home settings. The results indicate that it is imperative to conduct personalized healthcare by estimating individualized HR patterns. Furthermore, the proposed calibration method provides a more accurate (smaller mean errors) and more precise (smaller error standard deviations) HR estimation than raw HR and conventional methods, such as the mean. Full article

(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)

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9 pages, 659 KiB

Open AccessArticle

Analyzing the Thermal Characteristics of Three Lining Materials for Plantar Orthotics

by Esther Querol-Martínez, Artur Crespo-Martínez, Álvaro Gómez-Carrión, Juan Francisco Morán-Cortés, Alfonso Martínez-Nova and Raquel Sánchez-Rodríguez

Sensors 2024, 24(9), 2928; https://doi.org/10.3390/s24092928 - 4 May 2024

Viewed by 1141

Abstract

Introduction: The choice of materials for covering plantar orthoses or wearable insoles is often based on their hardness, breathability, and moisture absorption capacity, although more due to professional preference than clear scientific criteria. An analysis of the thermal response to the use of these materials would provide information about their behavior; hence, the objective of this study was to assess the temperature of three lining materials with different characteristics. Materials and Methods: The temperature of three materials for covering plantar orthoses was analyzed in a sample of 36 subjects (15 men and 21 women, aged 24.6 ± 8.2 years, mass 67.1 ± 13.6 kg, and height 1.7 ± 0.09 m). Temperature was measured before and after 3 h of use in clinical activities, using a polyethylene foam copolymer (PE), ethylene vinyl acetate (EVA), and PE-EVA copolymer foam insole with the use of a FLIR E60BX thermal camera. Results: In the PE copolymer (material 1), temperature increases between 1.07 and 1.85 °C were found after activity, with these differences being statistically significant in all regions of interest (p < 0.001), except for the first toe (0.36 °C, p = 0.170). In the EVA foam (material 2) and the expansive foam of the PE-EVA copolymer (material 3), the temperatures were also significantly higher in all analyzed areas (p < 0.001), ranging between 1.49 and 2.73 °C for EVA and 0.58 and 2.16 °C for PE-EVA. The PE copolymer experienced lower overall overheating, and the area of the fifth metatarsal head underwent the greatest temperature increase, regardless of the material analyzed. Conclusions: PE foam lining materials, with lower density or an open-cell structure, would be preferred for controlling temperature rise in the lining/footbed interface and providing better thermal comfort for users. The area of the first toe was found to be the least overheated, while the fifth metatarsal head increased the most in temperature. This should be considered in the design of new wearables to avoid excessive temperatures due to the lining materials. Full article

(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)

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18 pages, 2592 KiB

Open AccessArticle

Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals

by Jason Nan, Matthew S. Herbert, Suzanna Purpura, Andrea N. Henneken, Dhakshin Ramanathan and Jyoti Mishra

Sensors 2024, 24(8), 2640; https://doi.org/10.3390/s24082640 - 20 Apr 2024

Viewed by 1780

Abstract

Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care. Full article

(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)

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15 pages, 13051 KiB

Open AccessArticle

Wearable Loop Sensor for Bilateral Knee Flexion Monitoring

by Yingzhe Zhang, Jaclyn B. Caccese and Asimina Kiourti

Sensors 2024, 24(5), 1549; https://doi.org/10.3390/s24051549 - 28 Feb 2024

Cited by 2 | Viewed by 1311

Abstract

We have previously reported wearable loop sensors that can accurately monitor knee flexion with unique merits over the state of the art. However, validation to date has been limited to single-leg configurations, discrete flexion angles, and in vitro (phantom-based) experiments. In this work, we take a major step forward to explore the bilateral monitoring of knee flexion angles, in a continuous manner, in vivo. The manuscript provides the theoretical framework of bilateral sensor operation and reports a detailed error analysis that has not been previously reported for wearable loop sensors. This includes the flatness of calibration curves that limits resolution at small angles (such as during walking) as well as the presence of motional electromotive force (EMF) noise at high angular velocities (such as during running). A novel fabrication method for flexible and mechanically robust loops is also introduced. Electromagnetic simulations and phantom-based experimental studies optimize the setup and evaluate feasibility. Proof-of-concept in vivo validation is then conducted for a human subject performing three activities (walking, brisk walking, and running), each lasting 30 s and repeated three times. The results demonstrate a promising root mean square error (RMSE) of less than 3° in most cases. Full article

(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)

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25 pages, 6971 KiB

Open AccessArticle

PIPTO: Precise Inertial-Based Pipeline for Threshold-Based Fall Detection Using Three-Axis Accelerometers

by Stavros N. Moutsis, Konstantinos A. Tsintotas and Antonios Gasteratos

Sensors 2023, 23(18), 7951; https://doi.org/10.3390/s23187951 - 18 Sep 2023

Cited by 5 | Viewed by 1711

Abstract

After traffic-related incidents, falls are the second cause of human death, presenting the highest percentage among the elderly. Aiming to address this problem, the research community has developed methods built upon different sensors, such as wearable, ambiance, or hybrid, and various techniques, such as those that are machine learning- and heuristic based. Concerning the models used in the former case, they classify the input data between fall and no fall, and specific data dimensions are required. Yet, when algorithms that adopt heuristic techniques, mainly using thresholds, are combined with the previous models, they reduce the computational cost. To this end, this article presents a pipeline for detecting falls through a threshold-based technique over the data provided by a three-axis accelerometer. This way, we propose a low-complexity system that can be adopted from any acceleration sensor that receives information at different frequencies. Moreover, the input lengths can differ, while we achieve to detect multiple falls in a time series of sum vector magnitudes, providing the specific time range of the fall. As evaluated on several datasets, our pipeline reaches high performance results at 90.40% and 91.56% sensitivity on MMsys and KFall, respectively, while the generated specificity is 93.96% and 85.90%. Lastly, aiming to facilitate the research community, our framework, entitled PIPTO (drawing inspiration from the Greek verb “

π

\overset{´}{ι}

π

τ

ω

”, signifying “to fall”), is open sourced in Python and C. Full article

(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)

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13 pages, 4400 KiB

Open AccessArticle

Validation of a Skin Calorimeter to Determine the Heat Capacity and the Thermal Resistance of the Skin

by Pedro Jesús Rodríguez de Rivera, Miriam Rodríguez de Rivera, Fabiola Socorro and Manuel Rodríguez de Rivera

Sensors 2023, 23(9), 4391; https://doi.org/10.3390/s23094391 - 29 Apr 2023

Cited by 4 | Viewed by 1515

Abstract

In vivo determination of the skin’s thermal properties is of growing interest. Several types of sensors are being designed and tested. In this field, we have developed a skin calorimeter for the determination of the heat flow, the heat capacity and the thermal resistance of the skin. The calorimeter calibration consists of the determination of the parameters of the model we have chosen to represent the behavior of the device. This model considers the heat capacity and the thermal resistance of the skin, which depend on the case (body zone, subject, physical state, etc.) and also have a strong time dependence. Therefore, this work includes a validation study with reference materials. Finally, it is concluded that the heat capacity determined is a function of the thermal penetration depth of the measurement characteristics. In the case of high thermal conductivity materials in which the thermal penetration is nearly total, the heat capacity obtained coincides with that of the reference material sample. Full article

(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)

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