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Wearable Sensors for Continuous Health Monitoring and Analysis
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Wearable Sensors for Continuous Health Monitoring and Analysis

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 3761

Special Issue Editors

Prof. Dr. Juan Antonio Holgado-Terriza

E-Mail Website
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

E-Mail Website
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

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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Published Papers (4 papers)

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Research

18 pages, 2592 KiB  
Article
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 318
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 [...] Read more.
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  
Article
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
Viewed by 601
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, [...] Read more.
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  
Article
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 3 | Viewed by 1118
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 [...] Read more.
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 “πι´πτω”, 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  
Article
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 1 | Viewed by 1005
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 [...] Read more.
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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