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Data Analytics and Applications of Wearable Sensors in Healthcare

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

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 9798

Special Issue Editors


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Guest Editor
Johns Hopkins University School of Medicine, Department of Medicine, Division of Geriatric Medicine and Gerontology, Center on Aging and Health, 2024 E. Monument St., Baltimore MD 21205, USA
Interests: wearable devices; free-living physical activity; biostatistics; digital signal processing; gerontology

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Guest Editor
Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, MA 02115, USA
Interests: human activity recognition; physical activity; sedentary behavior; digital phenotyping; smartphone sensors

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Guest Editor
Department of Biostatistics, Indiana University Richard M. Fairbanks School of Public Health and School of Medicine, 410 W. 10th St., Indianapolis, IN 46202, USA
Interests: biostatistics; wearable physical activity monitors; electronic medical records; statistical software

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Guest Editor
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
Interests: Raw accelerometry; Feature extraction; Statistical modeling; Circadian rhythm analysis; Longitudinal study

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed substantial growth in popularity and application of both research- and consumer-grade wearable devices that can collect a variety of biological signals continuously over multiple days at a time. For example, large observational studies, including NHANES and UKBiobank, have already collected free-living physical activity data with wearable accelerometers on over a hundred thousand participants. As a result, there is a growing number of more focused observational studies and clinical trials also introducing accelerometry measurement protocols in their design. Additionally, rapidly advancing technology and decreasing prices have given rise to other exciting and useful types of wearables, including, among many others, ambulatory ECGs, implantable blood-glucose monitors, and personal GPS devices.

To top it all off, we are observing the accelerating migration of consumer-grade devices into clinical and research applications evidenced by the marketing of the newest Apple Watch as a physical activity and ECG monitor, or by the acquisition of Fitbit by Google and the Google Health initiative. Given the increasing acceptance of wearable technology, it is imperative to gain a better understanding of collected data and their potential and to provide a transparent and accessible analytical know-how that will help to improve the design and avoid confusion in future research utilizing wearable devices. This Special Issue will focus on novel analytical methods and original research applications that make use of data collected by a wide range of wearable sensors in medicine and epidemiology.

Dr. Jacek Urbanek
Dr. Marcin Strączkiewicz
Dr. William Fadel

Dr. Jiawei Bai

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.

Keywords

  • Wearable computing
  • Accelerometers
  • Ambulatory ECG
  • Implantable blood glucose monitors
  • Sensor fusion
  • High-density bio-signals
  • Digital phenotyping

Published Papers (2 papers)

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Research

17 pages, 4604 KiB  
Article
Improving R Peak Detection in ECG Signal Using Dynamic Mode Selected Energy and Adaptive Window Sizing Algorithm with Decision Tree Algorithm
by Zubaer Md. Abdullah Al, Keshav Thapa and Sung-Hyun Yang
Sensors 2021, 21(19), 6682; https://doi.org/10.3390/s21196682 - 8 Oct 2021
Cited by 10 | Viewed by 5875
Abstract
R peak detection is crucial in electrocardiogram (ECG) signal analysis to detect and diagnose cardiovascular diseases (CVDs). Herein, the dynamic mode selected energy (DMSE) and adaptive window sizing (AWS) algorithm are proposed for detecting R peaks with better efficiency. The DMSE algorithm adaptively [...] Read more.
R peak detection is crucial in electrocardiogram (ECG) signal analysis to detect and diagnose cardiovascular diseases (CVDs). Herein, the dynamic mode selected energy (DMSE) and adaptive window sizing (AWS) algorithm are proposed for detecting R peaks with better efficiency. The DMSE algorithm adaptively separates the QRS components and all non-objective components from the ECG signal. Based on local peaks in QRS components, the AWS algorithm adaptively determines the Region of Interest (ROI). The Feature Extraction process computes the statistical properties of energy, frequency, and noise from each ROI. The Sequential Forward Selection (SFS) procedure is used to find the best subsets of features. Based on these characteristics, an ensemble of decision tree algorithms detects the R peaks. Finally, the R peak position on the initial ECG signal is adjusted using the R location correction (RLC) algorithm. The proposed method has an experimental accuracy of 99.94%, a sensitivity of 99.98%, positive predictability of 99.96%, and a detection error rate of 0.06%. Given the high efficiency in detection and fast processing speed, the proposed approach is ideal for intelligent medical and wearable devices in the diagnosis of CVDs. Full article
(This article belongs to the Special Issue Data Analytics and Applications of Wearable Sensors in Healthcare)
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23 pages, 3405 KiB  
Article
Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation
by Túlio Fernandes de Almeida, Edgard Morya, Abner Cardoso Rodrigues and André Felipe Oliveira de Azevedo Dantas
Sensors 2021, 21(19), 6477; https://doi.org/10.3390/s21196477 - 28 Sep 2021
Cited by 7 | Viewed by 3097
Abstract
The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software. The hardware was developed using [...] Read more.
The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software. The hardware was developed using a low-cost IMU and microcontroller. The IMU data analysis software was developed in Python and has three fusion filters: Complementary Filter, Kalman Filter, and Madgwick Filter. Three experiments were performed for the proof of concept of the system. First, we evaluated the knee joint of Lokomat, with a predefined average range of motion (ROM) of 60. In the second, we evaluated our system in a real scenario, evaluating the knee of a healthy adult individual during gait. In the third experiment, we evaluated the software using data from gold standard devices, comparing the results of our software with Ground Truth. In the evaluation of the Lokomat, our system achieved an average ROM of 58.28, and during evaluation in a real scenario it achieved an average ROM of 44.62. In comparing our software with Ground Truth, we achieved a root-mean-square error of 0.04 and a mean average percentage error of 2.95%. These results encourage the use of this system in other scenarios. Full article
(This article belongs to the Special Issue Data Analytics and Applications of Wearable Sensors in Healthcare)
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