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Bioengineering
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Body-Worn Sensors for Biomedical Applications
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Body-Worn Sensors for Biomedical Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2467

Special Issue Editor

Dr. Vipul A. Lugade

E-Mail Website
Guest Editor
Decker College of Nursing and Health Sciences, Binghamton University, State University of New York, Binghamton, NY, USA
Interests: gait and balance control in older adults at risk of falling; development of remote activity monitoring tools; return-to-play decision making in adolescents with mild traumatic brain injury; dual-task motor and cognitive evaluations; single- and dual-task intervention in adolescents with concussion and older adults with balance impairment

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the latest advancements and applications associated with body-worn sensors in the field of biomedical sciences. Body-worn sensors play a crucial role in monitoring and diagnosing various physiological parameters, providing valuable insights into patients' health conditions. This Special Issue seeks to foster collaboration between researchers and experts working on designing, developing, and applying body-worn sensors across a broad range of biomedical domains.

The Special Issue welcomes original research articles, reviews, and case studies that highlight the design, development, and utilization of body-worn sensors for biomedical applications. Potential research topics include (but are not limited to) the following:

  1. Novel sensing technologies for monitoring vital signs and physiological parameters;
  2. Wearable devices for continuous health monitoring and disease management;
  3. Body-worn sensors for remote patient monitoring and telemedicine;
  4. Biomechanical sensors for movement analysis and sports medicine;
  5. Integration of body-worn sensors with artificial intelligence and data analytics;
  6. Wireless communication and data transmission protocols for body-worn sensors;
  7. Wearable sensors for personalized healthcare and well-being monitoring;
  8. Regulatory and ethical considerations in the use of body-worn sensors in biomedical contexts.

By collating cutting-edge research articles pertinent to this domain, this Special Issue aims to advance our understanding of body-worn sensors applied for biomedical purposes, ultimately contributing to the improvement of patient care and healthcare outcomes. Researchers, engineers, and healthcare professionals are encouraged to contribute and share their findings in this rapidly evolving field.

Dr. Vipul A. Lugade
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 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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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
  • injury biomechanics
  • telehealth
  • telemedicine
  • smartphones
  • IMUs
  • remote monitoring
  • motion analysis
  • patient care
  • data science

Published Papers (3 papers)

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Research

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12 pages, 1733 KiB  
Article
Mechanical Method for Rapid Determination of Step Count Sensor Settings
by Sydney Lundell and Kenton R. Kaufman
Bioengineering 2024, 11(6), 547; https://doi.org/10.3390/bioengineering11060547 - 27 May 2024
Viewed by 265
Abstract
With the increased push for personalized medicine, researchers and clinicians have begun exploring the use of wearable sensors to track patient activity. These sensors typically prioritize device life over robust onboard analysis, which results in lower accuracies in step count, particularly at lower [...] Read more.
With the increased push for personalized medicine, researchers and clinicians have begun exploring the use of wearable sensors to track patient activity. These sensors typically prioritize device life over robust onboard analysis, which results in lower accuracies in step count, particularly at lower cadences. To optimize the accuracy of activity-monitoring devices, particularly at slower walking speeds, proven methods must be established to identify suitable settings in a controlled and repeatable manner prior to human validation trials. Currently, there are no methods for optimizing these low-power wearable sensor settings prior to human validation, which requires manual counting for in-laboratory participants and is limited by time and the cadences that can be tested. This article proposes a novel method for determining sensor step counting accuracy prior to human validation trials by using a mechanical camshaft actuator that produces continuous steps. Sensor error was identified across a representative subspace of possible sensor setting combinations at cadences ranging from 30 steps/min to 110 steps/min. These true errors were then used to train a multivariate polynomial regression to model errors across all possible setting combinations and cadences. The resulting model predicted errors with an R2 of 0.8 and root-mean-square error (RMSE) of 0.044 across all setting combinations. An optimization algorithm was then used to determine the combinations of settings that produced the lowest RMSE and median error for three ranges of cadence that represent disabled low-mobility ambulators, disabled high-mobility ambulators, and healthy ambulators (30–60, 20–90, and 30–110 steps/min, respectively). The model identified six setting combinations for each range of interest that achieved a ±10% error in cadence prior to human validation. The anticipated range of errors from the optimized settings at lower walking speeds are lower than the reported errors of wearable sensors (±30%), suggesting that pre-human-validation optimization of sensors may decrease errors at lower cadences. This method provides a novel and efficient approach to optimizing the accuracy of wearable activity monitors prior to human validation trials. Full article
(This article belongs to the Special Issue Body-Worn Sensors for Biomedical Applications)
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Review

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31 pages, 458 KiB  
Review
Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review
by Jae-Hwan Kang, Jang-Han Bae and Young-Ju Jeon
Bioengineering 2024, 11(5), 418; https://doi.org/10.3390/bioengineering11050418 - 24 Apr 2024
Viewed by 780
Abstract
The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures [...] Read more.
The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models. Full article
(This article belongs to the Special Issue Body-Worn Sensors for Biomedical Applications)

Other

Jump to: Research, Review

11 pages, 498 KiB  
Brief Report
Validity and Reliability of a Smartphone Application for Home Measurement of Four-Meter Gait Speed in Older Adults
by Pei-An Lee, Clark DuMontier, Wanting Yu, Levi Ask, Junhong Zhou, Marcia A. Testa, Dae Kim, Gregory Abel, Tom Travison, Brad Manor and On-Yee Lo
Bioengineering 2024, 11(3), 257; https://doi.org/10.3390/bioengineering11030257 - 6 Mar 2024
Viewed by 1088
Abstract
The four-meter gait speed (4MGS) is a recommended physical performance test in older adults but is challenging to implement clinically. We developed a smartphone application (App) with a four-meter ribbon for remote 4MGS testing at home. This study aimed to assess the validity [...] Read more.
The four-meter gait speed (4MGS) is a recommended physical performance test in older adults but is challenging to implement clinically. We developed a smartphone application (App) with a four-meter ribbon for remote 4MGS testing at home. This study aimed to assess the validity and reliability of this smartphone App-based assessment of the home 4MGS. We assessed the validity of the smartphone App by comparing it against a gold standard video assessment of the 4MGS conducted by study staff visiting community-dwelling older adults and against the stopwatch-based measurement. Moreover, we assessed the test–retest reliability in two supervised sessions and three additional sessions performed by the participants independently, without staff supervision. The 4MGS measured by the smartphone App was highly correlated with video-based 4MGS (r = 0.94), with minimal differences (mean = 0.07 m/s, ± 1.96 SD = 0.12) across a range of gait speeds. The test–retest reliability for the smartphone App 4MGS was high (ICC values: 0.75 to 0.93). The home 4MGS in older adults can be measured accurately and reliably using a smartphone in the pants pocket and a four-meter strip of ribbon. Leveraging existing technology carried by a significant portion of the older adult population could overcome barriers in busy clinical settings for this well-established objective mobility test. Full article
(This article belongs to the Special Issue Body-Worn Sensors for Biomedical Applications)
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