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Bioengineering
Special Issues
Smart Sensing for Health Informatics
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Smart Sensing for Health Informatics

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 6260

Special Issue Editors

Dr. Dangdang Shao

E-Mail Website
Guest Editor
Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
Interests: computer vision; image processing; physiological sensing; biomedical engineering; deep learning; machine learning; video processing
Special Issues, Collections and Topics in MDPI journals
Dr. Sai Zhang

E-Mail Website
Guest Editor
Department of Electrical Engineering，Arizona State University, Tempe, AZ 85281, USA
Interests: signal processing;communications;sensor networks;distributed computation; machine learning; deep learning; medical image analysis;

Special Issue Information

Dear Colleagues,

Advanced technologies such as artificial intelligence, non-contact physiological measurements, and novel sensor design have attracted a lot of attention in academic and industry in the past decade. Methods based on smart sensing, wireless sensor networks, machine learning, and neural networks have been developed to improve the quality of healthcare and the productivity of clinicians. This Special Issue will focus on original research papers and comprehensive reviews, dealing with cutting-edge experimental and computational methodologies for related work. Topics of interest for this Special Issue include, but are not limited to, the following:

  1. Noncontact physiological monitoring;
    2. Wireless sensor-based measurement and detection;
    3. Automated medical image analysis based on artificial intelligence;
    4. Acoustic-based physiological monitoring;
    5. Novel chemical sensor for healthcare;
    6. Deep Learning for health application.

Dr. Dangdang Shao
Dr. Sai Zhang
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. 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

  • health informatics
  • artificial intelligence
  • mobile health
  • deep learning
  • wireless monitoring
  • camera-based physiological monitoring

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Published Papers (2 papers)

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Research

32 pages, 1494 KiB  
Article
MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches
by Karrar Hameed Abdulkareem, Mohammed Nasser Al-Mhiqani, Ahmed M. Dinar, Mazin Abed Mohammed, Mustafa Jawad Al-Imari, Alaa S. Al-Waisy, Abed Saif Alghawli and Mohammed A. A. Al-Qaness
Bioengineering 2022, 9(9), 457; https://doi.org/10.3390/bioengineering9090457 - 8 Sep 2022
Cited by 13 | Viewed by 3013
Abstract
Effective prioritization plays critical roles in precision medicine. Healthcare decisions are complex, involving trade-offs among numerous frequently contradictory priorities. Considering the numerous difficulties associated with COVID-19, approaches that could triage COVID-19 patients may help in prioritizing treatment and provide precise medicine for those [...] Read more.
Effective prioritization plays critical roles in precision medicine. Healthcare decisions are complex, involving trade-offs among numerous frequently contradictory priorities. Considering the numerous difficulties associated with COVID-19, approaches that could triage COVID-19 patients may help in prioritizing treatment and provide precise medicine for those who are at risk of serious disease. Prioritizing a patient with COVID-19 depends on a variety of examination criteria, but due to the large number of these biomarkers, it may be hard for medical practitioners and emergency systems to decide which cases should be given priority for treatment. The aim of this paper is to propose a Multidimensional Examination Framework (MEF) for the prioritization of COVID-19 severe patients on the basis of combined multi-criteria decision-making (MCDM) methods. In contrast to the existing literature, the MEF has not considered only a single dimension of the examination factors; instead, the proposed framework included different multidimensional examination criteria such as demographic, laboratory findings, vital signs, symptoms, and chronic conditions. A real dataset that consists of data from 78 patients with different examination criteria was used as a base in the construction of Multidimensional Evaluation Matrix (MEM). The proposed framework employs the CRITIC (CRiteria Importance Through Intercriteria Correlation) method to identify objective weights and importance for multidimensional examination criteria. Furthermore, the VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method is utilized to prioritize COVID-19 severe patients. The results based on the CRITIC method showed that the most important examination criterion for prioritization is COVID-19 patients with heart disease, followed by cough and nasal congestion symptoms. Moreover, the VIKOR method showed that Patients 8, 3, 9, 59, and 1 are the most urgent cases that required the highest priority among the other 78 patients. Finally, the proposed framework can be used by medical organizations to prioritize the most critical COVID-19 patient that has multidimensional examination criteria and to promptly give appropriate care for more precise medicine. Full article
(This article belongs to the Special Issue Smart Sensing for Health Informatics)
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12 pages, 3532 KiB  
Article
Identification of Secondary Biomechanical Abnormalities in the Lower Limb Joints after Chronic Transtibial Amputation: A Proof-of-Concept Study Using SPM1D Analysis
by Amr Alhossary, Wei Tech Ang, Karen Sui Geok Chua, Matthew Rong Jie Tay, Poo Lee Ong, Tsurayuki Murakami, Tabitha Quake, Trevor Binedell, Seng Kwee Wee, Min Wee Phua, Yong Jia Wei and Cyril John Donnelly
Bioengineering 2022, 9(7), 293; https://doi.org/10.3390/bioengineering9070293 - 30 Jun 2022
Cited by 3 | Viewed by 2753
Abstract
SPM is a statistical method of analysis of time-varying human movement gait signal, depending on the random field theory (RFT). MovementRx is our inhouse-developed decision-support system that depends on SPM1D Python implementation of the SPM (spm1d.org). We present the potential application of MovementRx [...] Read more.
SPM is a statistical method of analysis of time-varying human movement gait signal, depending on the random field theory (RFT). MovementRx is our inhouse-developed decision-support system that depends on SPM1D Python implementation of the SPM (spm1d.org). We present the potential application of MovementRx in the prediction of increased joint forces with the possibility to predispose to osteoarthritis in a sample of post-surgical Transtibial Amputation (TTA) patients who were ambulant in the community. We captured the three-dimensional movement profile of 12 males with TTA and studied them using MovementRx, employing the SPM1D Python library to quantify the deviation(s) they have from our corresponding reference data, using “Hotelling 2” and “T test 2” statistics for the 3D movement vectors of the 3 main lower limb joints (hip, knee, and ankle) and their nine respective components (3 joints × 3 dimensions), respectively. MovementRx results visually demonstrated a clear distinction in the biomechanical recordings between TTA patients and a reference set of normal people (ABILITY data project), and variability within the TTA patients’ group enabled identification of those with an increased risk of developing osteoarthritis in the future. We conclude that MovementRx is a potential tool to detect increased specific joint forces with the ability to identify TTA survivors who may be at risk for osteoarthritis. Full article
(This article belongs to the Special Issue Smart Sensing for Health Informatics)
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