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Big Data Analytics of Wireless Sensors and Wearable Devices for Biomedical Applications and Healthcare

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

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 8795

Special Issue Editor

Prof. Dr. Hesham H. Ali

E-Mail Website
Guest Editor
UNO Bioinformatics Core Facility, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
Interests: bioinformatics; graph theory; design and analysis of algorithms; graph modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last several years, we have witnessed major advancements in the development of sensor technologies and wearable devices with the goal of collecting various types of data in many application domains. Based on such technologies, many commercial products have swamped the market and found their way onto the wrists, ankles, and belts of many users. Although these developments are certainly welcome, so much is left to be done to take full advantage of the data gathered from such devices. The most critical missing component is the lack of advanced data analytics. In the case of health monitoring, like many aspects of healthcare, the focus has been primarily on producing devices with data collection capabilities rather than developing advanced models for analyzing the available data.

In this Special Issues of Sensors, we attempt to fill this knowledge gap by soliciting papers related to data analytics that connect mobility and heath. New algorithms and tools can be applied to analyze big mobility data and reveal new useful health-related features. Positive results in this direction will pave the way towards a new decision support system that leads to new discoveries in Biomedical research and healthcare applications.

Sensors is a journal that primarily focuses on publishing research associated with the technology of sesors. It also includes, with less frequency, papers that address various applications of sensors. The proposed Special Issue highlights the application side of Sensors and hence enriches the applied research published in Sensors and expands on that aspect of the journal. In particular, by choosing a rather critical application domain related to biomedical studies and healthcare, it builds on the notion of “how useful sensors can be” and takes the application aspect of Sensors to a higher level. Sensors have been used to gather health-relatead data for a while now, but mostly in a casual and non-formal way. The proposed issue emphasizes the concept of extracting useful knowledge from raw data, which is a critical concept in big data analyatics.

Prof. Dr. Hesham H. Ali
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. 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.

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Published Papers (3 papers)

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Research

14 pages, 739 KiB  
Article
Sleep CLIP: A Multimodal Sleep Staging Model Based on Sleep Signals and Sleep Staging Labels
by Weijia Yang, Yuxian Wang, Jiancheng Hu and Tuming Yuan
Sensors 2023, 23(17), 7341; https://doi.org/10.3390/s23177341 - 23 Aug 2023
Cited by 3 | Viewed by 3023
Abstract
Since the release of the contrastive language-image pre-training (CLIP) model designed by the OpenAI team, it has been applied in several fields owing to its high accuracy. Sleep staging is an important method of diagnosing sleep disorders, and the completion of sleep staging [...] Read more.
Since the release of the contrastive language-image pre-training (CLIP) model designed by the OpenAI team, it has been applied in several fields owing to its high accuracy. Sleep staging is an important method of diagnosing sleep disorders, and the completion of sleep staging tasks with high accuracy has always remained the main goal of sleep staging algorithm designers. This study is aimed at designing a multimodal model based on the CLIP model that is more suitable for sleep staging tasks using sleep signals and labels. The pre-training efforts of the model involve five different training sets. Finally, the proposed method is tested on two training sets (EDF-39 and EDF-153), with accuracies of 87.3 and 85.4%, respectively. Full article
(This article belongs to the Special Issue Big Data Analytics of Wireless Sensors and Wearable Devices for Biomedical Applications and Healthcare)
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14 pages, 333 KiB  
Article
Analysis of Dynamic Plantar Pressure and Influence of Clinical-Functional Measures on Their Performance in Subjects with Bimalleolar Ankle Fracture at 6 and 12 Months Post-Surgery
by Mario Fernández-Gorgojo, Diana Salas-Gómez, Pascual Sánchez-Juan, Esther Laguna-Bercero and María Isabel Pérez-Núñez
Sensors 2023, 23(8), 3975; https://doi.org/10.3390/s23083975 - 13 Apr 2023
Cited by 1 | Viewed by 2446
Abstract
Recovery after ankle fracture surgery can be slow and even present functional deficits in the long term, so it is essential to monitor the rehabilitation process objectively and detect which parameters are recovered earlier or later. The aim of this study was (1) [...] Read more.
Recovery after ankle fracture surgery can be slow and even present functional deficits in the long term, so it is essential to monitor the rehabilitation process objectively and detect which parameters are recovered earlier or later. The aim of this study was (1) to evaluate dynamic plantar pressure and functional status in patients with bimalleolar ankle fracture 6 and 12 months after surgery, and (2) to study their degree of correlation with previously collected clinical variables. Twenty-two subjects with bimalleolar ankle fractures and eleven healthy subjects were included in the study. Data collection was performed at 6 and 12 months after surgery and included clinical measurements (ankle dorsiflexion range of motion and bimalleolar/calf circumference), functional scales (AOFAS and OMAS), and dynamic plantar pressure analysis. The main results found in plantar pressure were a lower mean/peak plantar pressure, as well as a lower contact time at 6 and 12 months with respect to the healthy leg and control group and only the control group, respectively (effect size 0.63 ≤ d ≤ 0.97). Furthermore, in the ankle fracture group there is a moderate negative correlation (−0.435 ≤ r ≤ 0.674) between plantar pressures (average and peak) with bimalleolar and calf circumference. The AOFAS and OMAS scale scores increased at 12 months to 84.4 and 80.0 points, respectively. Despite the evident improvement one year after surgery, data collected using the pressure platform and functional scales suggest that recovery is not yet complete. Full article
(This article belongs to the Special Issue Big Data Analytics of Wireless Sensors and Wearable Devices for Biomedical Applications and Healthcare)
21 pages, 4240 KiB  
Article
Noise in ICUs: Review and Detailed Analysis of Long-Term SPL Monitoring in ICUs in Northern Spain
by Awwab Qasim Jumaah Althahab, Branislav Vuksanovic, Mohamed Al-Mosawi, Maria Machimbarrena and Roi Arias
Sensors 2022, 22(23), 9038; https://doi.org/10.3390/s22239038 - 22 Nov 2022
Cited by 6 | Viewed by 2532
Abstract
Intensive care units (ICUs) are busy and noisy areas where patients and professional staff can be exposed to acoustic noise for long periods of time. In many cases, noise levels significantly exceed the levels recommended by the official health organisations. This situation can [...] Read more.
Intensive care units (ICUs) are busy and noisy areas where patients and professional staff can be exposed to acoustic noise for long periods of time. In many cases, noise levels significantly exceed the levels recommended by the official health organisations. This situation can affect not only patient recovery but also professional staff, making ICUs unhealthy work and treatment environments. To introduce the measures and reduce the acoustic noise in the ICU, acoustic noise levels should first be measured and then appropriately analysed. However, in most studies dealing with this problem, measurements have been performed manually over short periods, leading to limited data being collected. They are usually followed by insufficient analysis, which in turn results in inadequate measures and noise reduction. This paper reviews recent works dealing with the problem of excessively high noise levels in ICUs and proposes a more thorough analysis of measured data both in the time and frequency domains. Applied frequency domain analysis identifies the cyclic behaviour of the measured sound pressure levels (SPLs) and detects the dominant frequency components in the SPL time series. Moreover, statistical analyses are produced to depict the patterns and SPLs to which patients in ICUs are typically exposed during their stay in the ICU. It has been shown that the acoustic environment is very similar every night, while it can vary significantly during the day or evening periods. However, during most of the observed time, recorded SPLs were significantly above the prescribed values, indicating an urgent need for their control and reduction. To effectively tackle this problem, more detailed information about the nature of noise during each of the analysed periods of the day is needed. This issue will be addressed in the continuation of this project. Full article
(This article belongs to the Special Issue Big Data Analytics of Wireless Sensors and Wearable Devices for Biomedical Applications and Healthcare)
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