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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 8718

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Special Issue Editors

Dr. Ilaria Bortone

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Guest Editor

1. Institute of Clinical Physiology, National Research Council (IFC-CNR), 56124 Pisa, Italy
2. Innovation, Marketing & Technology (IMT Lab)-Expriva SpA, 70056 Molfetta, Italy
Interests: wearables devices; human motion analysis; signal processing; virtual reality

Prof. Dr. Tommaso Di Noia

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Guest Editor

Department of Electrical and Information Engineering, Polytechnic of Bari, 70125 Bari, Italy
Interests: big data and semantic technologies; artificial intelligence and machine learning; recommender systems; cybersecurity
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Azzurra Ragone

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Department of Information Technology, University of Bari, 70121 Bari, Italy
Interests: innovation and transformation; machine learning and recommended systems; big data analytics

Prof. Dr. Rodolfo Sardone

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Guest Editor

Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis”, Research Hospital, 70013 Bari, Italy
Interests: public health/epidemiology; special senses; signal processing; neuroscience

Special Issue Information

Dear Colleagues,

Healthcare diagnostics is going through a digital transformation driven by the use of digitally enabled diagnosis tools and wearables that use AI and its subsets to provide the most accurate results. The emergence of the Internet of Things (IoT) has enabled many changes, as it can connect devices and neural networks for better monitoring and management. However, clinicians still play a significant role in understanding complex medical data for the diagnosis of diseases. Therefore, significant research is necessary to explore how AI technologies can be applied in biosignal processing and healthcare applications to improve detection and prediction performance and support clinical diagnosis. Issues related to big data in health data processing also require exploration in healthcare research.

This Special Issue will provide a forum for high-quality contributions in the modeling, design, and application of AI to all aspects of health data research and healthcare applications. Reviews and surveys on the topic are also welcomed.

The scope and topic of this Special Issue include but are not limited to:

Application of deep learning techniques;
AI methods in health and medicine;
Machine learning techniques in health data analysis.

Dr. Ilaria Bortone
Prof. Dr. Tommaso Di Noia
Prof. Dr. Azzurra Ragone
Prof. Dr. Rodolfo Sardone
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 sensors
big data analytics
AI o Smart health
signal processing
healthcare applications

Published Papers (5 papers)

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Research

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12 pages, 3115 KiB

Open AccessArticle

MSK-TIM: A Telerobotic Ultrasound System for Assessing the Musculoskeletal System

by Zachary Ochitwa, Reza Fotouhi, Scott J. Adams, Adriana Paola Noguera Cundar and Haron Obaid

Sensors 2024, 24(7), 2368; https://doi.org/10.3390/s24072368 - 8 Apr 2024

Viewed by 426

Abstract

The aim of this paper is to investigate technological advancements made to a robotic tele-ultrasound system for musculoskeletal imaging, the MSK-TIM (Musculoskeletal Telerobotic Imaging Machine). The hardware was enhanced with a force feedback sensor and a new controller was introduced. Software improvements were developed which allowed the operator to access ultrasound functions such as focus, depth, gain, zoom, color, and power Doppler controls. The device was equipped with Wi-Fi network capability which allowed the master and slave stations to be positioned in different locations. A trial assessing the system to scan the wrist was conducted with twelve participants, for a total of twenty-four arms. Both the participants and radiologist reported their experience. The images obtained were determined to be of satisfactory quality for diagnosis. The system improvements resulted in a better user and patient experience for the radiologist and participants. Latency with the VPN configuration was similar to the WLAN in our experiments. This research explores several technologies in medical telerobotics and provides insight into how they should be used in future. This study provides evidence to support larger-scale trials of the MSK-TIM for musculoskeletal imaging. Full article

(This article belongs to the Special Issue AI-Enabling Solutions in Healthcare)

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10 pages, 2778 KiB

Open AccessArticle

Effect of Sensor Size, Number and Position under the Foot to Measure the Center of Pressure (CoP) Displacement and Total Center of Pressure (CoPT) Using an Anatomical Foot Model

by Hussein Abou Ghaida, Luiz Poffo, Ronan Le Page and Jean-Marc Goujon

Sensors 2023, 23(10), 4848; https://doi.org/10.3390/s23104848 - 17 May 2023

Viewed by 1135

Abstract

Ambulatory instrumented insoles are widely used in real-time monitoring of the plantar pressure in order to calculate balance indicators such as Center of Pressure (CoP) or Pressure Maps. Such insoles include many pressure sensors; the required number and surface area of the sensors used are usually determined experimentally. Additionally, they follow the common plantar pressure zones, and the quality of measurement is usually strongly related to the number of sensors. In this paper, we experimentally investigate the robustness of an anatomical foot model, combined with a specific learning algorithm, to measure the static displacement of the center of pressure (CoP) and the center of total pressure (CoPT), as a function of the number, size, and position of sensors. Application of our algorithm to the pressure maps of nine healthy subjects shows that only three sensors per foot, with an area of about 1.5 × 1.5 cm², are needed to give a good approximation of the CoP during quiet standing when placed on the main pressure areas. Full article

(This article belongs to the Special Issue AI-Enabling Solutions in Healthcare)

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22 pages, 3199 KiB

Open AccessArticle

Designing an Embedded Feature Selection Algorithm for a Drowsiness Detector Model Based on Electroencephalogram Data

by Blanka Bencsik, István Reményi, Márton Szemenyei and János Botzheim

Sensors 2023, 23(4), 1874; https://doi.org/10.3390/s23041874 - 7 Feb 2023

Cited by 2 | Viewed by 1994

Abstract

Driver fatigue reduces the safety of traditional driving and limits the widespread adoption of self-driving cars; hence, the monitoring and early detection of drivers’ drowsiness plays a key role in driving automation. When representing the drowsiness indicators as large feature vectors, fitting a machine learning model to the problem becomes challenging, and the problem’s perspicuity decreases, making dimensionality reduction crucial in practice. For this reason, we propose an embedded feature selection algorithm that can be later utilized as a building block in the system development of a neural network-based drowsiness detector. We have adopted a technique: a so-called Feature Prune Layer is placed in front of the first layer in the architecture; as a result, its weights change regarding the importance of the corresponding input features and are deleted iteratively until the desired number is reached. We test the algorithm on EEG data, as it is one of the best indicators of drowsiness based on the literature. The proposed FS algorithm is able to reduce the original feature set by 95% with only 1% degradation in precision, while the precision increases by 1.5% and 2.7% respectively when selecting the top 10% and top 20% of the initial features. Moreover, the proposed method outperforms the widely popular Principal Component Analysis and the Chi-squared test when reducing the original feature set by 95%: it achieves 24.3% and 3.2% higher precision respectively. Full article

(This article belongs to the Special Issue AI-Enabling Solutions in Healthcare)

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16 pages, 3257 KiB

Open AccessArticle

Introducing the Monitoring Equipment Mask Environment

by Andrea Pazienza and Daniele Monte

Sensors 2022, 22(17), 6365; https://doi.org/10.3390/s22176365 - 24 Aug 2022

Cited by 8 | Viewed by 2586

Abstract

Filter face masks are Respiratory Protective Equipment designed to protect the wearer from various hazards, suit various health situations, and match the specific requirements of the wearer. Current traditional face masks have several limitations. In this paper, we present (ME)

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, the [...] Read more.

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, the Monitoring Equipment Mask Environment: an innovative reusable 3D-printed eco-sustainable mask with an interchangeable filter. (ME)

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is equipped with multiple vital sensors on board, connected to a system-on-a-chip micro-controller with computational capabilities, Bluetooth communication, and a rechargeable battery that allows continuous monitoring of the wearer’s vital signs. It monitors body temperature, heart rate, and oxygen saturation in a non-invasive, strategically positioned way. (ME)

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is accompanied by a mobile application that provides users’ health information. Furthermore, through Edge Computing Artificial Intelligence (Edge AI) modules, it is possible to detect an abnormal and early symptoms linked to possible pathologies, possibly linked to the respiratory or cardiovascular tract, and therefore perform predictive analysis, launch alerts, and recommendations. To validate the feasibility of embedded in-app Edge AI modules, we tested a machine learning model able to distinguish COVID-19 versus seasonal influenza using only vital signs. By generating new synthetic data, we confirm the highly reliable performances of such a model, with an accuracy of

94.80 %

. Full article

(This article belongs to the Special Issue AI-Enabling Solutions in Healthcare)

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Review

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18 pages, 1220 KiB

Open AccessReview

Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning

by Caroline Ferguson, Yu Zhang, Cristiano Palego and Xuanhong Cheng

Sensors 2023, 23(13), 5990; https://doi.org/10.3390/s23135990 - 28 Jun 2023

Viewed by 1353

Abstract

Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells. Full article

(This article belongs to the Special Issue AI-Enabling Solutions in Healthcare)

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