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

Deadline for manuscript submissions: 10 October 2024 | Viewed by 8926

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Special Issue Editors

Dr. Ganesh R. Naik

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Guest Editor

College Medicine and Public Health, Flinders University, Bedford Park 5042, Australia
Interests: biomedical engineering; signal processing; sleep, cardio respiratory research; BCI; wearables
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Elena Pirogova

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Guest Editor

School of Engineering, STEM College, RMIT University, Melbourne 3000, Australia
Interests: biomedical engineering; bioelectromagnetics; peptide-based therapeutics; signal processing; bioengineering

Prof. Dr. Margaret Lech

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Guest Editor

School of Engineering, STEM College, RMIT University, Melbourne 3000, Australia
Interests: machine learning; signal processing; speech, image and biomedical signal processing and optimisation

Special Issue Information

Dear Colleagues,

Health monitoring that measures and evaluates physiological signals generated by the human body can provide detailed information about human wellness, thus presenting significant potential for personalized healthcare. There is a great need for the long-term monitoring of human vital physiological parameters, such as EEG, ECG, heart rate, etc., for elderly and chronic patients to take care of their health (effectively) and provide treatment during emergencies. Wearable sensors present an exciting opportunity to measure human physiologic parameters in a continuous, real-time, and nonintrusive manner. The market for wearable medical devices is experiencing unprecedented growth, expecting to increase from USD 8.9 billion in 2018 to USD 29.9 billion in 2023. The fast market growth along with advancements in microfabrication, microelectronics, flexible electronics, nanomaterials, wireless communication, and machine learning techniques have led to the evolution of various biosensors and textile-based wearable technologies.

Physiological monitoring using digital health platforms using Artificial Intelligence (AI) can provide detailed information about health conditions, therefore presenting great potential for personalized healthcare. Digital health monitoring redefines health care in multiple ways. It plays a vital role in this transformation, allowing easy access to relevant data, improving quality of care, and delivering value to patients, healthcare practitioners, hospitals, and governments.

In this Special Issue, we want to build a bridge between different scientific disciplines and offer highly innovative researchers in various fields a platform to exchange research in this exciting and emerging field: Sensors for Physiological Monitoring and Digital Health.

We, the Guest Editors of this Special Issue, represent research backgrounds in biomedical signal processing, health informatics, artificial intelligence, mobility research, and bioinformatics, focusing on biomedical applications and sports science. We stand for the highly interdisciplinary approach that is essential in research in this emerging scientific field and highly anticipate submissions from a broad range of specialties to this Special Issue.

Dr. Ganesh R. Naik
Prof. Dr. Elena Pirogova
Prof. Dr. Margaret Lech
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.

Published Papers (4 papers)

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Research

19 pages, 3347 KiB

Open AccessArticle

Non-Invasive Blood Pressure Sensing via Machine Learning

by Filippo Attivissimo, Vito Ivano D’Alessandro, Luisa De Palma, Anna Maria Lucia Lanzolla and Attilio Di Nisio

Sensors 2023, 23(19), 8342; https://doi.org/10.3390/s23198342 - 09 Oct 2023

Cited by 2 | Viewed by 1458

Abstract

In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard. Full article

(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health)

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17 pages, 5115 KiB

Open AccessArticle

Sensor Selection for Tidal Volume Determination via Linear Regression—Impact of Lasso versus Ridge Regression

by Bernhard Laufer, Paul D. Docherty, Rua Murray, Sabine Krueger-Ziolek, Nour Aldeen Jalal, Fabian Hoeflinger, Stefan J. Rupitsch, Leonhard Reindl and Knut Moeller

Sensors 2023, 23(17), 7407; https://doi.org/10.3390/s23177407 - 25 Aug 2023

Viewed by 777

Abstract

The measurement of respiratory volume based on upper body movements by means of a smart shirt is increasingly requested in medical applications. This research used upper body surface motions obtained by a motion capture system, and two regression methods to determine the optimal selection and placement of sensors on a smart shirt to recover respiratory parameters from benchmark spirometry values. The results of the two regression methods (Ridge regression and the least absolute shrinkage and selection operator (Lasso)) were compared. This work shows that the Lasso method offers advantages compared to the Ridge regression, as it provides sparse solutions and is more robust to outliers. However, both methods can be used in this application since they lead to a similar sensor subset with lower computational demand (from exponential effort for full exhaustive search down to the order of O (n²)). A smart shirt for respiratory volume estimation could replace spirometry in some cases and would allow for a more convenient measurement of respiratory parameters in home care or hospital settings. Full article

(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health)

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19 pages, 11434 KiB

Open AccessArticle

Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning

by Yi-Hsuan Cheng, Margaret Lech and Richardt Howard Wilkinson

Sensors 2023, 23(7), 3468; https://doi.org/10.3390/s23073468 - 26 Mar 2023

Cited by 4 | Viewed by 2060

Abstract

Sleep scoring involves the inspection of multimodal recordings of sleep data to detect potential sleep disorders. Given that symptoms of sleep disorders may be correlated with specific sleep stages, the diagnosis is typically supported by the simultaneous identification of a sleep stage and a sleep disorder. This paper investigates the automatic recognition of sleep stages and disorders from multimodal sensory data (EEG, ECG, and EMG). We propose a new distributed multimodal and multilabel decision-making system (MML-DMS). It comprises several interconnected classifier modules, including deep convolutional neural networks (CNNs) and shallow perceptron neural networks (NNs). Each module works with a different data modality and data label. The flow of information between the MML-DMS modules provides the final identification of the sleep stage and sleep disorder. We show that the fused multilabel and multimodal method improves the diagnostic performance compared to single-label and single-modality approaches. We tested the proposed MML-DMS on the PhysioNet CAP Sleep Database, with VGG16 CNN structures, achieving an average classification accuracy of 94.34% and

F_{1}

score of 0.92 for sleep stage detection (six stages) and an average classification accuracy of 99.09% and

F_{1}

score of 0.99 for sleep disorder detection (eight disorders). A comparison with related studies indicates that the proposed approach significantly improves upon the existing state-of-the-art approaches. Full article

(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health)

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21 pages, 868 KiB

Open AccessArticle

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors

by Muhammad Tausif Irshad, Muhammad Adeel Nisar, Xinyu Huang, Jana Hartz, Olaf Flak, Frédéric Li, Philip Gouverneur, Artur Piet, Kerstin M. Oltmanns and Marcin Grzegorzek

Sensors 2022, 22(20), 7711; https://doi.org/10.3390/s22207711 - 11 Oct 2022

Cited by 12 | Viewed by 3655

Abstract

The perception of hunger and satiety is of great importance to maintaining a healthy body weight and avoiding chronic diseases such as obesity, underweight, or deficiency syndromes due to malnutrition. There are a number of disease patterns, characterized by a chronic loss of this perception. To our best knowledge, hunger and satiety cannot be classified using non-invasive measurements. Aiming to develop an objective classification system, this paper presents a multimodal sensory system using associated signal processing and pattern recognition methods for hunger and satiety detection based on non-invasive monitoring. We used an Empatica E4 smartwatch, a RespiBan wearable device, and JINS MEME smart glasses to capture physiological signals from five healthy normal weight subjects inactively sitting on a chair in a state of hunger and satiety. After pre-processing the signals, we compared different feature extraction approaches, either based on manual feature engineering or deep feature learning. Comparative experiments were carried out to determine the most appropriate sensor channel, device, and classifier to reliably discriminate between hunger and satiety states. Our experiments showed that the most discriminative features come from three specific sensor modalities: Electrodermal Activity (EDA), infrared Thermopile (Tmp), and Blood Volume Pulse (BVP). Full article

(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health)

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