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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 (20 April 2024) | Viewed by 3887

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Dr. Georgios Dimitrakopoulos

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Guest Editor

Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
Interests: cognitive neuroscience; graph theory; machine learning; data science; computational biology

Prof. Dr. George Matsopoulos

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Guest Editor

BIO-Medical Informatics Group, National Technical University of Athens, Athens, Greece
Interests: medical image processing; biosignal analysis; medical decision support systems; 3D visualisation techniques; fMRI data processing; medial information systems
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Special Issue Information

Dear Colleagues,

Wearable medical devices and rehabilitation treatments often rely on sensors to collect data and monitor patients' health conditions in real-time. These devices are designed to be worn on the body or integrated into clothing or accessories, enabling continuous monitoring of various health parameters or providing medical interventions. The wearable devices are usually non-invasive, comfortable, and offer convenience for both patients and healthcare providers. They can monitor various physiological parameters, such as blood pressure, respiration, temperature, electrocardiogram (EGC), electroencephalography (EEG), sleep patterns, etc. Indicative examples of wearable medical devices include fitness trackers, smartwatches with ECG, continuous glucose monitors, wearable blood pressure monitors, wearable neurostimulation devices, and other sensors embedded on clothing.

Dr. Georgios Dimitrakopoulos
Prof. Dr. George Matsopoulos
Guest Editors

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Keywords

wearable medical devices
biomonitoring
physiological signals
real-time patient monitoring
Internet of Things
signal processing

Published Papers (4 papers)

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Research

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15 pages, 2023 KiB

Open AccessArticle

A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors

by Anthony Bawa, Konstantinos Banitsas and Maysam Abbod

Sensors 2024, 24(5), 1500; https://doi.org/10.3390/s24051500 - 26 Feb 2024

Viewed by 521

Abstract

Gait disorder is common among people with neurological disease and musculoskeletal disorders. The detection of gait disorders plays an integral role in designing appropriate rehabilitation protocols. This study presents a clinical gait analysis of patients with polymyalgia rheumatica to determine impaired gait patterns using machine learning models. A clinical gait assessment was conducted at KATH hospital between August and September 2022, and the 25 recruited participants comprised 18 patients and 7 control subjects. The demographics of the participants follow: age 56 years ± 7, height 175 cm ± 8, and weight 82 kg ± 10. Electromyography data were collected from four strained hip muscles of patients, which were the rectus femoris, vastus lateralis, biceps femoris, and semitendinosus. Four classification models were used—namely, support vector machine (SVM), rotation forest (RF), k-nearest neighbors (KNN), and decision tree (DT)—to distinguish the gait patterns for the two groups. SVM recorded the highest accuracy of 85% among the classifiers, while KNN had 75%, RF had 80%, and DT had the lowest accuracy of 70%. Furthermore, the SVM classifier had the highest sensitivity of 92%, while RF had 86%, DT had 90%, and KNN had the lowest sensitivity of 84%. The classifiers achieved significant results in discriminating between the impaired gait pattern of patients with polymyalgia rheumatica and control subjects. This information could be useful for clinicians designing therapeutic exercises and may be used for developing a decision support system for diagnostic purposes. Full article

(This article belongs to the Special Issue Sensors for Wearable Medical Devices and Rehabilitation Treatments)

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21 pages, 9011 KiB

Open AccessArticle

Multi-Modal Spectroscopic Assessment of Skin Hydration

by Iman M. Gidado, Ifeabunike I. Nwokoye, Iasonas F. Triantis, Meha Qassem and Panicos A. Kyriacou

Sensors 2024, 24(5), 1419; https://doi.org/10.3390/s24051419 - 22 Feb 2024

Viewed by 651

Abstract

Human skin acts as a protective barrier, preserving bodily functions and regulating water loss. Disruption to the skin barrier can lead to skin conditions and diseases, emphasizing the need for skin hydration monitoring. The gold-standard sensing method for assessing skin hydration is the Corneometer, monitoring the skin’s electrical properties. It relies on measuring capacitance and has the advantage of precisely detecting a wide range of hydration levels within the skin’s superficial layer. However, measurement errors due to its front end requiring contact with the skin, combined with the bipolar configuration of the electrodes used and discrepancies due to variations in various interfering analytes, often result in significant inaccuracy and a need to perform measurements under controlled conditions. To overcome these issues, we explore the merits of a different approach to sensing electrical properties, namely, a tetrapolar bioimpedance sensing approach, with the merits of a novel optical sensing modality. Tetrapolar bioimpedance allows for the elimination of bipolar measurement errors, and optical spectroscopy allows for the identification of skin water absorption peaks at wavelengths of 970 nm and 1450 nm. Employing both electrical and optical sensing modalities through a multimodal approach enhances skin hydration measurement sensitivity and validity. This layered approach may be particularly beneficial for minimising errors, providing a more robust and comprehensive tool for skin hydration assessment. An ex vivo desorption experiment was carried out on fresh porcine skin, and an in vivo indicative case study was conducted utilising the developed optical and bioimpedance sensing devices. Expected outcomes were expressed from both techniques, with an increase in the output of the optical sensor voltage and a decrease in bioimpedance as skin hydration decreased. MLR models were employed, and the results presented strong correlations (R-squared = 0.996 and p-value = 6.45 × 10⁻²¹), with an enhanced outcome for hydration parameters when both modalities were combined as opposed to independently, highlighting the advantage of the multimodal sensing approach for skin hydration assessment. Full article

(This article belongs to the Special Issue Sensors for Wearable Medical Devices and Rehabilitation Treatments)

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23 pages, 4248 KiB

Open AccessArticle

Age and Gender Impact on Heart Rate Variability towards Noninvasive Glucose Measurement

by Aleksandar Stojmenski, Marjan Gusev, Ivan Chorbev, Stojancho Tudjarski, Lidija Poposka and Marija Vavlukis

Sensors 2023, 23(21), 8697; https://doi.org/10.3390/s23218697 - 25 Oct 2023

Viewed by 1251

Abstract

Heart rate variability (HRV) parameters can reveal the performance of the autonomic nervous system and possibly estimate the type of its malfunction, such as that of detecting the blood glucose level. Therefore, we aim to find the impact of other factors on the proper calculation of HRV. In this paper, we research the relation between HRV and the age and gender of the patient to adjust the threshold correspondingly to the noninvasive glucose estimator that we are developing and improve its performance. While most of the literature research so far addresses healthy patients and only short- or long-term HRV, we apply a more holistic approach by including both healthy patients and patients with arrhythmia and different lengths of HRV measurements (short, middle, and long). The methods necessary to determine the correlation are (i) point biserial correlation, (ii) Pearson correlation, and (iii) Spearman rank correlation. We developed a mathematical model of a linear or monotonic dependence function and a machine learning and deep learning model, building a classification detector and level estimator. We used electrocardiogram (ECG) data from 4 different datasets consisting of 284 subjects. Age and gender influence HRV with a moderate correlation value of 0.58. This work elucidates the intricate interplay between individual input and output parameters compared with previous efforts, where correlations were found between HRV and blood glucose levels using deep learning techniques. It can successfully detect the influence of each input. Full article

(This article belongs to the Special Issue Sensors for Wearable Medical Devices and Rehabilitation Treatments)

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Review

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18 pages, 2306 KiB

Open AccessReview

Non-Immersive Virtual Reality-Based Therapy Applied in Cardiac Rehabilitation: A Systematic Review with Meta-Analysis

by Ana Belén Peinado-Rubia, Alberto Verdejo-Herrero, Esteban Obrero-Gaitán, María Catalina Osuna-Pérez, Irene Cortés-Pérez and Héctor García-López

Sensors 2024, 24(3), 903; https://doi.org/10.3390/s24030903 - 30 Jan 2024

Viewed by 901

Abstract

Background: The aim of this systematic review with meta-analysis was to assess the effectiveness of non-immersive virtual reality (niVR) active videogames in patients who underwent cardiac rehabilitation (CR). Methods: A systematic review with meta-analysis, according to the PRISMA guidelines and previously registered in PROSPERO (CRD42023485240), was performed through a literature search in PubMed (Medline), SCOPUS, WOS, and PEDro since inception to 21 November 2023. We included randomized controlled trials (RCTs) that assessed the effectiveness of an niVR intervention, in comparison with conventional CR and usual care, on aerobic capacity and cardiovascular endurance (physical function), anxiety, depression, and quality of life (QoL). The risk of bias in individual studies was assessed using the Cochrane risk of bias tool. Effect size was estimated using Cohen’s standardized mean difference (SMD) and its 95% confidence interval (95% CI) in a random-effects model. Results: Nine RCT that met the inclusion criteria were included in the meta-analysis. The meta-analysis showed a moderate-to-large effect favoring niVR active videogames included in CR in increasing aerobic capacity and cardiovascular endurance (SMD = 0.74; 95% CI 0.11 to 1.37; p = 0.021) and reducing anxiety (SMD = −0.66; 95% CI −1.13 to −0.2; p = 0.006). Only 4.8% of patients reported adverse events while performing niVR active videogames. Conclusions: Inclusion of niVR active videogames in CR programs is more effective than conventional CR in improving aerobic capacity and cardiovascular endurance and in reducing anxiety. Full article

(This article belongs to the Special Issue Sensors for Wearable Medical Devices and Rehabilitation Treatments)

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