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Smart Sensing Technologies for Human-Centered Healthcare: Research and Applications

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

Deadline for manuscript submissions: 25 May 2024 | Viewed by 15891

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Special Issue Editors

Dr. Thanasis Tsanas

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Guest Editor

1. Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh EH16 4UX, UK
2. School of Mathematics, University of Edinburgh, Edinburgh EH16 4UX, UK
3. Alan Turing Institute, London, UK
Interests: biomedical signal processing; statistical machine learning; time-series analysis; wearable sensors
Special Issues, Collections and Topics in MDPI journals

Dr. Andreas Triantafyllidis

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Guest Editor

Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Interests: mobile health; medical informatics; pervasive computing; artificial intelligence; computerised decision support
Special Issues, Collections and Topics in MDPI journals

Dr. Asimina Kiourti

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Guest Editor

ElectroScience Laboratory, Dept. of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA
Interests: bioelectromagnetics; wearable sensors; implantable sensors; antennas for body area applications
Special Issues, Collections and Topics in MDPI journals

Dr. Siddharth Arora

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Guest Editor

Saïd Business School, University of Oxford, Oxford, UK
Interests: time-series analysis; biomedical applications; machine learning

Special Issue Information

Dear Colleagues,

Smart sensing technologies, including wearable sensors, ambient sensors, and smartphone sensor modalities, have been receiving increased research and commercial attention over the last 5–10 years. Indicatively, wearables are now already a strong multi-billion dollar industry, and along with smartphone apps, have been widely embraced by the public to keep track of fitness and health aspects. Similarly, other sensing modalities, including ambient sensing, can be used in innovative ways to inform healthcare provision and have enormous potential in transforming contemporary healthcare delivery. Collectively, the data that are collected from these devices, along with emerging advances in data science and machine learning, are leading to unprecedented opportunities in rethinking human-centered healthcare.

This Special Issue will focus on sensing technologies for healthcare to provide a state-of-the-art forum to report on both algorithmic-focused and application-focused papers, following the organization of the 2022 Pervasive Computing Technologies for Healthcare conference (https://pervasivehealth.eai-conferences.org/2022/).

We invite extensions of some of the best works presented at the conference (authors need to ensure there is at least 50% new content compared to the conference paper), along with papers submitted within the open call, taking also into account the target audience of Sensors.

This Special Issue fits excellently with the aims and scope of Sensors. It aims to provide a forum to document advances in sensor technologies and mining sensor data to improve healthcare. We aim to attract both algorithmic-focused studies and application-focused studies, as long as there is a clear link between the use of sensing technologies (interpreted broadly) and healthcare. Many of the key areas that we will be soliciting manuscripts from align excellently with the keywords that are identified in the formal page of the journal.

Prospective authors are encouraged to contact the Guest Editors in advance if they have any questions.

Dr. Thanasis Tsanas
Dr. Andreas Triantafyllidis
Dr. Asimina Kiourti
Dr. Siddharth Arora
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

actigraphy analysis
wearable sensors
ambient sensors
smartwatches
smartphones
data mining
biomedical signal processing and image processing
pattern recognition
telemonitoring
machine/deep learning and artificial intelligence in sensing and imaging

Published Papers (8 papers)

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Research

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12 pages, 1903 KiB

Open AccessArticle

Causal Analysis of Physiological Sleep Data Using Granger Causality and Score-Based Structure Learning

by Alex Thomas, Mahesan Niranjan and Julian Legg

Sensors 2023, 23(23), 9455; https://doi.org/10.3390/s23239455 - 28 Nov 2023

Viewed by 614

Abstract

Understanding how the human body works during sleep and how this varies in the population is a task with significant implications for medicine. Polysomnographic studies, or sleep studies, are a common diagnostic method that produces a significant quantity of time-series sensor data. This study seeks to learn the causal structure from data from polysomnographic studies carried out on 600 adult volunteers in the United States. Two methods are used to learn the causal structure of these data: the well-established Granger causality and “DYNOTEARS”, a modern approach that uses continuous optimisation to learn dynamic Bayesian networks (DBNs). The results from the two methods are then compared. Both methods produce graphs that have a number of similarities, including the mutual causation between electrooculogram (EOG) and electroencephelogram (EEG) signals and between sleeping position and SpO2 (blood oxygen level). However, DYNOTEARS, unlike Granger causality, frequently finds a causal link to sleeping position from the other variables. Following the creation of these causal graphs, the relationship between the discovered causal structure and the characteristics of the participants is explored. It is found that there is an association between the waist size of a participant and whether a causal link is found between the electrocardiogram (ECG) measurement and the EOG and EEG measurements. It is concluded that a person’s body shape appears to impact the relationship between their heart and brain during sleep and that Granger causality and DYNOTEARS can produce differing results on real-world data. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Human-Centered Healthcare: Research and Applications)

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30 pages, 969 KiB

Open AccessArticle

Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework to Identify Barriers and Facilitators for the Implementation of Digital Twins in Cardiovascular Medicine

by Peter D. Winter and Timothy J. A. Chico

Sensors 2023, 23(14), 6333; https://doi.org/10.3390/s23146333 - 12 Jul 2023

Cited by 2 | Viewed by 1756

Abstract

A digital twin is a computer-based “virtual” representation of a complex system, updated using data from the “real” twin. Digital twins are established in product manufacturing, aviation, and infrastructure and are attracting significant attention in medicine. In medicine, digital twins hold great promise to improve prevention of cardiovascular diseases and enable personalised health care through a range of Internet of Things (IoT) devices which collect patient data in real-time. However, the promise of such new technology is often met with many technical, scientific, social, and ethical challenges that need to be overcome—if these challenges are not met, the technology is therefore less likely on balance to be adopted by stakeholders. The purpose of this work is to identify the facilitators and barriers to the implementation of digital twins in cardiovascular medicine. Using, the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, we conducted a document analysis of policy reports, industry websites, online magazines, and academic publications on digital twins in cardiovascular medicine, identifying potential facilitators and barriers to adoption. Our results show key facilitating factors for implementation: preventing cardiovascular disease, in silico simulation and experimentation, and personalised care. Key barriers to implementation included: establishing real-time data exchange, perceived specialist skills required, high demand for patient data, and ethical risks related to privacy and surveillance. Furthermore, the lack of empirical research on the attributes of digital twins by different research groups, the characteristics and behaviour of adopters, and the nature and extent of social, regulatory, economic, and political contexts in the planning and development process of these technologies is perceived as a major hindering factor to future implementation. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Human-Centered Healthcare: Research and Applications)

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17 pages, 2540 KiB

Open AccessArticle

Real-Time Magnetocardiography with Passive Miniaturized Coil Array in Earth Ambient Field

by Keren Zhu and Asimina Kiourti

Sensors 2023, 23(12), 5567; https://doi.org/10.3390/s23125567 - 14 Jun 2023

Viewed by 1428

Abstract

We demonstrate a magnetocardiography (MCG) sensor that operates in non-shielded environments, in real-time, and without the need for an accompanying device to identify the cardiac cycles for averaging. We further validate the sensor’s performance on human subjects. Our approach integrates seven (7) coils, previously optimized for maximum sensitivity, into a coil array. Based on Faraday’s law, magnetic flux from the heart is translated into voltage across the coils. By leveraging digital signal processing (DSP), namely, bandpass filtering and averaging across coils, MCG can be retrieved in real-time. Our coil array can monitor real-time human MCG with clear QRS complexes in non-shielded environments. Intra- and inter-subject variability tests confirm repeatability and accuracy comparable to gold-standard electrocardiography (ECG), viz., a cardiac cycle detection accuracy of >99.13% and averaged R-R interval accuracy of <5.8 ms. Our results confirm the feasibility of real-time R-peak detection using the MCG sensor, as well as the ability to retrieve the full MCG spectrum as based upon the averaging of cycles identified via the MCG sensor itself. This work provides new insights into the development of accessible, miniaturized, safe, and low-cost MCG tools. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Human-Centered Healthcare: Research and Applications)

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30 pages, 1914 KiB

Open AccessArticle

From Lab to Real World: Assessing the Effectiveness of Human Activity Recognition and Optimization through Personalization

by Marija Stojchevska, Mathias De Brouwer, Martijn Courteaux, Femke Ongenae and Sofie Van Hoecke

Sensors 2023, 23(10), 4606; https://doi.org/10.3390/s23104606 - 09 May 2023

Cited by 1 | Viewed by 1662

Abstract

Human activity recognition (HAR) algorithms today are designed and evaluated on data collected in controlled settings, providing limited insights into their performance in real-world situations with noisy and missing sensor data and natural human activities. We present a real-world HAR open dataset compiled from a wristband equipped with a triaxial accelerometer. During data collection, participants had autonomy in their daily life activities, and the process remained unobserved and uncontrolled. A general convolutional neural network model was trained on this dataset, achieving a mean balanced accuracy (MBA) of 80%. Personalizing the general model through transfer learning can yield comparable and even superior results using fewer data, with the MBA improving to 85%. To emphasize the issue of insufficient real-world training data, we conducted training of the model using the public MHEALTH dataset, resulting in 100% MBA. However, upon evaluating the MHEALTH-trained model on our real-world dataset, the MBA drops to 62%. After personalizing the model with real-world data, an improvement of 17% in the MBA is achieved. This paper showcases the potential of transfer learning to make HAR models trained in different contexts (lab vs. real-world) and on different participants perform well for new individuals with limited real-world labeled data available. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Human-Centered Healthcare: Research and Applications)

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21 pages, 2378 KiB

Open AccessArticle

Efficacy of Individualized Sensory-Based mHealth Interventions to Improve Distress Coping in Healthcare Professionals: A Multi-Arm Parallel-Group Randomized Controlled Trial

by Hannes Baumann, Luis Heuel, Laura Louise Bischoff and Bettina Wollesen

Sensors 2023, 23(4), 2322; https://doi.org/10.3390/s23042322 - 19 Feb 2023

Cited by 4 | Viewed by 3124

Abstract

Detrimental effects of chronic stress on healthcare professionals have been well-established, but the implementation and evaluation of effective interventions aimed at improving distress coping remains inadequate. Individualized mHealth interventions incorporating sensor feedback have been proposed as a promising approach. This study aimed to investigate the impact of individualized, sensor-based mHealth interventions focusing on stress and physical activity on distress coping in healthcare professionals. The study utilized a multi-arm, parallel group randomized controlled trial design, comparing five intervention groups (three variations of web-based training and two variations of an app training) that represented varying levels of individualization to a control group. Both self-reported questionnaire data (collected using Limesurvey) as well as electrocardiography and accelerometry-based sensory data (collected using Mesana Sensor) were assessed at baseline and post-intervention (after eight weeks). Of the 995 eligible participants, 170 (26%) completed the post-intervention measurement (Group 1: N = 21; Group 2: N = 23; Group 3: N = 7; Group 4: N = 34; Group 5: N = 16; Control Group: N = 69). MANOVA results indicated small to moderate time-by-group interaction effects for physical activity-related outcomes, including moderate to vigorous physical activity (F(1,5) = 5.8, p = ≤0.001, η²p = 0.057) and inactivity disruption (F(1,5) = 11.2, p = <0.001, η²p = 0.100), in the app-based intervention groups, but not for step counts and inactivity. No changes were observed in stress-related heart rate variability parameters over time. Despite a high dropout rate and a complex study design, the individualized interventions showed initial positive effects on physical activity. However, no significant changes in stress-related outcomes were observed, suggesting that the intervention duration was insufficient to induce physiological adaptations that would result in improved distress coping. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Human-Centered Healthcare: Research and Applications)

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21 pages, 2373 KiB

Open AccessArticle

New Insights into Stroke from Continuous Passively Collected Temperature and Sleep Data Using Wrist-Worn Wearables

by Katherine Edgley, Ho-Yan Yvonne Chun, William N. Whiteley and Athanasios Tsanas

Sensors 2023, 23(3), 1069; https://doi.org/10.3390/s23031069 - 17 Jan 2023

Cited by 3 | Viewed by 1865

Abstract

Actigraphy may provide new insights into clinical outcomes and symptom management of patients through passive, continuous data collection. We used the GENEActiv smartwatch to passively collect actigraphy, wrist temperature, and ambient light data from 27 participants after stroke or probable brain transient ischemic attack (TIA) over 42 periods of device wear. We computed 323 features using established algorithms and proposed 25 novel features to characterize sleep and temperature. We investigated statistical associations between the extracted features and clinical outcomes evaluated using clinically validated questionnaires to gain insight into post-stroke recovery. We subsequently fitted logistic regression models to replicate clinical diagnosis (stroke or TIA) and disability due to stroke. The model generalization performance was assessed using a leave-one-subject-out cross validation method with the selected feature subsets, reporting the area under the curve (AUC). We found that several novel features were strongly correlated (

| r | > 0.3

) with stroke symptoms and mental health measures. Using selected novel features, we obtained an AUC of 0.766 to estimate diagnosis and an AUC of 0.749 to estimate whether disability due to stroke was present. Collectively, these findings suggest that features extracted from the temperature smartwatch sensor may reveal additional clinically useful information over and above existing actigraphy-based features. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Human-Centered Healthcare: Research and Applications)

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Review

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15 pages, 658 KiB

Open AccessReview

How Could Sensor-Based Measurement of Physical Activity Be Used in Cardiovascular Healthcare?

by Megan E. Hughes and Timothy J. A. Chico

Sensors 2023, 23(19), 8154; https://doi.org/10.3390/s23198154 - 28 Sep 2023

Viewed by 879

Abstract

Physical activity and cardiovascular disease (CVD) are intimately linked. Low levels of physical activity increase the risk of CVDs, including myocardial infarction and stroke. Conversely, when CVD develops, it often reduces the ability to be physically active. Despite these largely understood relationships, the objective measurement of physical activity is rarely performed in routine healthcare. The ability to use sensor-based approaches to accurately measure aspects of physical activity has the potential to improve many aspects of cardiovascular healthcare across the spectrum of healthcare, from prediction, prevention, diagnosis, and treatment to disease monitoring. This review discusses the potential of sensor-based measurement of physical activity to augment current cardiovascular healthcare. We highlight many factors that should be considered to maximise the benefit and reduce the risks of such an approach. Because the widespread use of such devices in society is already a reality, it is important that scientists, clinicians, and healthcare providers are aware of these considerations. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Human-Centered Healthcare: Research and Applications)

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16 pages, 702 KiB

Open AccessReview

Mobile App Interventions for Parkinson’s Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review

by Andreas Triantafyllidis, Sofia Segkouli, Stelios Zygouris, Christina Michailidou, Konstantinos Avgerinakis, Evangelia Fappa, Sophia Vassiliades, Anastasia Bougea, Nikos Papagiannakis, Ioannis Katakis, Evangelos Mathioudis, Alexandru Sorici, Lidia Bajenaru, Valentina Tageo, Francesco Camonita, Christoniki Magga-Nteve, Stefanos Vrochidis, Ludovico Pedullà, Giampaolo Brichetto, Panagiotis Tsakanikas, Konstantinos Votis and Dimitrios Tzovaras Show full author list Hide full author list

Sensors 2023, 23(7), 3396; https://doi.org/10.3390/s23073396 - 23 Mar 2023

Cited by 2 | Viewed by 3203

Abstract

Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson’s disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient’s physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Human-Centered Healthcare: Research and Applications)

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