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eHealth Platforms and Sensors for Health and Human Activity Monitoring
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eHealth Platforms and Sensors for Health and Human Activity Monitoring

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 3327

Special Issue Editors

Dr. Safak Dogan

E-Mail Website
Guest Editor
Institute for Digital Technologies, Loughborough University London, London E20 3BS, UK
Interests: digital media processing; immersive and interactive technologies and media quality; applied machine learning and neural networks in digital signal processing, cybersecurity reinforcement, and health data analytics; cybersecurity/privacy protection tools and solutions applied in digital health, care, and wellbeing
Dr. Xiyu Shi

E-Mail Website
Guest Editor
Institute for Digital Technologies, Loughborough University London, London E20 3BS, UK
Interests: explainable AI; deep generative AI models; cyber security; digital signal processing
Dr. Yogachandran Rahulamathavan

E-Mail Website
Guest Editor
Institute for Digital Technologies, Loughborough University London, London E20 3BS, UK
Interests: privacy-preserving techniques; applied cryptography; homomorphic machine learning; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andrew Weightman

E-Mail Website
Guest Editor
Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M13 9PL, UK
Interests: bio-engineering; innovative manufacturing; design; data acquisition
Dr. Glen Cooper

E-Mail Website
Guest Editor
Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M13 9PL, UK
Interests: biomedical modelling; medical device development; lower limb biomechanics; novel measurement devices to understand medical problems
Prof. Dr. Helen Dawes

E-Mail Website
Guest Editor
College of Medicine and Health, St. Luke’s Campus, University of Exeter, Exeter EX1 2LU, UK
Interests: exercise and rehabilitation, movement science mechanisms, and validating innovative outcome measures
Dr. Katherine Bradbury

E-Mail Website
Guest Editor
Centre for Clinical and Community Applications of Health Psychology (CCCAHP), University of Southampton, Southampton SO17 1BJ, UK
Interests: help people developing and evaluating complex interventions, with particular expertise in digital behaviour change interventions

Special Issue Information

Dear Colleagues,

Proliferation of eHealth platforms has been largely motivated by finding viable solutions for releasing immense pressures building on the health and care systems that are struggling to cope with the growing number of health management demands across the globe. Through digital transformation, world nations have adopted varying degrees of digitalisation in their health and care systems to date. Such digital health platforms aim at building and managing patient data records, which include those collated through means of health and human activity monitoring that relies on a multitude of multi-modal sensor and actuation technologies, smart wearables, and Internet of Things (IoT) networks. Through health analytics methods, the collated data can be analysed and necessary responsive steps can be planned. To further reduce growing pressure and costs on the health and care systems while increasing their efficiency and effectiveness in dealing with patient requests and conditions, remote health monitoring and self-managed care are deemed key. Yet, all of these come with caveats, as eHealth platforms and particularly sensor-based health data contain personal information, which is susceptible to cybersecurity threats and risk of privacy compromises. Further, in a digital transformation scenario, not all stakeholders may be able or willing to adopt technology-enhanced solutions offered. Thus, any offering made should consider these caveats and more, and hence cater to all.

Accordingly, this Special Issue aims to call for innovative research work presentations on how to realise eHealth platforms that can capture the essence of providing digital technology-enhanced solutions for remote care, self-management, health and human activity monitoring, health analytics and informatics, while considering security, trust, privacy, user acceptance and adoption as core traits. As such, we invite submissions of original research and novel work on a wide range of topics, such as (but are not limited to):

  • eHealth and mHealth platforms
  • Multi-modal sensor technologies for health and human activity monitoring
  • Actuator technologies for remote and predictive healthcare and management
  • Sensor data fusion and smart health diagnostics
  • Health analytics and informatics, including deep learning, and machine learning techniques and applications to wearable and sensor data
  • Smart wearable and mobile technologies, Internet of Things, and sensor networks for physiological signal monitoring
  • Emotion and well-being recognition from wearable and mobile systems data, e.g., speech recognition, social signal processing, facial expression analysis
  • Personalised health management and self-managed care
  • Cybersecurity and privacy protection in eHealth and mHealth platforms
  • User acceptance and adoption of digital health and care technologies
  • Energy-aware solutions in wearable, sensor, and IoT networks for eHealth/mHealth platforms
  • Quality of life monitoring, management, and improvement

Dr. Safak Dogan
Dr. Xiyu Shi
Dr. Yogachandran Rahulamathavan
Prof. Dr. Andrew Weightman
Dr. Glen Cooper
Prof. Dr. Helen Dawes
Dr. Katherine Bradbury
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

  • digital health (eHealth/mHealth) platforms
  • remote and predictive healthcare
  • self-managed care
  • health monitoring and management
  • human activity monitoring
  • sensing and actuation
  • smart wearables, and Internet of Things (IoT) networks
  • health analytics and AI
  • cybersecurity and privacy protection
  • user acceptance and adoption

Published Papers (3 papers)

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Research

21 pages, 3597 KiB  
Article
Evaluating the Effectiveness of the SleepTracker App for Detecting Anxiety- and Depression-Related Sleep Disturbances
by Doaa Alamoudi, Ian Nabney and Esther Crawley
Sensors 2024, 24(3), 722; https://doi.org/10.3390/s24030722 - 23 Jan 2024
Viewed by 811
Abstract
This study emphasises the critical role of quality sleep in physical and mental well-being, exploring its impact on bodily recovery and cognitive function. Investigating poor sleep quality in approximately 40% of individuals with insomnia symptoms, the research delves into its potential diagnostic relevance [...] Read more.
This study emphasises the critical role of quality sleep in physical and mental well-being, exploring its impact on bodily recovery and cognitive function. Investigating poor sleep quality in approximately 40% of individuals with insomnia symptoms, the research delves into its potential diagnostic relevance for depression and anxiety, with a focus on intervention in mental health by understanding sleep patterns, especially in young individuals. This study includes an exploration of phone usage habits among young adults during PPI sessions, providing insights for developing the SleepTracker app. This pivotal tool utilises phone usage and movement data from mobile device sensors to identify indicators of anxiety or depression, with participant information organised comprehensively in a table categorising condition related to phone usage and movement data. The analysis compares this data with survey results, incorporating scores from the Sleep Condition Indicator (SCI), Patient Health Questionnaire-9 (PHQ-9), and Generalised Anxiety Disorder-7 (GAD-7). Generated confusion matrices offer a detailed overview of the relationship between sleep metrics, phone usage, and movement data. In summary, this study reveals the accurate detection of negative sleep disruption instances by the classifier. However, improvements are needed in identifying positive instances, reflected in the F1-score of 0.5 and a precision result of 0.33. While early intervention potential is significant, this study emphasises the need for a larger participant pool to enhance the model’s performance. Full article
(This article belongs to the Special Issue eHealth Platforms and Sensors for Health and Human Activity Monitoring)
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11 pages, 4061 KiB  
Article
Design and Verification of Integrated Circuitry for Real-Time Frailty Monitoring
by Luis Rodriguez-Cobo, Guillermo Diaz-SanMartin, Jose Francisco Algorri, Carlos Fernandez-Viadero, Jose Miguel Lopez-Higuera and Adolfo Cobo
Sensors 2024, 24(1), 29; https://doi.org/10.3390/s24010029 - 20 Dec 2023
Viewed by 872
Abstract
In this study, a new wireless electronic circuitry to analyze weight distribution was designed and incorporated into a chair to gather data related to common human postures (sitting and standing up). These common actions have a significant impact on various motor capabilities, including [...] Read more.
In this study, a new wireless electronic circuitry to analyze weight distribution was designed and incorporated into a chair to gather data related to common human postures (sitting and standing up). These common actions have a significant impact on various motor capabilities, including gait parameters, fall risk, and information on sarcopenia. The quality of these actions lacks an absolute measurement, and currently, there is no qualitative and objective metric for it. To address this, the designed analyzer introduces variables like Smoothness and Percussion to provide more information and objectify measurements in the assessment of stand-up/sit-down actions. Both the analyzer and the proposed variables offer additional information that can objectify assessments depending on the clinical eye of the physicians. Full article
(This article belongs to the Special Issue eHealth Platforms and Sensors for Health and Human Activity Monitoring)
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20 pages, 3334 KiB  
Article
FoGGAN: Generating Realistic Parkinson’s Disease Freezing of Gait Data Using GANs
by Nikolaos Peppes, Panagiotis Tsakanikas, Emmanouil Daskalakis, Theodoros Alexakis, Evgenia Adamopoulou and Konstantinos Demestichas
Sensors 2023, 23(19), 8158; https://doi.org/10.3390/s23198158 - 28 Sep 2023
Cited by 2 | Viewed by 1036
Abstract
Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding [...] Read more.
Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson’s Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues. In this light, this study focuses on a data augmentation solution engaging Generative Adversarial Networks (GANs) using a freezing of gait (FoG) symptom dataset as input. The data generated by the so-called FoGGAN architecture presented in this study are almost identical to the original as concluded by a variety of similarity metrics. This highlights the significance of such solutions as they can provide credible synthetically generated data which can be utilized as training dataset inputs to AI applications. Additionally, a DNN classifier’s performance is evaluated using three different evaluation datasets and the accuracy results were quite encouraging, highlighting that the FOGGAN solution could lead to the alleviation of the data shortage matter. Full article
(This article belongs to the Special Issue eHealth Platforms and Sensors for Health and Human Activity Monitoring)
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