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eHealth Platforms and Sensors for Health and Human Activity Monitoring

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

Deadline for manuscript submissions: 25 February 2025 | Viewed by 18860

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Special Issue Editors

Dr. Safak Dogan

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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

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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

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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

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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

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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

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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

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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

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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

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Published Papers (11 papers)

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Research

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18 pages, 7087 KiB

Open AccessArticle

Steady-State Visual Evoked Potential-Based Brain–Computer Interface System for Enhanced Human Activity Monitoring and Assessment

by Yuankun Chen, Xiyu Shi, Varuna De Silva and Safak Dogan

Sensors 2024, 24(21), 7084; https://doi.org/10.3390/s24217084 - 3 Nov 2024

Viewed by 922

Abstract

Advances in brain–computer interfaces (BCIs) have enabled direct and functional connections between human brains and computing systems. Recent developments in artificial intelligence have also significantly improved the ability to detect brain activity patterns. In particular, using steady-state visual evoked potentials (SSVEPs) in BCIs has enabled noticeable advances in human activity monitoring and identification. However, the lack of publicly available electroencephalogram (EEG) datasets has limited the development of SSVEP-based BCI systems (SSVEP-BCIs) for human activity monitoring and assisted living. This study aims to provide an open-access multicategory EEG dataset created under the SSVEP-BCI paradigm, with participants performing forward, backward, left, and right movements to simulate directional control commands in a virtual environment developed in Unity. The purpose of these actions is to explore how the brain responds to visual stimuli of control commands. An SSVEP-BCI system is proposed to enable hands-free control of a virtual target in the virtual environment allowing participants to maneuver the virtual target using only their brain activity. This work demonstrates the feasibility of using SSVEP-BCIs in human activity monitoring and assessment. The preliminary experiment results indicate the effectiveness of the developed system with high accuracy, successfully classifying 89.88% of brainwave activity. Full article

(This article belongs to the Special Issue eHealth Platforms and Sensors for Health and Human Activity Monitoring)

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21 pages, 981 KiB

Open AccessArticle

A Crowdsourced AI Framework for Atrial Fibrillation Detection in Apple Watch and Kardia Mobile ECGs

by Ali Bahrami Rad, Miguel Kirsch, Qiao Li, Joel Xue, Reza Sameni, Dave Albert and Gari D. Clifford

Sensors 2024, 24(17), 5708; https://doi.org/10.3390/s24175708 - 2 Sep 2024

Cited by 1 | Viewed by 1246

Abstract

Background: Atrial fibrillation (AFib) detection via mobile ECG devices is promising, but algorithms often struggle to generalize across diverse datasets and platforms, limiting their real-world applicability. Objective: This study aims to develop a robust, generalizable AFib detection approach for mobile ECG devices using crowdsourced algorithms. Methods: We developed a voting algorithm using random forest, integrating six open-source AFib detection algorithms from the PhysioNet Challenge. The algorithm was trained on an AliveCor dataset and tested on two disjoint AliveCor datasets and one Apple Watch dataset. Results: The voting algorithm outperformed the base algorithms across all metrics: the average of sensitivity (0.884), specificity (0.988), PPV (0.917), NPV (0.985), and F1-score (0.943) on all datasets. It also demonstrated the least variability among datasets, signifying its highest robustness and effectiveness in diverse data environments. Moreover, it surpassed Apple’s algorithm on all metrics and showed higher specificity but lower sensitivity than AliveCor’s Kardia algorithm. Conclusions: This study demonstrates the potential of crowdsourced, multi-algorithmic strategies in enhancing AFib detection. Our approach shows robust cross-platform performance, addressing key generalization challenges in AI-enabled cardiac monitoring and underlining the potential for collaborative algorithms in wearable monitoring devices. Full article

(This article belongs to the Special Issue eHealth Platforms and Sensors for Health and Human Activity Monitoring)

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15 pages, 4132 KiB

Open AccessArticle

GaitKeeper: An AI-Enabled Mobile Technology to Standardize and Measure Gait Speed

by Naomi Davey, Gillian Harte, Aidan Boran, Paul Mc Elwaine and Seán P. Kennelly

Sensors 2024, 24(17), 5550; https://doi.org/10.3390/s24175550 - 28 Aug 2024

Viewed by 1354

Abstract

Gait speed is increasingly recognized as an important health indicator. However, gait analysis in clinical settings often encounters inconsistencies due to methodological variability and resource constraints. To address these challenges, GaitKeeper uses artificial intelligence (AI) and augmented reality (AR) to standardize gait speed assessments. In laboratory conditions, GaitKeeper demonstrates close alignment with the Vicon system and, in clinical environments, it strongly correlates with the Gaitrite system. The integration of a cloud-based processing platform and robust data security positions GaitKeeper as an accurate, cost-effective, and user-friendly tool for gait assessment in diverse clinical settings. Full article

(This article belongs to the Special Issue eHealth Platforms and Sensors for Health and Human Activity Monitoring)

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13 pages, 5008 KiB

Open AccessArticle

A Digital Platform for Home-Based Exercise Prescription for Older People with Sarcopenia

by Matteo Bonato, Federica Marmondi, Claudio Mastropaolo, Cecilia Inzaghi, Camilla Cerizza, Laura Galli, Giuseppe Banfi and Paola Cinque

Sensors 2024, 24(15), 4788; https://doi.org/10.3390/s24154788 - 24 Jul 2024

Viewed by 1215

Abstract

Digital therapeutics refers to smartphone applications, software, and wearable devices that provide digital solutions to improve healthcare delivery. We developed a digital platform to support the GYM (Grow Your Muscle) study, an ongoing 48-week randomized, controlled trial on reduction of sarcopenia through a home-based, app-monitored physical exercise intervention. The GYM platform consists of a smartphone application including the exercise program and video tutorials of body-weight exercises, a wearable device to monitor heart rate during training, and a website for downloading training data to remotely monitor the exercise. The aim of this paper is to describe the platform in detail and to discuss the technical issues emerging during the study and those related to usability of the smartphone application through a retrospective survey. The main technical issue concerned the API level 33 upgrade, which did not enable participants using the Android operating systems to use the wearable device. The survey revealed some problems with viewing the video tutorials and with internet or smartphone connection. On the other hand, the smartphone application was reported to be easy to use and helpful to guide home exercising. Despite the issues encountered during the study, this digital-supported physical exercise intervention could provide useful to improve muscle measures of sarcopenia. Full article

(This article belongs to the Special Issue eHealth Platforms and Sensors for Health and Human Activity Monitoring)

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22 pages, 2016 KiB

Open AccessArticle

RADAR-IoT: An Open-Source, Interoperable, and Extensible IoT Gateway Framework for Health Research

by Yatharth Ranjan, Jiangeng Chang, Heet Sankesara, Pauline Conde, Zulqarnain Rashid, Richard J. B. Dobson and Amos Folarin

Sensors 2024, 24(14), 4614; https://doi.org/10.3390/s24144614 - 16 Jul 2024

Viewed by 1488

Abstract

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user’s health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability. Full article

(This article belongs to the Special Issue eHealth Platforms and Sensors for Health and Human Activity Monitoring)

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21 pages, 3597 KiB

Open AccessArticle

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 2207

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 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

Open AccessArticle

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 1291

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 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

Open AccessArticle

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 8 | Viewed by 1842

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 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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Review

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14 pages, 1673 KiB

Open AccessReview

Concurrent Validity Evidence for Pressure-Sensing Walkways Measuring Spatiotemporal Features of Gait: A Systematic Review and Meta-Analysis

by Ozell Sanders, Bin Wang and Kimberly Kontson

Sensors 2024, 24(14), 4537; https://doi.org/10.3390/s24144537 - 13 Jul 2024

Cited by 1 | Viewed by 1110

Abstract

Technologies that capture and analyze movement patterns for diagnostic or therapeutic purposes are a major locus of innovation in the United States. Several studies have evaluated their measurement properties in different conditions with variable findings. To date, the authors are not aware of any systematic review of studies conducted to assess the concurrent validity of pressure-sensing walkway technologies. The results of such an analysis could establish the body of evidence needed to confidently use these systems as reference or gold-standard systems when validating novel tools or measures. A comprehensive search of electronic databases including MEDLINE, Embase, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) was performed. The initial search yielded 7670 papers. After removing duplicates and applying study inclusion/exclusion criteria, 11 papers were included in the systematic review with 10 included in a meta-analysis. There were 25 spatial and temporal gait parameters extracted from the included studies. The results showed there was not a significant bias for nearly all spatiotemporal gait parameters when the walkway system was compared to the reference systems. The findings from this analysis should provide confidence in using the walkway systems as reference systems in future studies to support the evaluation and validation of novel technologies deriving gait parameters. Full article

(This article belongs to the Special Issue eHealth Platforms and Sensors for Health and Human Activity Monitoring)

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17 pages, 692 KiB

Open AccessReview

Introducing a Remote Patient Monitoring Usability Impact Model to Overcome Challenges

by Steffen Baumann, Richard T. Stone and Esraa Abdelall

Sensors 2024, 24(12), 3977; https://doi.org/10.3390/s24123977 - 19 Jun 2024

Viewed by 1694

Abstract

Telehealth and remote patient monitoring (RPM), in particular, have been through a massive surge of adoption since 2020. This initiative has proven potential for the patient and the healthcare provider in areas such as reductions in the cost of care. While home-use medical devices or wearables have been shown to be beneficial, a literature review illustrates challenges with the data generated, driven by limited device usability. This could lead to inaccurate data when an exam is completed without clinical supervision, with the consequence that incorrect data lead to improper treatment. Upon further analysis of the existing literature, the RPM Usability Impact model is introduced. The goal is to guide researchers and device manufacturers to increase the usability of wearable and home-use medical devices in the future. The importance of this model is highlighted when the user-centered design process is integrated, which is needed to develop these types of devices to provide the proper user experience. Full article

(This article belongs to the Special Issue eHealth Platforms and Sensors for Health and Human Activity Monitoring)

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Other

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49 pages, 2156 KiB

Open AccessSystematic Review

Monitoring Daily Sleep, Mood, and Affect Using Digital Technologies and Wearables: A Systematic Review

by Robert Hickman, Teresa C. D’Oliveira, Ashleigh Davies and Sukhi Shergill

Sensors 2024, 24(14), 4701; https://doi.org/10.3390/s24144701 - 19 Jul 2024

Cited by 1 | Viewed by 3069

Abstract

Background: Sleep and affective states are closely intertwined. Nevertheless, previous methods to evaluate sleep-affect associations have been limited by poor ecological validity, with a few studies examining temporal or dynamic interactions in naturalistic settings. Objectives: First, to update and integrate evidence from studies investigating the reciprocal relationship between daily sleep and affective phenomena (mood, affect, and emotions) through ambulatory and prospective monitoring. Second, to evaluate differential patterns based on age, affective disorder diagnosis (bipolar, depression, and anxiety), and shift work patterns on day-to-day sleep-emotion dyads. Third, to summarise the use of wearables, actigraphy, and digital tools in assessing longitudinal sleep-affect associations. Method: A comprehensive PRISMA-compliant systematic review was conducted through the EMBASE, Ovid MEDLINE(R), PsycINFO, and Scopus databases. Results: Of the 3024 records screened, 121 studies were included. Bidirectionality of sleep-affect associations was found (in general) across affective disorders (bipolar, depression, and anxiety), shift workers, and healthy participants representing a range of age groups. However, findings were influenced by the sleep indices and affective dimensions operationalised, sampling resolution, time of day effects, and diagnostic status. Conclusions: Sleep disturbances, especially poorer sleep quality and truncated sleep duration, were consistently found to influence positive and negative affective experiences. Sleep was more often a stronger predictor of subsequent daytime affect than vice versa. The strength and magnitude of sleep-affect associations were more robust for subjective (self-reported) sleep parameters compared to objective (actigraphic) sleep parameters. Full article

(This article belongs to the Special Issue eHealth Platforms and Sensors for Health and Human Activity Monitoring)

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