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Sensors in mHealth Applications
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Sensors in mHealth Applications

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 9267

Special Issue Editors

Prof. Dr. Johannes Schobel

E-Mail Website
Guest Editor
DigiHealth Institute, Neu-Ulm University of Applied Sciences, 89231 Neu-Ulm, Germany
Interests: healthcare services; digital health; mobile data collection; medical information systems; medical informatics; API design; web APIs; cloud services management; process management; information systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rüdiger Pryss

E-Mail Website
Guest Editor
Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97070 Würzburg, Germany
Interests: medical informatics; mobile crowdsensing; mHealth; health services research; expert systems; medical data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Current research in the field of mHealth applications heavily relies on (intelligent) sensors that are used to capture additional information from participants. For example, smart mobile applications (“apps”) connect with external devices (i.e., blood sugar measurement devices) to collect measurements, communicate with so-called “wearables”, or detect emotions or moods via video camera. Such intelligent sensors enable a plethora of novel application scenarios that may be particularly interesting in the context of medical applications or applications to educate and train participants.

This Special Issue aims to collect top-quality research focusing on novel approaches or emerging trends in the field of sensors in mHealth applications.

The key topics of interest include, but are not limited to, the following:

  • mHealth applications;
  • eHealth applications;
  • mobile sensing;
  • mobile data collection using sensors;
  • IoT in healthcare;
  • wearables;
  • activity recognition;
  • robotics (in medicine or care);
  • emotion/mood detection using camera systems;
  • ambient assisted living;
  • AR and VR in healthcare applications (i.e., education).

Prof. Dr. Johannes Schobel
Prof. Dr. Rüdiger Pryss
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

  • mHealth applications
  • eHealth applications
  • mobile sensing
  • mobile data collection using sensors
  • IoT in healthcare
  • wearables
  • activity recognition
  • robotics (in medicine or care)
  • emotion/mood detection using camera systems
  • ambient assisted living
  • AR and VR in healthcare applications (i.e., education)

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Published Papers (5 papers)

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Research

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33 pages, 6347 KiB  
Article
From Steps to Context: Optimizing Digital Phenotyping for Physical Activity Monitoring in Older Adults by Integrating Wearable Data and Ecological Momentary Assessment
by Kim Daniels, Kirsten Quadflieg, Jolien Robijns, Jochen De Vry, Hans Van Alphen, Robbe Van Beers, Britt Sourbron, Anaïs Vanbuel, Siebe Meekers, Marlies Mattheeussen, Annemie Spooren, Dominique Hansen and Bruno Bonnechère
Sensors 2025, 25(3), 858; https://doi.org/10.3390/s25030858 - 31 Jan 2025
Viewed by 850
Abstract
Physical activity (PA) is essential for healthy aging, but its accurate assessment in older adults remains challenging due to the limitations and biases of traditional clinical assessment. Mobile technologies and wearable sensors offer a more ecological, less biased alternative for evaluating PA in [...] Read more.
Physical activity (PA) is essential for healthy aging, but its accurate assessment in older adults remains challenging due to the limitations and biases of traditional clinical assessment. Mobile technologies and wearable sensors offer a more ecological, less biased alternative for evaluating PA in this population. This study aimed to optimize digital phenotyping strategies for assessing PA patterns in older adults, by integrating ecological momentary assessment (EMA) and continuous wearable sensor data collection. Over two weeks, 108 community-dwelling older adults provided real-time EMA responses while their PA was continuously monitored using Garmin Vivo 5 sensors. The combined approach proved feasible, with 67.2% adherence to EMA prompts, consistent across time points (morning: 68.1%; evening: 65.4%). PA predominantly occurred at low (51.4%) and moderate (46.2%) intensities, with midday activity peaks. Motivation and self-efficacy were significantly associated with low-intensity PA (R = 0.20 and 0.14 respectively), particularly in the morning. However, discrepancies between objective step counts and self-reported PA measures, which showed no correlation (R = −0.026, p = 0.65), highlight the complementary value of subjective and objective data sources. These findings support integrating EMA, wearable sensors, and temporal frameworks to enhance PA assessment, offering precise insights for personalized, time-sensitive interventions to promote PA. Full article
(This article belongs to the Special Issue Sensors in mHealth Applications)
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34 pages, 6030 KiB  
Article
Optimized Adaboost Support Vector Machine-Based Encryption for Securing IoT-Cloud Healthcare Data
by Yoosef B. Abushark, Shabbir Hassan and Asif Irshad Khan
Sensors 2025, 25(3), 731; https://doi.org/10.3390/s25030731 - 25 Jan 2025
Cited by 1 | Viewed by 771
Abstract
The Internet of Things (IoT) connects various medical devices that enable remote monitoring, which can improve patient outcomes and help healthcare providers deliver precise diagnoses and better service to patients. However, IoT-based healthcare management systems face significant challenges in data security, such as [...] Read more.
The Internet of Things (IoT) connects various medical devices that enable remote monitoring, which can improve patient outcomes and help healthcare providers deliver precise diagnoses and better service to patients. However, IoT-based healthcare management systems face significant challenges in data security, such as maintaining a triad of confidentiality, integrity, and availability (CIA) and securing data transmission. This paper proposes a novel AdaBoost support vector machine (ASVM) based on the grey wolf optimization and international data encryption algorithm (ASVM-based GWO-IDEA) to secure medical data in an IoT-enabled healthcare system. The primary objective of this work was to prevent possible cyberattacks, unauthorized access, and tampering with the security of such healthcare systems. The proposed scheme encodes the healthcare data before transmitting them, protecting them from unauthorized access and other network vulnerabilities. The scheme was implemented in Python, and its efficiency was evaluated using a Kaggle-based public healthcare dataset. The performance of the model/scheme was evaluated with existing strategies in the context of effective security parameters, such as the confidentiality rate and throughput. When using the suggested methodology, the data transmission process was improved and achieved a high throughput of 97.86%, an improved resource utilization degree of 98.45%, and a high efficiency of 93.45% during data transmission. Full article
(This article belongs to the Special Issue Sensors in mHealth Applications)
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21 pages, 1339 KiB  
Article
Stacking Ensemble Deep Learning for Real-Time Intrusion Detection in IoMT Environments
by Easa Alalwany, Bader Alsharif, Yazeed Alotaibi, Abdullah Alfahaid, Imad Mahgoub and Mohammad Ilyas
Sensors 2025, 25(3), 624; https://doi.org/10.3390/s25030624 - 22 Jan 2025
Cited by 3 | Viewed by 1500
Abstract
The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling advanced patient care through interconnected medical devices and systems. However, its critical role and sensitive data make it a prime target for cyber threats, requiring the implementation of effective security solutions. This [...] Read more.
The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling advanced patient care through interconnected medical devices and systems. However, its critical role and sensitive data make it a prime target for cyber threats, requiring the implementation of effective security solutions. This paper presents a novel intrusion detection system (IDS) specifically designed for IoMT networks. The proposed IDS leverages machine learning (ML) and deep learning (DL) techniques, employing a stacking ensemble method to enhance detection accuracy by integrating the strengths of multiple classifiers. To ensure real-time performance, the IDS is implemented within a Kappa Architecture framework, enabling continuous processing of IoMT data streams. The system effectively detects and classifies a wide range of cyberattacks, including ARP spoofing, DoS, Smurf, and Port Scan, achieving an outstanding detection accuracy of 0.991 in binary classification and 0.993 in multi-class classification. This research highlights the potential of combining advanced ML and DL methods with ensemble learning to address the unique cybersecurity challenges of IoMT systems, providing a reliable and scalable solution for safeguarding healthcare services. Full article
(This article belongs to the Special Issue Sensors in mHealth Applications)
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9 pages, 638 KiB  
Article
The Validity and Reliability of the My Jump Lab App for the Measurement of Vertical Jump Performance Using Artificial Intelligence
by Carlos Balsalobre-Fernández and Daniel Varela-Olalla
Sensors 2024, 24(24), 7897; https://doi.org/10.3390/s24247897 - 10 Dec 2024
Cited by 1 | Viewed by 2522
Abstract
The countermovement jump (CMJ) is a widely used test to assess lower body neuromuscular performance. This study aims to analyze the validity and reliability of an iOS application using artificial intelligence to measure CMJ height, force, velocity, and power in unloaded and loaded [...] Read more.
The countermovement jump (CMJ) is a widely used test to assess lower body neuromuscular performance. This study aims to analyze the validity and reliability of an iOS application using artificial intelligence to measure CMJ height, force, velocity, and power in unloaded and loaded conditions. Twelve physically active participants performed 12 CMJs with external loads ranging from 0% to 70% of their body mass while being simultaneously monitored with a pair of force platforms and the My Jump Lab application. The scores for jump height, mean propulsive force, velocity, and power between devices were compared for validity and reliability purposes. The force platform and the application showed a high association (r > 0.91, p < 0.05) for measuring CMJ height, force, velocity, and power. Small and no statistically significant differences (p < 0.05) were observed in most loading conditions. Both instruments showed high reliability (Cronbach’s α > 0.93, Coefficient of variation < 6%) for measuring the different trials performed by each participant. The My Jump Lab application was shown to be valid and reliable for measuring CMJ height, force, velocity, and power in both loaded and unloaded jumps, eliminating the problems associated with the cost and portability of force plates for daily practice. Full article
(This article belongs to the Special Issue Sensors in mHealth Applications)
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Other

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19 pages, 1831 KiB  
Systematic Review
Technological Features of Immersive Virtual Reality Systems for Upper Limb Stroke Rehabilitation: A Systematic Review
by Chala Diriba Kenea, Teklu Gemechu Abessa, Dheeraj Lamba and Bruno Bonnechère
Sensors 2024, 24(11), 3546; https://doi.org/10.3390/s24113546 - 31 May 2024
Cited by 3 | Viewed by 2558
Abstract
Stroke is the second most common cause of death worldwide, and it greatly impacts the quality of life for survivors by causing impairments in their upper limbs. Due to the difficulties in accessing rehabilitation services, immersive virtual reality (IVR) is an interesting approach [...] Read more.
Stroke is the second most common cause of death worldwide, and it greatly impacts the quality of life for survivors by causing impairments in their upper limbs. Due to the difficulties in accessing rehabilitation services, immersive virtual reality (IVR) is an interesting approach to improve the availability of rehabilitation services. This systematic review evaluates the technological characteristics of IVR systems used in the rehabilitation of upper limb stroke patients. Twenty-five publications were included. Various technical aspects such as game engines, programming languages, headsets, platforms, game genres, and technical evaluation were extracted from these papers. Unity 3D and C# are the primary tools for creating IVR apps, while the Oculus Quest (Meta Platforms Technologies, Menlo Park, CA, USA) is the most often used headset. The majority of systems are created specifically for rehabilitation purposes rather than being readily available for purchase (i.e., commercial games). The analysis also highlights key areas for future research, such as game assessment, the combination of hardware and software, and the potential integration incorporation of biofeedback sensors. The study highlights the significance of technological progress in improving the effectiveness and user-friendliness of IVR. It calls for additional research to fully exploit IVR’s potential in enhancing stroke rehabilitation results. Full article
(This article belongs to the Special Issue Sensors in mHealth Applications)
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