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Advances in Mobile Sensing for Smart Healthcare

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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: 30 July 2024 | Viewed by 3849

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Dear Colleagues,

The intersection of mobile sensing technologies and healthcare promises a revolution in the way we monitor, diagnose, and manage our health. "Advances in Mobile Sensing for Smart Healthcare" is a Special Issue dedicated to exploring the cutting-edge advancements at this intersection. Mobile devices, from smartphones to wearables, have become indispensable tools in healthcare, enabling real-time data collection, analysis, and personalized interventions. This Special Issue will provide a platform to showcase the latest research, innovations, and breakthroughs that harness mobile sensing for smart healthcare applications.

Potential topics include but for are not limited to:

Wearable Health Devices;
Remote patient monitoring;
Disease management;
Preventive healthcare;
Telemedicine and Telehealth;
mHealth applications;
Sensor technologies, data analytics, and the ethical implications of mobile sensing in healthcare;
Personalized Health and Wellness;
Clinical applications;
Healthcare access and equity;
Regulatory and Industry Developments.

With an emphasis on the rapid dissemination of cutting-edge developments, this Special Issue serves as a bridge between sensor technology and healthcare applications, underscoring the journal's commitment to advancements in sensor design, technology, applications, and proof of concept.

Dr. Maria Valero
Guest Editor

Manuscript Submission Information

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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

wearable sensors
remote patient monitoring
disease management
preventive healthcare
telemedicine and telehealth
mHealth applications

Published Papers (2 papers)

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Research

13 pages, 778 KiB

Open AccessArticle

Smartphone-Based Cognitive Telerehabilitation: A Usability and Feasibility Study Focusing on Mild Cognitive Impairment

by Caterina Formica, Mirjam Bonanno, Chiara Sorbera, Angelo Quartarone, Fabio Mauro Giambò, Angela Marra and Rocco Salvatore Calabrò

Sensors 2024, 24(2), 525; https://doi.org/10.3390/s24020525 - 15 Jan 2024

Viewed by 899

Abstract

The implementation of cognitive health apps in patients with mild cognitive impairment (MCI) is challenging because of their cognitive, age, and other clinical characteristics. In this project, we aimed to evaluate the usability and feasibility of the Rehastart app tested in MCI patients. Eighteen subjects affected by MCI due to neurodegenerative disorders (including Parkinson’s disease, multiple sclerosis, and amnestic/multidomain MCI) and eighteen healthcare professionals were recruited to this study. Patients were registered on the app by clinicians and they were assigned a protocol of specific cognitive exercises. The recruitment was conducted in the period between March and June 2023. The trial testing of the app consisted of three sessions per week for three weeks, with each session lasting about 30 min. After three weeks, the participants as well as medical personnel were invited to rate the usability and feasibility of the Rehastart mobile application. The instruments employed to evaluate the usability and feasibility of the app were the System Usability Scale (SUS), The Intrinsic Motivation Inventory (IMI) and the Client Satisfaction Questionnaire (CSQ). We did not find statistically significant differences on the SUS (p = 0.07) between healthcare professionals and patients. In addition, we found promising results on subscales of the Intrinsic Motivation Inventory, suggesting high levels of interest and enjoyment when using the Rehastart app. Our study demonstrated that smartphone-based telerehabilitation could be a suitable tool for people with MCI due to neurodegenerative disorders, since the Rehastart app was easy to use and motivating for both patients and healthy people. Full article

(This article belongs to the Special Issue Advances in Mobile Sensing for Smart Healthcare)

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23 pages, 29967 KiB

Open AccessArticle

Intelligent Millimeter-Wave System for Human Activity Monitoring for Telemedicine

by Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson and Vamsy P. Chodavarapu

Sensors 2024, 24(1), 268; https://doi.org/10.3390/s24010268 - 2 Jan 2024

Cited by 1 | Viewed by 2479

Abstract

Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can provide activity data reports, tracking maps, and fall alerts. Using radar helps to safeguard patients’ privacy by abstaining from recording camera images. We evaluated our system for real-time operation and achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Our system would facilitate the ability to detect falls and monitor physical activity in home and institutional settings to improve telemedicine by providing objective data for more timely and targeted interventions. This work demonstrates the potential of artificial intelligence algorithms and mmwave sensors for HAR. Full article

(This article belongs to the Special Issue Advances in Mobile Sensing for Smart Healthcare)

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