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Applied Sciences
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2nd Edition: Mobile Health Interventions
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2nd Edition: Mobile Health Interventions

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 847

Special Issue Editor

Dr. Sung-Hee Kim

E-Mail Website
Guest Editor
Industrial ICT Engineering, Dong-eui University, Busan 47340, Republic of Korea
Interests: data visualization; human–computer interaction; health informatics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are organizing a second edition of the Special Issue entitled “Mobile Health Interventions.” Our first edition included diverse research topics from new interfaces or algorithms in mobile health applications to reviews/evaluations of applications. With the significant developments in mobile technology, mobile health is expanding its field in the digital health sector, providing individuals with healthcare support in both clinical and non-clinical populations. The range of applications varies from helping individuals’ lifestyles, such as improving fitness or food consumption, to supporting chronic diseases. The diseases and target users are also diverse. Due to the wide range of support, there are important research areas for designing health intervention systems and implementing them to enhance their impact. Additionally, a large amount of data are being collected and configured for personalization, driven by artificial intelligence. In this Special Issue, we invite you to share your work on mobile health interventions, which will positively impact the future to facilitate and improve care delivery effectively.

You may choose our Joint Special Issue in IJERPH.

Dr. Sung-Hee Kim
Guest Editor

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. Applied Sciences 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 2400 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

  • mobile health
  • telemedicine
  • behavioral change
  • personalization
  • wearable devices
  • chronic disease
  • elderly and diabetes
  • neurodegenerative diseases
  • behavioral change
  • data-driven decisions
  • physical activity
  • self-management
  • machine learning
  • digital health
  • digital therapeutics
  • digital literacy
  • health literacy

Published Papers (1 paper)

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Research

18 pages, 8468 KiB  
Article
Vision-Based Hand Rotation Recognition Technique with Ground-Truth Dataset
by Hui-Jun Kim, Jung-Soon Kim and Sung-Hee Kim
Appl. Sci. 2024, 14(1), 422; https://0-doi-org.brum.beds.ac.uk/10.3390/app14010422 - 03 Jan 2024
Viewed by 675
Abstract
The existing question-and-answer screening test has a limitation in that test accuracy varies due to a high learning effect and based on the inspector’s competency, which can have consequences for rapid-onset cognitive-related diseases. To solve this problem, a behavioral-data-based screening test is necessary, [...] Read more.
The existing question-and-answer screening test has a limitation in that test accuracy varies due to a high learning effect and based on the inspector’s competency, which can have consequences for rapid-onset cognitive-related diseases. To solve this problem, a behavioral-data-based screening test is necessary, and there are various types of tasks that can be adopted from previous studies, or new ones can be explored. In this study, we came up with a continuous hand movement, developed a behavioral measurement technology, and conducted validity verification. As a result of analyzing factors that hinder measurement accuracy, this measurement technology used a web camera to measure behavioral data of hand movements in order to lower psychological barriers and to pose no physical risk to subjects. The measured hand motion is a hand rotation that repeatedly performs an action in which the bottom of the hand is seen in front. The number of rotations, rotation angle, and rotation time generated by the hand rotation are derived as measurements; and for calculation, we performed hand recognition (MediaPipe), joint data detection, motion recognition, and motion analysis. To establish the validity of the derived measurements, we conducted a verification experiment by constructing our own ground-truth dataset. The dataset was developed using a robot arm with two-axis degrees of freedom and that quantitatively controls the number, time, and angle of rotations. The dataset includes 540 data points comprising 30 right- and left-handed tasks performed three times each at distances of 57, 77, and 97 cm from the camera. Thus, the accuracy of the number of rotations is 99.21%, the accuracy of the rotation angle is 91.90%, and the accuracy of the rotation time is 68.53%, making the overall rotation measurements more than 90% accurate for input data at 30 FPS for measuring the rotation time. This study is significant in that it not only contributes to the development of technology that can measure new behavioral data in health care but also shares image data and label values that perform quantitative hand movements in the image processing field. Full article
(This article belongs to the Special Issue 2nd Edition: Mobile Health Interventions)
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