Special Issue "Multimodal Data Fusion and Machine-Learning for Healthcare"
Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 30988
Interests: mobile and pervasive systems; e-health; affective computing; multimedia analysis; interaction design
Special Issues, Collections and Topics in MDPI journals
The Internet of things enables multi-modal data fusion and analysis to analyze person-centric data, and ultimately personalizes healthcare services delivery. Wireless, wearable, and ambient technologies support a more ongoing monitoring of people’s wellbeing status and of the contributing factors, such as environmental quality. The interconnecting cloud services bring together and integrate these data to streamline health assessments, while ensuring privacy and information security computationally. Machine learning for the intelligent analysis of health data is integral to long-term health tracking, forecasting, and early warning, as well as for the prevention and management of chronic diseases. Health practitioners should investigate the impact of technologies in order to achieve better planning, design, delivery, and evaluation of future healthcare services.
The goal of the Special Issue is to publish recent results and applications of integrated sensors, and machine learning enabled healthcare from academia and industry. We invite original and unpublished manuscripts that are related, but limited to, these topics:
- Integration of sensors-enabled monitoring of physical and psychological activities, including all of the considerations regarding data reliability, privacy, security, safety, comfort, and ease-of-use.
- Virtual reality, augmented reality, mixed reality, data visualization, and gamification for effective display and for use of integrated health sensors data.
- Multi-modal data fusion and analysis techniques for measuring health and wellbeing conditions, as well as environmental quality, such as human’s vital signs, physical exertions, sleep quality, mental stress, mood, and brain activity.
- Machine learning algorithms that support efficient and continuous learning from health data, including new datasets, techniques, and systems that can enable the rapid development of intelligent health analyses.
- Methods, concepts, and principles for evaluating the impact of sensors on data-driven and evidence-based health monitoring, diagnosis, and interventions.
- New and emerging integrated sensors technologies and systems that are more robust, sustainable, and secure for real-life deployments and applications.
Prof. Dian Tjondronegoro
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.
- Integrated medical sensors
- Multi-modal data fusion in healthcare
- Machine learning in healthcare
- Visualization and gamification of health big data