Multimodal Data Fusion and Machine-Learning for Promotion of Health/Well-Being
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 2783
Special Issue Editor
Interests: mobile and pervasive systems; E-Health; affective computing; multimedia analysis; interaction design
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of sensors and mobile and cloud computing enables multimodal data fusion and machine learning to analyze person-centric data and ultimately personalizes health and wellbeing service delivery. Wireless, wearable, and ambient sensing technologies support the continuous monitoring of people’s wellbeing status and of the contributing factors, such as environmental quality. 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, which supports wellness promotion as well as prevention and management of chronic diseases. Health and wellness practitioners should investigate the impact of technologies to achieve better planning, design, delivery, and evaluation of future healthcare and wellness promotion services.
The goal of this Special Issue is to publish recent results and applications of integrated sensors, and machine-learning-enabled healthcare and wellbeing services from academia and industry. We invite original and unpublished manuscripts that are related, but limited to, these topics:
- Integration of sensor-enabled monitoring of physical and psychological activities, including all 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 sensor data;
- Multimodal data fusion and analysis techniques for measuring health and wellbeing conditions, as well as environmental quality, such as human 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 sensor technologies and systems that are more robust, sustainable, and secure for real-life deployments and applications.
Prof. Dr. Dian Tjondronegoro
Guest Editor
Manuscript Submission Information
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Keywords
- multimodal health sensors
- multimodal data fusion in healthcare
- machine learning in healthcare
- visualization and gamification of health big data
