A webinar organized by IEEE Sapporo Section YP will be held on Feb 5 2021
Date: 10:25-11:55 am (JST) Feb 5, 2021
Location: Online (Zoom)
Emerging Networks and Systems Laboratory (ENeS), Muroran Institute of Technology
IEEE Sapporo Section Young Professionals (YP)
IEEE Muroran Institute of Technology Student Branch
Please click the link right before the webinar on Feb 5.
Fostering Innovation in Digital Health for the Next Generation of Healthcare — Joint force of the Informatics, Biology, and Engineering
Speaker: Ming Huang, Nara Institute of Science and Technology
Digital health refers to the use of IoT health devices to track personal health status and give feedback for self-health management. Thanks to the ready-to-use modality, the deterioration of health may be detected by continuous monitoring and early intervention can be taken to prevent further deterioration. Digital health is gradually recognized as a necessary measure to promote health. In considering the characteristics of relatively low signal qualities and mixtures of exogenous sources, a conventional framework for signal processing and information extraction is not suitable anymore. With this regard, to make full use of the wearable/IoT health system, a new framework adapted to the characteristics of the IoT health devices is indispensable. The idea are materialized by introducing a novel heart monitoring system. The heart is a crucial organ to human, whose underlying status can be revealed during the sleep stage. To monitor the physiological status of the heart during sleep, we have developed a personal heart monitoring system based on non-contact sensing techniques. The fundamental problem for this system is that not only the physiological information of the heart but also the physical information such as body movement will be recorded. To extract the physiological information of the heart, we proposed a new self-adaptive processing framework based on deep learning techniques combining the convolutional and recurrent neural networks, which can process the data stream in a quasi-real-time manner. By using this processing framework, we can extract the sleep position of the user in addition to the electrocardiogram of the heart. By this unconstrained monitoring system, it is plausible to monitor the heart status as well as the sleep quality closely which will contribute to the heart health promotion.
Dr. Ming Huang received his Ph.D degree from the University of Aizu in 2012. He is now an Assistant Professor with Nara Institute of Science and Technology and he is also a visiting scholar of Biomedical Engineering Department, University of California Davis. His research interests include biomedical signal processing, digital health based on bigdata analytics and machine learning and chemo/bioinformatics in biomedical engineering. His works have substantially contributed to the fostering of next generation personal healthcare, including the theoretical design and experimental validations of the noninvasive deep body thermometry (DHFM), and cuff-less blood pressure measurements. He is now dedicating to understand the influences of acquired factors on the heart health by incorporating state-of-the-art sensing technologies and information science approaches such as statistical modeling based on deep learning. He has published over 30 peer-reviewed Journal papers till now, and is serving as an active reviewer for a number of prestigious Journals, such as IEEE JBHI, IEEE TBME, MDPI Sensors. He was the recipient of IEEE EMBC Japan Chapter Young Researcher Award 2011 for his original work on inverse modeling of the organ temperature estimation based on skin temperature.
He Li (Sapporo YP Chair)
Department of Sciences and Informatics
Muroran Institute of Technology, Japan
Email: [email protected]