| Project/Area Number |
19F19081
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 外国 |
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| Review Section |
Basic Section 62010:Life, health and medical informatics-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
山本 義春 東京大学, 大学院教育学研究科(教育学部), 教授 (60251427)
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| Co-Investigator(Kenkyū-buntansha) |
QIAN KUN 東京大学, 教育学研究科(研究院), 外国人特別研究員
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| Project Period (FY) |
2019-10-11 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 2021: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2020: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2019: ¥600,000 (Direct Cost: ¥600,000)
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| Keywords | Signal Processing / Internet of Things / Artificial Intelligence |
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| Outline of Research at the Start |
This research aims to leverage the power of AI for analyzing and monitoring the daily behavior of the patients suffering from psychiatric diseases via the biomedical intensive longitudinal data. We will investigate the state-of-the-art techniques of machine learning, deep learning, and signal processing for their capacity on screening the patients from the healthy control. In addition, we will explore the feasibility to use the paradigm of AI to implement an automatic monitoring and evaluation system for subject’s health status by IoT sensor data.
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| Outline of Annual Research Achievements |
In the FY2021, we have experienced a productive research achievement. We have successfully published an overview paper on the topic of using artificial intelligence and internet of things (AIoT) technologies to better the ageing society. This paper has been published by the prestigious top journal, IEEE Signal Processing Magazine, and can itself be a good guidance for a broad community. In addition, this overview paper summarized the main ideas and/or techniques that learnt from our research project. We have used IoT sensor data to monitor the physical and mental status of elderly people (who are living alone), which has been found efficient and published in the IEEE Internet of Things Journal. Moreover, we have published our paper on using the state-of-the-art computer audition (CA) techniques to measure the health status of patients suffering from the COVID-19. The results were published by the IEEE Internet of Things Journal. A multi-modal learning system for monitoring the driver’s drowsiness level was accepted and published online by the top journal IEEE Transactions on Intelligent Transportation Systems. A systematic review on CA methods for snore site localization was published by the IEEE Journal of Biomedical and Health Informatics. Moreover, we have some collaborative works with other colleagues.
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| Research Progress Status |
令和3年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和3年度が最終年度であるため、記入しない。
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