| Project/Area Number |
16K16363
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | The University of Electro-Communications |
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Principal Investigator |
SUN Guanghao 電気通信大学, 大学院情報理工学研究科, 助教 (80756677)
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| Research Collaborator |
MATSUI Takemi
KIRIMOTO Tetsuo
ISHIBASHI Koichiro
YAO Yu
Sumiyakhand Dagdanpurev
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | バイタルサイン計測 / 感染症サーベイランス / 社会システム / 非接触生体計測 / 可視化 / 生体データ / 感染症検出システム / デング熱 / バイタルサイン / 新興・再興感染症 / サーベイランス / 生体情報 / 情報処理 / 安心の社会技術 / ニューラルネットワーク |
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| Outline of Final Research Achievements |
Fever-based screening method has been adopted to identify potentially infected individuals using thermography. However, some studies indicate that fever-based screening at early-stages of infectious diseases is limited due to many factors that can affect thermographic measurements, such as antifebrile intake. We propose an infection screening system that can rapidly and accurately perform medical inspections. Heart and respiration rates are determined using a medical radar by noncontact way, and body temperature is monitored by thermography. By using these three parameters, the detection accuracy of the system improved comparing to conventional screening method. In this study, to further improve screening performance, one of the most promising approaches is to connect multiple infection screening systems, which enables information sharing between different systems. This will allow us to apply big data analysis techniques, which can be used to predict outbreaks of infectious diseases.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では，新興・再興感染症の早期診断および発生地域における流行の最小化のために，大規模な生体情報データ（心拍数・呼吸数・体温）に基づく感染症サーベイランスシステムを開発し，機能させることを目的とした．本研究の学術的及び社会的意義として，開発する感染症サーベイランスシステムは客観的な生体情報データを人工知能の手法により解析し，感染症の流行状況を可視化でき，早期に探知することが可能になり，感染症の被害拡大を防ぐことが期待される．
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