Effect of Micrometeorology on Human Thermoreguatory Response and its Modeling

2022 Fiscal Year Final Research Report

• Project Area: Micro-meteorology control: Integrated technology of harmonic prediction and active monitoring of micro-meteorology for future autonomous society
• Project/Area Number: 20H05753
• Research Category: Grant-in-Aid for Transformative Research Areas (B)
• Allocation Type: Single-year Grants
• Review Section: Transformative Research Areas, Section (II)
• Research Institution: Nagoya Institute of Technology
• Principal Investigator: Hirata Akimasa 名古屋工業大学, 工学(系)研究科(研究院), 教授 (00335374)
• Co-Investigator(Kenkyū-buntansha): 小寺 紗千子 名古屋工業大学, 工学(系)研究科(研究院), 准教授 (40874219) ; Rashed Essam 兵庫県立大学, 情報科学研究科, 教授 (60837590) ; ゴメスタメス ホセデビツト 名古屋工業大学, 工学(系)研究科(研究院), 准教授 (60772902)
• Project Period (FY): 2020-10-02 – 2023-03-31
• Keywords: 熱中症リスク / 温熱生理応答 / 複合物理計算 / 深部温度 / 機械学習

Outline of Final Research Achievements

Body core temperature and perspiration are effective metrics to relate the risk of heat stroke. We have developed a computational method to calculate body core temperature and perspiration rate using an anatomical three-dimensional human body model combined with thermoregulatory response. In this study, the thermophysiological response of the human body under microclimatic conditions is quantitatively evaluated for estimating the reduction of heat stress toward future services of risk management. In addition, based on the results obtained, a fast numerical method for calculating the responses has been proposed. Specifically, a low-dimensional model has been developed that can be evaluated in milliseconds using a conventional personal computer, whereas an equivalent evaluation using a supercomputer would take three hours. Using a large scale data of computed results of themoregulatory response under microclimatic conditions, fast estimation of heat strain has been achieved.

Free Research Field

安全工学

Academic Significance and Societal Importance of the Research Achievements

熱中症リスクは年齢，活動など個人によって大きく異なる.また，現実の都市環境では，環境は時々刻々と変化しており，それを考慮することができれば，屋外での熱中症対策に資するデータができる.本研究では，微気象予測情報を用いて計算した温熱応答を機械学習することにより，現実的な環境下での熱ストレスを算出した.従来技術に比べて，簡易に評価でき，周辺環境が変化しやすい場面での利用が期待される.