Development of Heart Rate Variability Analysis-based Heat Stroke detection model with Multivariate Statistical Process Control

• OTA Koshi

  Nagoya University

• FUJIWARA Koichi

  Nagoya University

• INATSU Nao

  Kumamoto University

• YAMAKAWA Toshitaka

  Kumamoto University

• KUBO Takatomo

  Nara Institute of Science and Technology

Bibliographic Information

Other Title

• 心拍変動解析と多変量統計的プロセス管理による熱中症検知モデルの開発

Abstract

Objective: This study proposes a heat stroke detecting model using heart rate variability (HRV) and an anomaly detecting technique. If heat stroke can be detected at an early stage, its patient can rest before it is too late. Methods: Since it is reported that heat stress influences HRV, we developed the heat stroke detecting model that detects abnormality in HRV due to heat stroke by using multivariate statistical process control (MSPC). Results: We measured the HRV data from 30 employees who worked in multiple steel works, whose total data length was 1,042 hours. The result of applying the developed heat stroke detection model showed that the sensitivity of 40.0% and the false positive rate of 1.9 times per hour. Conclusion: This study constructed an early-stage heat stroke detection model by using HRV analysis and MSPC. Significance: This study illustrated the possibility of detecting heat stroke by using HRV analysis.

Journal

• Proceedings of the Annual Conference of JSAI

  Proceedings of the Annual Conference of JSAI JSAI2020 (0), 1C5GS1301-1C5GS1301, 2020

  The Japanese Society for Artificial Intelligence

Keywords

• Heat stroke detection
• Heart rate variability
• Multivariate statistical process control
• Anomaly detection

Details

• CRID: 1390566775142552192
• NII Article ID: 130007856614
• DOI: 10.11517/pjsai.jsai2020.0_1c5gs1301
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed