Dangerous Driving Prediction Model based on Long Short-term Memory Network with Dynamic Weighted Moving Average of Heart-Rate Variability

Cheng Yu Tsai, He In Cheong, Robert Houghton, Arnab Majumdar, Wen Te Liu, Kang Yun Lee, Cheng Jung Wu, Yi Shin Liu

研究成果: 書貢獻/報告類型會議貢獻

摘要

Dangerous driving behaviours contribute significantly to road accidents. Researchers have developed numerous models for predicting dangerous behaviours. However, these models have remained at the development stage. This paper proposes using a dynamic weight moving average (DWMA) method for processing heart rate variability (HRV) indices and establishing prediction models using long short-term memory (LSTM) networks. The changes in HRV indices between baseline and pre-event stages were also investigated. Thirty-three Taiwanese commercial drivers, which were 19 urban drives and 14 highway drivers, were recruited (between September 2019 and June 2020). Their driving behaviours and physiological signals during tasks were obtained by navigation software and an HRV watch. The DWMA and exponential moving average were applied to process the physiological signals. The derived data set was split into training and testing sets (ratio: 80% to 20%). To establish the models, the LSTM networks were trained using the training set and K-fold cross-validation (K = 10). Prediction performance was evaluated by sensitivity, specificity, and accuracy. For the urban drivers, the significantly raised values in the normalized low-frequency spectrum and the sympathovagal balance index were found. The significantly elevated values in the standard deviation of NN intervals were observed. For the highway drivers, the significantly increased heart rate and root mean square of successive RR interval differences can be observed. Besides, the LSTM models based on DWMA demonstrated the highest accuracy in urban and highway groups (Urban driving group: 80.31%, 95% confidence interval: 84.65-91.71%; Highway driving group: 80.70%, 95% confidence interval: 72.25-87.49%). The authors recommend using these models to prevent dangerous driving behaviours.

原文英語
主出版物標題7th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2020
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9780738105048
DOIs
出版狀態已發佈 - 12月 18 2020
事件7th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2020 - Kuala Lumpur, 马来西亚
持續時間: 12月 18 202012月 20 2020

出版系列

名字7th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2020

會議

會議7th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2020
國家/地區马来西亚
城市Kuala Lumpur
期間12/18/2012/20/20

ASJC Scopus subject areas

  • 電腦網路與通信
  • 電腦科學應用
  • 硬體和架構
  • 能源工程與電力技術
  • 工程(雜項)
  • 電氣與電子工程
  • 工業與製造工程

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