Chapter
Jun 29, 2016
Driving Physiological Indicator Recognition Based on Smart Bracelet Data
Authors: Li Zhao [email protected], Chaofei Li [email protected], Weiwei Guo [email protected], Jiyuan Tan [email protected], Li Li [email protected], and Kaili Yin [email protected]Author Affiliations
Publication: CICTP 2016
Abstract
Various research on heart rate variability (HRV) have focused on the characteristics of the peaks and the valleys of a heart rate graph and most have been analyzed using medical devices. This paper developed a driving physiological indicator extraction method. The method is based on a pulse signal detection with a feature point identification model and an abnormal signal detection combined with a revamp model. According to physiological theory and the relationship between pulse and ECG readings, the pulse feature points can be detected. This method is easier and more accurate than the existing wavelet pulse signal detection method, which can be used in the wearable devices, such as the smart band and the smart watch. The method was also validated efficiently and accurately in the experiments.
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© 2016 American Society of Civil Engineers.
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Published online: Jun 29, 2016
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Research Institute of Highway Ministry of Transport, Beijing, China. E-mail: [email protected]
College of Electrical and Control Engineering, North China Univ. of Technology, Beijing, China. E-mail: [email protected]
College of Electrical and Control Engineering, North China Univ. of Technology, Beijing, China. E-mail: [email protected]
College of Electrical and Control Engineering, North China Univ. of Technology, Beijing, China. E-mail: [email protected]
Dept. of Automation, Tsinghua Univ., Beijing, China. E-mail: [email protected]
College of Electrical and Control Engineering, North China Univ. of Technology, Beijing, China. E-mail: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.