Causal analysis between vehicle operating data and physiological responses

被引:1
作者
Bando S. [1 ]
Nozawa A. [1 ]
Matsuya Y. [1 ]
机构
[1] Aoyama Gakuin University, 5-10-1 Fuchinobe, Chuo-ku, Sagamihara, Kanagawa
关键词
Heart rate variability; Multidimensional directed coherence; Respiration; Vehicle operating data;
D O I
10.1007/s10015-015-0229-6
中图分类号
学科分类号
摘要
The objective of this paper is to clarify the interrelation between vehicle operating data and physiological responses to different psychological states. Multidimensional directed coherence (MDC) analysis was applied to the human–machine system, as observed by vehicle operating data and physiological indices to reveal the mechanism of the interrelation. The MDC analysis is one way to visualize the information flow between an arbitrary number of time series signals in the frequency domain. As a result, it was found that the entrainment of physiological indices on vehicle operating data is different depending on the cognitive resources devoted to driving motion. In particular, it was clarified that driving motion is easily influenced by physiological indices when the cognitive resources devoted to driving motion are few either absolutely or relatively. © 2015, ISAROB.
引用
收藏
页码:299 / 304
页数:5
相关论文
共 10 条
[1]  
Klauer S.G., Dingus T.A., Neale V., The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data. Technical Report No, DOT HS 810 594, (2006)
[2]  
Borghini G., Astolfi L., Vecchiato G., Et al., Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness, Neurosci Biobehav Rev, 44, pp. 58-75, (2014)
[3]  
Rodriguez-Ibanez N., Garcia-Gonzalez M.A., de la Cruz M.A.F., Et al., Changes in heart rate variability indexes due to drowsiness in professional drivers measured in a real environment, Comput Cardiol, 2012, pp. 913-916, (2012)
[4]  
Bando S., Nozawa A., Detection of driver inattention from fluctuations in vehicle operating data, Artif Life Robot, 20, pp. 28-33, (2015)
[5]  
Sakata O., Shiina T., Saito Y., Estimation of the number of EEG sources in human brain using multidimensional directed information analysis, Trans Inst Electr Eng Jpn C A Publ Electron Inf Syst Soc, 122, 9, pp. 1560-1566, (2002)
[6]  
Takigawa M., Wang G., Kawasaki H., Fukuzako H., EEG analysis of epilpsy by directed coherence method
[7]  
a data processing approach, Int J Psychopysiol, 21, pp. 65-73, (1996)
[8]  
Schelter B., Winterhlder M., Eichler M., Peifer M., Hellwig B., Guschlbauer B., Lucking C.H., Dahlhaus R., Timmer J., Testing for directed influences among neural signals using partial directed coherence, J Neurosci Methods, 152, pp. 210-219, (2005)
[9]  
Baccala L.A., Sameshima K., Partial directed coherence: a new concept in neural structure determination, Biol Cybern, 84, pp. 463-474, (2001)
[10]  
Nozawa A., Mizawa H., Mizuno T., Tanaka H., Ide H., Evaluation of the driver’s mental workload by conversational form based on facial skin thermal image analysis, IEEJ Trans Sens Micromach, 126, 8, pp. 412-418, (2006)