基于手环数据的愤怒驾驶行为实时检测方法

doi:10.13229/j.cnki.jdxbgxb.20230184

摘要/Abstract

摘要：

利用大众普遍使用的智能手环设计了驾驶人愤怒驾驶行为检测方法，为愤怒驾驶行为有效监测提供了新的途径和方法。本文招聘50名驾驶人开展模拟驾驶实验，设计了引发愤怒的模拟驾驶场景，利用手环采集数据获取心率指标HR和RR.mean、SDNN、RMSSD、PNN50、SDSD、HF、LF、LF/HF八个心率变异性（HRV）指标，对采集指标与愤怒驾驶行为进行关联研究，筛选显著性影响指标，利用支持向量机（SVM）、K-近邻（KNN）和线性判别分析（LDA）3种方法建立愤怒驾驶行为检测模型，并对其进行验证。结果表明：KNN算法的模型愤怒识别效果最好，对愤怒强度识别的准确率能达到75%，对愤怒状态识别的准确率为86%。结果表明：可穿戴式设备（智能手环）可以合理地检测驾驶人的愤怒情绪状态及愤怒情绪强度。

关键词: 载运工具运用工程, 愤怒驾驶行为, 机器学习, 智能手环, 心率变异性

Abstract:

A method for detecting drivers' angry driving behavior has been designed using widely used popular smart bracelet， which provides a new way and method for effective monitoring of angry driving behavior. 50 drivers were recruited to conduct a simulated driving experiment， and a simulated driving scene that caused anger was designed. Then， heart rate index HR and eight heart rate variability （HRV） indexes such as RR.mean， SDNN， RMSSD， PNN50， SDSD， HF， LF and LF/HF obtained from bracelet collection data were used to study the correlation between the acquisition indexes and the angry driving behavior， and screen the significant influence indexes Finally， using three methods， namely support vector machine （SVM）， K-nearest neighbor （KNN） and linear discriminant analysis （LDA）， established and verified the detection model of angry driving behavior. The results show that the model based on KNN algorithm has the best performance on anger recognition. The accuracy of anger intensity recognition can reach 75%， and the accuracy of anger state recognition is 86 %. The results show that the wearable device （smart bracelet） can reasonably detect the driver 's anger state and anger intensity.

Key words: vehicle application engineering, anger driving behavior, machine learning, smart bracelet, heart rate variability

中图分类号:

U492.8

牛世峰,于士杰,刘彦君,马冲. 基于手环数据的愤怒驾驶行为实时检测方法[J]. 吉林大学学报(工学版), 2024, 54(12): 3505-3512.

Shi-feng NIU,Shi-jie YU,Yan-jun LIU,Chong MA. Real-time detection method of angry driving behavior based on bracelet data[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3505-3512.

图/表 13

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图2

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图4

表1

表2

图5

表3

表4

评价指标及公式"

指标	公式
TPR	$T P R = T P T P + F N × 100 %$
FPR	$F P R = F P T N + F P × 100 %$
PPA	$P P A = T P T P + F P × 100 %$
F₁	$F 1 = 2 × P P A × T P R T P R + P P A$
ACC	$A C C = T P + T N T P + F P + F N + F P × 100 %$
AUC	$A U C = ∑ i ∈ U r a n k i - M × 1 + M 2 M × N$

表4

表5

表6

图6

表7

参考文献 18

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正常情绪实验场景	愤怒诱导实验场景
道路顺畅且无其他车辆干扰的城市道路，天气条件正常，能见度清晰，双向四车道，限速为60 km/h，相对愤怒诱导场景进行设置	A：周边车辆抢道加塞
	B：交通拥挤
	C：等红灯
	D：前车启动慢

特征类型	符号	含义
心率	HR/（次·min^-1）	-
HRV 时域特征	RR.mean/ms	RR间期均值
	SDNN/ms	RR间期标准差
	RMSSD/ms	RR间期差值均方根
	NN50/次	RR间期大于50 ms的个数
	PNN50/%	NN50除以NN间期总数的比例
HRV 频域特征	LF/ms²	低频功率（0.04~0.15 Hz）
HRV 频域特征	HF/ms²	高频功率（0.15~0.4 Hz）
	LF/HF	LF和HF的比值
非线性指标	SD₁	表示垂直于标识线点的标准偏差
	SD₂	表示的同一条直线的标准偏差
	SD₂/SD₁	横纵标准偏差比值

指标	卡方	自由度	渐近显著性
HR	155.114	2	.000
RR.mean	98.175	2	.000
SDNN	10.644	2	.005
RMSSD	22.942	2	.000
PNN50	14.460	2	.001
SD₂	7.702	2	0.026
SD₁	1.986	2	0.159
SD₂/SD₁	7.900	2	0.005
LF	0.215	2	0.834
HF	78.046	2	0.000
LF/HF	4.853	2	0.063

模型	指标	TPR	PPA	F₁	FPR
SVM	正常	0.909	0.833	0.870	0.190
	轻微	0.667	0.750	0.706	0.171
	强烈	0.600	0.48	0.533	0.121
KNN	正常	0.901	0.826	0.862	0.184
	轻微	0.812	0.720	0.763	0.242
	强烈	0.825	0.825	0.825	0.033
LDA	正常	0.835	0.815	0.824	0.181
	轻微	0.872	0.667	0.756	0.331
	强烈	0.500	0.952	0.656	0.005

评价指标	SVM	KNN	LDA
TPR	0.725	0.846	0.736
FPR	0.161	0.153	0.172
PPA	0.688	0.790	0.811
F₁ score	0.703	0.817	0.745
AUC	0.940	0.940	0.940
ACC	0.734	0.759	0.737