损伤的运动学特征：利用传感器数据和机器学习检测电动滑板车骑行者酒精中毒

中文摘要 AI 辅助

酒精中毒是导致电动滑板车骑行者死亡和重伤事故的主要因素。目前的应对措施，如限时骑行或骑行前认知筛查，无法实时持续评估骑行者的身体运动控制或损伤状态。我们进行了一项对照实验，25名参与者在清醒状态下以及两个目标血液酒精浓度水平（0.05%和0.08%）下，驾驶一辆装有仪器的电动滑板车通过测试赛道。该电动滑板车配备了六轴惯性测量单元（IMU）以及油门和刹车手柄位置传感器，所有数据以100 Hz采样。计算了两个互补的信号特征：归一化排列熵（量化时间复杂性）和标准差（量化信号幅度）。重复测量相关性分析确定了七个运动学特征（所有IMU和油门信号），随着醉酒程度的增加，这些特征的熵降低（p < 0.001），而标准差增加（p < 0.01），表明醉酒的骑行者从连续的、低幅度的微调校正转变为较少但高幅度的反应性校正。基于熵的多类逻辑回归分类器，通过留一参与者交叉验证评估，实现了85%的总体准确率和0.94的加权一对一接收者操作特征曲线下面积（AuROC），其中清醒与高醉状态的AuROC为1.00。转向速率和横向加速度是最重要的预测特征，表明酒精在骑行过程中导致横向平衡的显著崩溃。最终，这些结果表明，车载运动学传感结合基于熵的信号分析能够可靠地区分清醒与醉酒的电动滑板车骑行，为自动醉酒检测系统提供了基础，同时保留清醒骑行者的出行能力。

英文摘要

Alcohol intoxication is a leading contributor to fatal and severe-injured e-scooterist crashes. Current countermeasures, such as temporal restrictions or pre-ride cognitive screening, cannot continuously assess an e-scooterist's physical motor control or impairment in real time. We conducted a controlled experiment in which 25 participants rode an instrumented e-scooter through a test track while sober and at two targeted blood alcohol concentration levels (0.05% and 0.08%). The e-scooter was instrumented with a six-axis inertial measurement unit (IMU), and throttle and brake lever position sensors, all sampled at 100 Hz. Two complementary signal features were computed: normalised permutation entropy, which quantifies temporal complexity, and standard deviation, which quantifies signal amplitude. Repeated measures correlation identified seven kinematic features (all IMU and throttle signals) whose entropy decreased (p < 0.001) while standard deviation increased (p < 0.01) with increasing intoxication, indicating that intoxicated riders shift from continuous, low-amplitude micro-corrections to fewer, high-amplitude reactive corrections. An entropy based multi-class logistic regression classifier, evaluated through leave-one-participant-out cross-validation, achieved 85% overall accuracy and a weighted one-vs-rest area under the receiver operating characteristic curve (AuROC) of 0.94, with a sober-vs-high AuROC of 1.00. Steering rate and lateral acceleration were the most important predictive features, indicating that alcohol induces a distinct collapse in lateral equilibrium during riding. Ultimately, these results demonstrate that onboard kinematic sensing combined with entropy-based signal analysis can reliably distinguish sober from intoxicated e-scooter riding, providing a foundation for automatic intoxication detection systems that preserve mobility for sober riders.

发表机构

• Chalmers University of Technology（查尔姆斯理工大学）
• Voi Technology AB

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