arXiv：cs.LG（机器学习，全量分类）· Rahul Rajendra Pai, Marco Dozza, Alexander Rasch, Ali Mohammadi, Marco Capuccini·· 5 小时前AI 评分34
利用传感器数据与机器学习检测电动滑板车骑行者酒精中毒的运动学特征
Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning
AI 导读
一项控制实验让 25 名参与者在清醒及血液酒精浓度 0.05% 和 0.08% 状态下骑行装有六轴 IMU 与油门、刹车位置传感器的电动滑板车，采样率 100 Hz。基于熵的多分类逻辑回归分类器经留一参与者交叉验证达到 85% 总体准确率，加权 AuROC 为 0.94，清醒与高浓度对比 AuROC 达 1.00。转向速率与横向加速度是最重要的预测特征，表明酒精会引发骑行中横向平衡的明显崩溃。
正文
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Signal Processing (eess.SP); Applications (stat.AP) |
| Cite as: | arXiv:2609.38276 [cs.LG] |
| (or arXiv:2609.38276v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.38276 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Rahul Rajendra Pai [view email]
[v1]
Tue, 29 Sep 2026 15:40:39 UTC (2,491 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org