الملخص
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.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Pai, R. R., Dozza, M., Rasch, A., Mohammadi, A., & Capuccini, M. (2026). Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning. https://omanscience.com/ar/articles/kinematic-signatures-of-impairment-detecting-alcohol-intoxication-in-e-scooter-riders-using-sensor-data-and-machine-learning
MLA 9
Pai, Rahul Rajendra, et al. "Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning." https://omanscience.com/ar/articles/kinematic-signatures-of-impairment-detecting-alcohol-intoxication-in-e-scooter-riders-using-sensor-data-and-machine-learning.
شيكاغو (المؤلف–التاريخ)
Pai, Rahul Rajendra, Marco Dozza, Alexander Rasch, Ali Mohammadi, and Marco Capuccini. 2026. "Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning." https://omanscience.com/ar/articles/kinematic-signatures-of-impairment-detecting-alcohol-intoxication-in-e-scooter-riders-using-sensor-data-and-machine-learning.
هارفارد
Pai, R. R., Dozza, M., Rasch, A., Mohammadi, A. and Capuccini, M. (2026) 'Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning', Available at: https://omanscience.com/ar/articles/kinematic-signatures-of-impairment-detecting-alcohol-intoxication-in-e-scooter-riders-using-sensor-data-and-machine-learning.
فانكوفر
Pai RR, Dozza M, Rasch A, Mohammadi A, Capuccini M. Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning. https://omanscience.com/ar/articles/kinematic-signatures-of-impairment-detecting-alcohol-intoxication-in-e-scooter-riders-using-sensor-data-and-machine-learning
IEEE
R. R. Pai, M. Dozza, A. Rasch, A. Mohammadi, and M. Capuccini, "Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning," https://omanscience.com/ar/articles/kinematic-signatures-of-impairment-detecting-alcohol-intoxication-in-e-scooter-riders-using-sensor-data-and-machine-learning.