Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning

A controlled experiment with 25 participants riding an instrumented e-scooter found that an entropy-based multi-class logistic regression classifier distinguished sober from alcohol-intoxicated riding with 85% overall accuracy and a weighted one-vs-rest AuROC of 0.94, according to an arXiv paper (arXiv:2609.38276v1). Riders were tested sober and at targeted blood alcohol concentrations of 0.05% and 0.08% while a six-axis inertial measurement unit and throttle and brake lever position sensors sampled at 100 Hz; seven kinematic features showed entropy decreasing (p < 0.001) and standard deviation increasing (p < 0.01) with intoxication. Steering rate and lateral acceleration were the most important predictive features, and the sober-vs-high AuROC reached 1.00, supporting onboard kinematic sensing as a foundation for automatic intoxication detection that preserves mobility for sober riders.

arXiv:2609.38276v1 Announce Type: new 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.