Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning
Authors: Rahul Rajendra Pai, Marco Dozza, Alexander Rasch, Ali Mohammadi, Marco Capuccini
Organizations: Chalmers University of Technology, Division of Vehicle Safety, Department of Mechanical Engineering, Gothenburg, Sweden · Voi Technology AB, Stockholm, Sweden
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.
Figures & tables
Fig. 1: Instrumentation on the vehicle (Panel A) and the layout of the MicroLab test track (Panel B).
Feature
PE p -value
SD p -value
ry (steering rate)
2.50×10−28
1.75×10−10
rz (roll rate)
1.79×10−24
7.81×10−13
ax (lateral acceleration)
5.75×10−21
8.68×10−3
rx (pitch rate)
1.01×10−16
1.07×10−14
throttle
1.92×10−8
9.07×10−3
ay (vertical acceleration)
6.47×10−6
1.98×10−8
Table 1: Repeated measures correlations between features and intoxication level. PE was tested with H1:ρ<0 (complexity decreases); SD was tested with H1:ρ>0 (amplitude increases).
Fig. 2: Within-subject centred (WSC) permutation entropy (PE) distributions for all nine evaluated signals stratified by intoxication condition (sober, low, high).
Fig. 3: Within-subject centred (WSC) standard deviation (SD) distributions for all nine evaluated signals stratified by intoxication condition (sober, low, high).