[Submitted on 29 Sep 2026]
Title:Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning
View a PDF of the paper titled Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning, by Rahul Rajendra Pai and 4 other authors
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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
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