--- format: "aidr-story-markdown/v1" id: "d8658c9aaa0aa4238455b1c6b5b90c078d58fa265c589cdc1a968da3559a8622" canonical_url: "https://aidr.today/d8658c9a?lang=en" title: "Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning" lang: "en" requested_lang: "en" available_langs: ["en","vi"] translation_fallback: null fallback_fields: [] published_at: "2026-10-01T04:00:00.000Z" category: "Research" topics: ["safety"] source_urls: ["https://arxiv.org/abs/2609.38276"] summary: "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" --- # Kinematic signatures of impairment: Detecting alcohol intoxication in e\-scooter riders using sensor data and machine learning > [Open the canonical story]() **Published:** 2026-10-01T04:00:00.000Z **Category:** Research **Topics:** safety ## Summary 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 ## Sources - [Story source]()