{"slug": "kinematic-signatures-of-impairment-detecting-alcohol-intoxication-in-e-scooter", "title": "Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning", "summary": "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.", "body_md": "arXiv:2609.38276v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/kinematic-signatures-of-impairment-detecting-alcohol-intoxication-in-e-scooter", "canonical_source": "https://arxiv.org/abs/2609.38276", "published_at": "2026-10-02 04:00:00+00:00", "updated_at": "2026-10-02 04:16:13.029988+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "autonomous-vehicles"], "entities": ["arXiv", "e-scooter", "inertial measurement unit"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/kinematic-signatures-of-impairment-detecting-alcohol-intoxication-in-e-scooter", "markdown": "https://wpnews.pro/news/kinematic-signatures-of-impairment-detecting-alcohol-intoxication-in-e-scooter.md", "text": "https://wpnews.pro/news/kinematic-signatures-of-impairment-detecting-alcohol-intoxication-in-e-scooter.txt", "jsonld": "https://wpnews.pro/news/kinematic-signatures-of-impairment-detecting-alcohol-intoxication-in-e-scooter.jsonld"}}