{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.38276",
  "author_total": 5,
  "authors": [
    {
      "name": "Rahul Rajendra Pai"
    },
    {
      "name": "Marco Dozza"
    },
    {
      "name": "Alexander Rasch"
    },
    {
      "name": "Ali Mohammadi"
    },
    {
      "name": "Marco Capuccini"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fm1qa49zaag4y.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.38276",
  "fulltext_url": null,
  "id": "fm1qa49zaag4y",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.38276",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "2213|Traffic and Road Safety"
    ]
  ],
  "openalex": "W7215077871",
  "pdf_url": "https://arxiv.org/pdf/2609.38276",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fm1qa49zaag4y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:58Z",
      "native_id": "2609.38276",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.38276"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning",
  "title_text": "Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:58Z",
  "url": "https://research.draconitic.com/paper/fm1qa49zaag4y",
  "venue": "arXiv",
  "versions": []
}