Alcohol-impaired driving remains a major yet preventable cause of road traffic injury and death, with many drivers underestimating their level of intoxication. Compared to in-vehicle systems, mobile drunk-driving detection using consumer smartwatches offers a scalable way to trigger preventive interventions and increase awareness without additional in-vehicle hardware. We introduce a system that leverages wrist accelerometer data and heart rate variability-derived physiological signals to detect alcohol-related driving impairment. We collected data in a randomized, controlled three-arm test-track study (n=54) and trained both logistic regression models with window-aggregated features and a two-tower 1D convolutional neural network (CNN), to detect alcohol-impaired driving. The CNN achieved a participant-averaged area under the receiver operating characteristic (AUROC) of 0.88 for detecting any alcohol intoxication and 0.86 for detecting driving above the WHO-recommended limit of 0.05 g/dL. To the best of our knowledge, this is the first work to (1) demonstrate drunk-driving detection using consumer smartwatches, (2) develop and evaluate such a system in a real vehicle on a closed test track, and (3) rigorously assess generalization to unseen participants. Together, these findings highlight the potential of wearable-based sensing to support scalable, measurement-driven prevention of alcohol-related traffic harm.

翻译：酒精性驾驶损伤仍是导致道路交通伤害与死亡的主要可预防因素，且许多驾驶员低估自身醉酒程度。相较于车载系统，利用消费级智能手表开展移动端酒驾检测无需额外车载硬件，具备规模化触达预防干预并提升公众意识的潜力。本文提出一种基于手腕加速度计数据与心率变异性生理信号的系统，用于检测酒精相关性驾驶损伤。我们在随机对照三组封闭道路测试研究（n=54）中采集数据，分别训练了基于窗口聚合特征的逻辑回归模型与双塔一维卷积神经网络（CNN）。该CNN模型对任意酒精摄入的检测参与者平均受试者工作特征曲线下面积（AUROC）为0.88，对超过世界卫生组织推荐血液酒精浓度阈值（0.05 g/dL）的驾驶行为检测AUROC达0.86。据我们所知，本研究首次：（1）验证了消费级智能手表用于酒驾检测的可行性；（2）在封闭测试道路的真实车辆环境中开发并评估了此类系统；（3）严格评估了对未知参与者的泛化能力。上述发现共同揭示了基于可穿戴传感技术支撑可规模化、数据驱动的酒精性交通伤害预防的潜力。