这项研究把智能手表从健康监测延伸到公共安全领域，做可穿戴设备或交通安全研究的团队值得关注——它证明了消费级硬件也能做高精度酒驾检测，无需额外车载设备。
一项新研究利用市售智能手表的加速度计和心率变异性数据，检测酒精导致的驾驶损伤。研究在封闭测试轨道上进行了随机对照实验（n=54），训练了逻辑回归和1D卷积神经网络模型。CNN模型检测任何酒精摄入的AUROC为0.88，检测超过WHO推荐限值（0.05 g/dL）的AUROC为0.86。这是首个在真实车辆中验证、并严格评估对未见参与者泛化能力的智能手表酒驾检测系统。该成果展示了可穿戴设备在规模化预防酒精相关交通事故中的潜力。
Detecting Drunk Driving Using Off-the-Shelf Smartwatches
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.