Li Ruojun, Agu Emmanuel, Sarwar Atifa, Grimone Kristin, Herman Debra, Abrantes Ana M, Stein Michael D
Department of Optical Information, Huazhong University of Science and Technology, Wuhan, China.
Department of Electrical and Computer Engineering, Worcester Polytechnic Institute(WPI), Worcester, MA, USA.
IEEE Sens J. 2023 Dec;23(23):29733-29748. doi: 10.1109/jsen.2023.3248868. Epub 2023 Apr 7.
Consuming excessive amounts of alcohol causes impaired mobility and judgment and driving accidents, resulting in more than 800 injuries and fatalities each day. Passive methods to detect intoxicated drivers beyond the safe driving limit can facilitate Just-In-Time alerts and reduce Driving Under the Influence (DUI) incidents. Popularly-owned smartphones are not only equipped with motion sensors (accelerometer and gyroscope) that can be employed for passively collecting gait (walk) data but also have the processing power to run computationally expensive machine learning models. In this paper, we advance the state-of-the-art by proposing a novel method that utilizes a Bi-linear Convolution Neural Network (BiCNN) for analyzing smartphone accelerometer and gyroscope data to determine whether a smartphone user is over the legal driving limit (0.08) from their gait. After segmenting the gait data into steps, we converted the smartphone motion sensor data to a Gramian Angular Field (GAF) image and then leveraged the BiCNN architecture for intoxication classification. Distinguishing GAF-encoded images of the gait of intoxicated vs. sober users is challenging as the differences between the classes (intoxicated vs. sober) are subtle, also known as a fine-grained image classification problem. The BiCNN neural network has previously produced state-of-the-art results on fine-grained image classification of natural images. To the best of our knowledge, our work is the first to innovatively utilize the BiCNN to classify GAF encoded images of smartphone gait data in order to detect intoxication. Prior work had explored using the BiCNN to classify natural images or explored other gait-related tasks but not intoxication Our complete intoxication classification pipeline consists of several important pre-processing steps carefully adapted to the BAC classification task, including step detection and segmentation, data normalization to account for inter-subject variability, data fusion, GAF image generation from time-series data, and a BiCNN classification model. In rigorous evaluation, our BiCNN model achieves an accuracy of 83.5%, outperforming the previous state-of-the-art and demonstrating the feasibility of our approach.
过量饮酒会导致行动能力和判断力受损以及交通事故，每天造成800多人伤亡。检测超出安全驾驶限制的醉酒司机的被动方法有助于即时发出警报并减少酒后驾车（DUI）事件。大众普遍拥有的智能手机不仅配备了可用于被动收集步态（行走）数据的运动传感器（加速度计和陀螺仪），而且还具备运行计算成本高昂的机器学习模型的处理能力。在本文中，我们提出了一种新颖的方法，通过利用双线性卷积神经网络（BiCNN）来分析智能手机加速度计和陀螺仪数据，以确定智能手机用户的步态是否超过法定驾驶限制（0.08），从而推动了技术发展。在将步态数据分割成步长后，我们将智能手机运动传感器数据转换为格拉姆角场（GAF）图像，然后利用BiCNN架构进行醉酒分类。区分醉酒用户与清醒用户的步态的GAF编码图像具有挑战性，因为不同类别（醉酒与清醒）之间的差异很细微，这也被称为细粒度图像分类问题。BiCNN神经网络此前在自然图像的细粒度图像分类方面取得了领先成果。据我们所知，我们的工作是首次创新性地利用BiCNN对智能手机步态数据的GAF编码图像进行分类以检测醉酒状态。先前的工作曾探索使用BiCNN对自然图像进行分类或探索其他与步态相关的任务，但未涉及醉酒检测。我们完整的醉酒分类流程包括几个针对BAC分类任务精心调整的重要预处理步骤，包括步长检测与分割、考虑个体间差异的数据归一化、数据融合、从时间序列数据生成GAF图像以及一个BiCNN分类模型。在严格评估中，我们的BiCNN模型达到了83.5%的准确率，超过了先前的先进水平，证明了我们方法的可行性。
