用手机麦克风听声音，实时识别开车时抽烟行为
HearSmoking: Smoking Detection in Driving Environment via Acoustic Sensing on Smartphones
- 利用手机扬声器发声、麦克风收声，捕捉手部与胸部动作信号
- 在真实驾驶场景中实现93.44%的抽烟事件检测准确率
- 无需额外设备，适合车载安全监控系统部署
近年来，随着汽车数量激增，驾驶安全受到广泛关注。抽烟是威胁驾驶安全的因素之一，却常被司机忽视。现有抽烟检测方法多依赖接触式或需额外设备。为此，我们提出仅使用智能手机音频传感器的抽烟检测系统HearSmoking，以提升驾驶安全。通过分析司机典型抽烟习惯（如手部动作和胸廓起伏），设计由扬声器发出、麦克风接收的声学信号，计算接收信号的相对相关系数，获取手部与胸廓运动模式。处理后的数据送入训练好的卷积神经网络进行手部动作分类，并同步设计呼吸检测方法。为进一步提升性能，分析复合抽烟动作的周期性特征。在真实驾驶环境中开展大量实验，结果表明HearSmoking可实现实时抽烟事件检测，平均总准确率达93.44%。
原文摘要 · Abstract (English)
Driving safety has drawn much public attention in recent years due to the fast-growing number of cars. Smoking is one of the threats to driving safety but is often ignored by drivers. Existing works on smoking detection either work in contact manner or need additional devices. This motivates us to explore the practicability of using smartphones to detect smoking events in driving environment. In this paper, we propose a cigarette smoking detection system, named HearSmoking, which only uses acoustic sensors on smartphones to improve driving safety. After investigating typical smoking habits of drivers, including hand movement and chest fluctuation, we design an acoustic signal to be emitted by the speaker and received by the microphone. We calculate Relative Correlation Coefficient of received signals to obtain movement patterns of hands and chest. The processed data is sent into a trained Convolutional Neural Network for classification of hand movement. We also design a method to detect respiration at the same time. To improve system performance, we further analyse the periodicity of the composite smoking motion. Through extensive experiments in real driving environments, HearSmoking detects smoking events with an average total accuracy of 93.44 percent in real-time.
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