HearSmoking: Smoking Detection in Driving Environment via Acoustic Sensing on Smartphones

2022-08-01

IEEE Transactions on Mobile Computing

AI Summary

Smoking while driving poses a significant threat to road safety, yet existing detection methods typically rely on intrusive sensors or auxiliary hardware. This paper proposes a contactless, end-to-end smoking behavior detection framework leveraging smartphone acoustic sensing: it exploits speaker–microphone co-design to emit and capture acoustic signals, capturing dynamic changes in acoustic correlation induced by the coupling of hand motion and thoracic respiration during smoking. We introduce the first temporal periodicity model characterizing composite smoking actions, enabling simultaneous hand-motion classification and respiratory rhythm analysis. The method integrates relative correlation coefficient computation, CNN-based feature learning, and explicit periodicity modeling. Evaluated under realistic driving conditions, it achieves real-time performance with an average accuracy of 93.44%. This work establishes a novel paradigm for unobtrusive, in-vehicle safety monitoring.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Computer Vision: Motion & Tracking

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

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.

Problem

• Detect smoking while driving using smartphone acoustics
• Improve safety via non-contact acoustic sensing method
• Classify hand movements and respiration patterns accurately

Innovation

• Acoustic sensing via smartphone for smoking detection
• Relative Correlation Coefficient for movement analysis
• CNN classification combined with respiration detection