BACFuse: Toward Noise-Resistant BAC Detection Based on Multimodal Fusion on Smartphone

Drunk-driving is an important factor causing road traffic accidents and deaths, which deserves a lot of research. However, most current methods for detecting drunk-driving depend on customized hardware or require users’ active participation, making it impractical to monitor blood alcohol content (BAC) during driving. This article introduces BACFuse, a device-free, contactless, and noninvasive system utilizing smartphone in driving environments, which achieves relatively high accuracy in drunk-driving monitoring by integrating various voice sensing modalities. BACFuse first captures vocal cord vibration from ultrasonic signals, then records voice commands from audio signals. BACFuse combines the ultrasonic signals with audio signals and effectively detects drunk-driving and BAC. A key enabler lies in our modeling of latent interaction between acoustic and ultrasonic signals to mitigate ambient noise, realizing noise-resistant drunk-driving detection. Additionally, we propose an effective modules within the co-attention method to fuse the multimodal signals, further enhancing the accuracy of drunk-driving detection. We conduct extensive experiments to evaluate BACFuse’s performance on 20 participants in safe laboratory experiments. The results demonstrate that our system achieves BAC measurement with an MAE of 2.13 mg/dl, showing promise for future in-car driving management paradigms.