Detecting driver's distracted behaviour from Wi-Fi

The hypothesis of our research is that our body movements, posture and style of driving gives information about our behavioural state. When the driver is distracted by internal or external factors, his driving style changes. We detect these changes by utilising the non- intrusive, cheap and commercially off the shelf Wi-Fi sensors. We capture and analyse the fluctuations in Channel State Information (CSI) due to unusual body movements. The system detects distracted behaviour based on unusual head turns and arm movements during driving situations. This research is conducted in BMW Group Research, New Technologies and Innovation centre, Germany. We validate the hardware prototype by performing a human study of 40 participants, where the drivers are distracted by inducing unknown triggers. We capture the head and arm movements resulting in response of triggers. We introduce our denoising, phase correction and impulsive windowing technique to separate the human movements and distinguish between different activities. Combining this further with optimum time domain features from peaks and subcarrier analysis of CSI phase information, and applying multi- label classification techniques, the system is able to achieve 94.5% accuracy for head- vs. arm movement separation. We thus prove our hypothesis by spotting prolonged, unusual and frequent upper body movements.