DoubleCheck: Single-Handed Cycling Detection with a Smartphone

Riding bicycles with only one hand on the handlebar can severely undermine the operator’s steering capability and threaten road and transportation safety. Prior studies have exploited motion sensors to detect riding contexts and recognize related behaviors. Nevertheless, they fail to integrate a scheme to account for single-handed riding with elements crucial to danger prevention: awareness of the surroundings, response to danger, and convenient adoption. In this work, we proposed, designed, and implemented DoubleCheck: a smartphone-based real-time framework for cycling hand detection and distraction recognition. The method monitors handlebar holding on different road surfaces and recognizes hazardous distraction activities related to single-handed cycling using motion signals captured by a built-in inertial measurement unit in a handlebar-borne smartphone. It was designed on the premise that single-handed cycling enabled operators to adapt their body movements to different (often distracting) activities. We conducted an evaluation experiment using 22 participants on asphalt and pavement. The results indicate that DoubleCheck achieves an F1-score of 0.96 for hand detection and 0.69 for distraction recognition, demonstrating its efficacy as a candidate rider-safety precautionary measure.