Johnny Ni¹ and Jonathan Sahagun², ¹USA, ²California State Polytechnic University, USA

As micromobility devices like e-scooters rise in popularity, so do safety concerns. GuardRide addresses the growing number of injuries from rider fatigue, excessive speed, and collisions by combining sensor-based monitoring and AI-powered analysis. The system is available in two forms: a Raspberry Pi-based wearable module using a BNO085 IMU and VL53L4CD distance sensor, and a smartphone app using GPS and OpenAI's vision models to detect fatigue [1]. Real-time alerts are delivered through a user interface on both platforms. Challenges included ensuring detection accuracy under varying conditions and minimizing false alerts. Experiments showed strong performance, with high accuracy in identifying fatigue and crashes. Compared to existing solutions, GuardRide is more adaptable to dynamic, outdoor use and doesn't require vehicle enclosures or specialized equipment. By offering proactive safety monitoring in a lightweight, scalable package, GuardRide supports safer urban travel and helps reduce injuries for micromobility users.

Micromobility Safety, Fatigue Detection, AI Monitoring, Wearable Sensors