iRider: Integrating Sensors and Cameras for In-depth Biomechanical Analysis of Electric Scooter
Architecting real-time vision systems for e-scooter rider evaluation through LiDAR and sensor fusion.
The Challenge
As urban mobility shifts towards micro-vehicles, rider safety becomes a data-driven imperative. We faced the complex task of designing a collaborative research framework to analyze rider safety through high-frequency sensor data and LiDAR environmental mapping, bridging the gap between raw physics and digital intelligence.
Engineering Architecture
Real-Time Pipeline
Designed to process heterogeneous sensor data and high-speed camera feeds with sub-millisecond latency for instant biomechanical feedback.
LiDAR Vision
AI-powered system for environment scanning and obstacle detection using dense point clouds to reconstruct 3D urban contexts.
Pattern Recognition
ML models trained for biomechanical evaluation and predictive analytics, identifying risk factors before accidents occur.
"Real-Time Biomechanical Evaluation of E-Scooter Riders"
Presented at the IEEE Applied Sensing Conference 2024, sharing insights with over 100+ industry professionals and academics. The research highlights the intersection of computer vision and human safety dynamics.
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Global Academic
Validation
Peer-reviewed by leading experts in biomechanics and sensing technology.
Validated against real-world urban transit datasets.
Open-source framework contributing to urban safety standards.
Ready for the full technical breakdown?
Explore the deep-level implementation details and the open-source code behind iRider.