An AI Architecture for Detecting Irregular Cycling Forms Using IMU-LiDAR Fusion

Abstract

Due to the recent popularity of sports cycling, many people are interested in cycling tourism. Notifying novices of irregular cycling forms is helpful to avoid muscle fatigue. However, the conventional technologies for detecting irregular cycling forms by inertial measurement unit (IMU) alone face significant limitations in accurately capturing cycling movements. Moreover, simple threshold-based methods fail to account for individual differences among cyclists and fatigue-induced variations. Hence, we propose a novel AI architecture for detecting irregular cycling forms. The proposed architecture combines data from IMUs and 2D Light Detection and Ranging for more accurate detection of irregular cycling forms. Our proposed architecture achieved 0.900 in accuracy, 0.892 in precision, and 0.841 in recall for an F1-score of 0.862, demonstrating the effectiveness of our approaches. Compared to models using only IMUs data, our proposed architecture achieved a significant improvement in precision, increasing by 10.0 points.