Who’s Riding? Machine Learning-Powered Rider Identification from E-Bike CAN Data

This study investigates the feasibility of identifying individual e-bike riders based on Controller Area Network (CAN) data using machine learning techniques. Datasets from 12 test riders performing various predefined cycling tasks on a dynamometer test bench are collected and used to ensure controlled and reproducible conditions. The recorded CAN data includes various sensor signals, such as power output, cadence, torque, and the used support mode. After pre-processing, two different methods of feature extraction are tested and compared, one based on snapshots of the data and one based on driving events such as braking and accelerating, measured by calculating statistics of the riding data over sliding windows. A range of machine learning models is employed to classify riders based on their distinct riding patterns using the extracted features. The evaluated models comprise k-Nearest Neighbors (KNN), Random Forest and Naive Bayes. The findings demonstrate the efficacy of machine learning in differentiating riders, with Random Forest and KNN achieving the highest and most robust accuracy among the tested models. The KNN-model achieves up to 99% accuracy, the Random Forest up to 75%. The paper analyzes the influence of different signals, feature extraction methods and model parameterizations on the results. The results show that machine learning-based rider identification using CAN data is a viable approach for enhancing e-bike security, authentication, and personalization. Potential applications include theft prevention, automatic user recognition for personalized assistance settings, and access control. Future research could include the exploration of the impact of additional sensor data, real-world outdoor conditions, and deep learning approaches, with the aim to further enhance identification accuracy and efficiency.