In this paper, a machine-learning framework is used for riding pattern recognition. The problem is formulated as a classification task to identify the class of riding patterns using data collected from 3-D accelerometer/gyroscope sensors mounted on motorcycles. These measurements constitute an experimental database used to analyze powered two-wheeler rider behavior. Several well-known machine-learning techniques are investigated, including the Gaussian mixture models, the k-nearest neighbor model, the support vector machines, the random forests, and the hidden Markov models (HMMs), for both discrete and continuous cases.
Additionally, an approach for sensor selection is proposed to identify the significant measurements for improved riding pattern recognition. The experimental study, performed on a real data set, shows the effectiveness of the proposed methodology and the effectiveness of the HMM approach in riding pattern recognition. These results encourage the development of these methodologies in the context of naturalistic riding studies.