Riding Pattern Recognition for Powered Two-Wheelers Using a Long Short-Term Memory Network

The automatic recognition of different riding patterns in the context of naturalistic riding studies (NRSs) facilitates the behavioral analysis of powered two-wheelers (PTW), which is a challenging problem. In the NRS context, various multivariate time series data are provided using an inertial measurement unit (IMU). Modeling the temporal dependency between riding patterns using state-of-the-art machine learning methods is not a straightforward task and requires the extraction of relevant features. In this article, we suggest the use of recurrent neural networks (RNNs) for modeling the temporal dependence between successive patterns without requiring manual feature engineering. Experiments are carried out using a real-world dataset of instrumented motorbikes. The analysis of the network activations and estimated weights allows us to describe the complex riding patterns. Furthermore, comparisons with state-of-the-art machine learning methods show the effectiveness of RNNs in the identification of riding patterns.