Manoeuvre segmentation using smartphone sensors

In this paper, we propose a classifier-based approach for driving manoeuvre recognition from mobile phone data. We introduce a driving manoeuvre classifier using Support Vector Machines (SVM). We investigate the performance of a sliding window of velocity and angular velocity signals obtained using a smartphone as features for our classifier. Principal Component Analysis (PCA) is used for dimensionality reduction. The classifiers use a vehicle simulation for training data and experimental data for validation. A novel technique to extract the rotation matrix using PCA is presented to calibrate the smartphone's orientation. A classifier performance of 0.8158 average precision and 0.8279 average recall was achieved resulting in an average F1 score of 0.8194. Balanced accuracy was calculated to be 0.8874.