Urban Transportation Mode Detection From Inertial and Barometric Data in Pedestrian Mobility

Abstract

This paper presents a novel urban transportation mode detection (TMD) method based on inertial and barometric data. To the best of the authors knowledge, this is the first time that a 3-axis accelerometer, a 3-axis gyroscope, and a barometer are combined for TMD purposes. One contribution was to build and share an optimized TMD dataset in terms of duration (44 hours), number of participants (34), sensor placements (on-foot, waist-attached and in the trouser’s pocket) and considered transportation modes (7). To infer transportation mode information, we tackle two classification scenarios: still, walk, bike, tramway, and bus, on the one hand, and still, walk, bike, elevator, stairs and public transport on the other hand. One major finding was that TMD classification accuracy has been widely overestimated by adopting the familiar randomized cross validation approach. According to the latter, the accuracy of our non-optimized classifiers ranged from 90% to 99%. However, few of them were accurate when the test samples were drawn from data of five unknown subjects. By selecting features in a personalized manner and by studying the importance of the placement of sensors, we realized that the latter significantly impact the performance of TMD models. Thus, we present here two robust TMD classifiers that show respectively an average accuracy of 75.63% for the first scenario and 79.41% for the second scenario using a foot-mounted sensor