A Classical Machine Learning Method for Locomotion and Transportation Recognition using both Motion and Location Data

Guillaume Habault, Huy Quang Ung, Shinya Wada

Abstract: The fifth edition of Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge aims at determining the mode of locomotion and transportation of users using sensor-equipped devices. This recognition task relies on measurements captured from Inertial Measurement Unit and radio sensors placed on the user’s hand, bag, hips and torso. The provided dataset presents several challenges, including its size, asynchronicity between the data types, lack of time continuity, and imbalanced distribution of the locomotion and transportation modes. To address these issues and enhance the recognition performance, our team, KDDI Research, performed data pre-processing and hand-crafted additional features. We explored different classifiers based on both Machine Learning or Deep Learning methods. Finally, the XGBoost Classifier model achieved the highest accuracy and f1-score across different validation datasets (bag, hands, hips, and torso). This model, used for our final submission on the testing dataset, achieved an average accuracy of 0.75 on these datasets.