Applying 1D sensor DenseNet to Sussex-Huawei locomotion-transportation recognition challenge

Abstract

The Sussex-Huawei Transportation-Locomotion (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp 2019 presents a large and realistic dataset with different activities and transportation. The goal of this machine learning/data science challenge is to recognize eight modes of locomotion and transportation from the inertial sensor data of a smartphone in a mobile-phone placement independent manner. In this paper, our team (We can fly) summarize our submission to the competition. We proposed a 1D DenseNet model, a deep learning method for transportation classification. We first convert sensor readings from phone coordinate system to navigation coordinate system. Then, we normalized each sensor using different maximums and minimums and construct multichannel sensor input. Finally, 1D DenseNet model output the predictions. In the experiment, we utilized three internal datasets to train our model and achieved averaged F1 score 0.78 on four internal datasets.