A Post-processing Machine Learning for Activity Recognition Challenge with OpenStreetMap Data

Shiyao Huang, Junliang Lyu, Sinian Zhang, Ruiying Tang, Huan Xiao, Yuanyuan Zhang, Xiaoling Lu

Abstract: This paper aims to address the Sussex-Huawei Locomotion - Transportation (SHL) recognition challenge organized at the HASCA Workshop of UbiComp 2023. The challenge focuses on achieving user-independent recognition of eight different modes of activities using motion and GPS sensor data[3, 9]. Our team, named DataScience SHL Team, proposes a pipeline, which involves extracting features including time domain, motion position, road map, and differential features, utilizing the OpenStreetMap platform as a additional resource. We carefully select the Random Forest model as our classification model. Additionally, a post-processing approach is introduced to modify labels. Since the test data partition lacks identification, we have aggregated models trained at four sites, enhancing the overall performance and robustness. The proposed pipeline has achieved an accuracy of 84.38% and an f1-score of 57.49% for hand data during the validation phase, demonstrating a significant improvement in motion state recognition.