User-Independent Motion and Location Analysis for Sussex-Huawei Locomotion Data

Abstract

Transportation mode detection (TMD) is a context-aware computing technology with significant potential in several applications. However, the development of TMD technologies for real-world scenarios remains challenging, including user-independent evaluations and multimodal analyses. In this study, our team (HYU-CSE) suggested a TMD model as part of the Sussex-Huawei Locomotion (SHL) recognition challenge, and we used the SHL motion and location data. The proposed TMD model was based on the DenseNet architecture, and post-processing using voting schemes was applied to refine the detection performance. The results suggested that the proposed method achieved 94.13% of an F1 score with user-independent analysis. We hope that our study will ultimately help in the design of better TMD applications.