Enhanced SHL Recognition Using Machine Learning and Deep Learning Models with Multi-source Data

Mengyuan Li, Jun Zhu, Yuanyuan Zhang, Xiaoling Lu

Abstract: The Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge collects sensor data for activity recognition, garnering significant interest among researchers. Our team, named "Juliet", extracts features from multi-source data. It is worth noting that we incorporate OpenStreetMap (OSM) data as supplementary information, which significantly improves the prediction performance. We employ an ensemble model that combines both machine learning and deep learning techniques. For machine learning, we utilize XGBoost, LightGBM, and CatBoost models that take hand-crafted features. For deep learning, we adopt a CNN-RNN-Transformer framework that accepts both raw features and hand-crafted features as input. By combining the ensemble model with post-smoothing, our approach enhances the accuracy of SHL recognition.