Applying Multiple Knowledge to Sussex-Huawei Locomotion Challenge

Abstract

In recent years, activity recognition (AR) has become prominent in ubiquitous systems. Following this trend, the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge provides a unique opportunity for researchers to test their AR methods against a common, real-life and large-scale benchmark. The goal of the challenge is to recognize eight everyday activities including transit. Our team, JSI-Deep, utilized an AR approach based on combining multiple machine-learning methods following the principle of multiple knowledge. We first created several base learners using classical and deep learning approaches, then integrated them into an ensemble, and finally refined the ensemble's predictions by smoothing. On the internal test data, the approach achieved 96% accuracy, which is a significant leap over the baseline 60%.