Open Set Mixed-Reality Human Activity Recognition
Zixuan Zhang, Lei Chu, Songpengcheng Xia, Ling Pei · 2021 IEEE Global Communications Conference (GLOBECOM) · 2021
Sensor-based human activity recognition (HAR) is a fundamental problem that can have a broad impact on many research/industrial fields. The deep learning methods pave the way for extracting robust and informative features from the non-stationary HAR data, achieving high-accuracy HAR. Most of the works in the literature consider the closed set activity recognition, which assumes all classes of activities are known in both the training and test stages. However, it is a challenging way to apply deep learning models to real-world applications, which can contain unseen activities. These activities will sharply decrease the performance of the deep learning models. To this end, we introduce the problem of Open Set Mixed-Reality (OSM) HAR, which aims to recognize unseen activities while classify seen samples. Furthermore, we propose a novel balanced open set backpropagation method to realize accurate and robust OSM-HAR. Lastly, we verify the effectiveness of the proposed method with a publicly available and our newly collected dataset.