EmbraceNet for Activity: A Deep Multimodal Fusion Architecture for Activity Recognition
This work addresses activity recognition for applications like health monitoring, but appears incremental as it builds on an existing fusion architecture.
The authors tackled human activity recognition using multiple sensors by proposing a deep multimodal fusion model based on EmbraceNet, achieving results submitted to the SHL recognition challenge.
Human activity recognition using multiple sensors is a challenging but promising task in recent decades. In this paper, we propose a deep multimodal fusion model for activity recognition based on the recently proposed feature fusion architecture named EmbraceNet. Our model processes each sensor data independently, combines the features with the EmbraceNet architecture, and post-processes the fused feature to predict the activity. In addition, we propose additional processes to boost the performance of our model. We submit the results obtained from our proposed model to the SHL recognition challenge with the team name "Yonsei-MCML."