EmbraceNet for Activity: A Deep Multimodal Fusion Architecture for Activity Recognition

Abstract

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."

arXiv commentAccepted in HASCA at ACM UbiComp/ISWC 2019, won the 2nd place in the SHL Recognition Challenge 2019

References in corpus (1)

• EmbraceNet: A robust deep learning architecture for multimodal classification

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• Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition
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• A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation