MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels

arXiv:2404.01958v1 Announce Type: new
Abstract: Human activity recognition (HAR) will be an essential function of various emerging applications. However, HAR typically encounters challenges related to modality limitations and label scarcity, leading to an application gap between current solutions and real-world requirements. In this work, we propose MESEN, a multimodal-empowered unimodal sensing framework, to utilize unlabeled multimodal data available during the HAR model design phase for unimodal HAR enhancement during the deployment phase. From a study on the impact of supervised …

abstract application applications arxiv challenges cs.lg current data design exploit function gap however human labels limitations multimodal multimodal data recognition requirements solutions type will work world