FedMHAR: Federated Multimodal Human Activity Recognition using Multi-Agent Reinforcement Learning

Excerpt

arXiv:2609.33492v2 Announce Type: replace Abstract: Human Activity Recognition (HAR) from heterogeneous wearable sensors is fundamental to the Internet of Health Things (IoHT), supporting rehabilitation, elderly care, and smart healthcare. Existing multimodal fusion methods often assign fixed equal weights to sensor streams, overlooking differences in modality importance, acquisition cost, and sensor quality, which can vary due to movement, incorrect placement, or temporary blockage. We propose