A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition

arXiv:2403.15444v1 Announce Type: cross
Abstract: Despite living in a multi-sensory world, most AI models are limited to textual and visual understanding of human motion and behavior. In fact, full situational awareness of human motion could best be understood through a combination of sensors. In this survey we investigate how knowledge can be transferred and utilized amongst modalities for Human Activity/Action Recognition (HAR), i.e. cross-modality transfer learning. We motivate the importance and potential of IMU data and its applicability in cross-modality …

abstract ai models arxiv behavior combination cs.ai cs.cv cs.lg eess.iv eess.sp human modal recognition sensors sensory situational awareness survey textual through transfer transfer learning type understanding visual world