CAN YOU SEE IT?GOOD,SO WE CAN SENSE IT!Pushing the Boundaries of IMU-Based Human Activity Recognition Using Videos

摘要

Today's smartphones and wearable devices come equipped with an array of inertial sensors, along with IMU-based Human Activity Recognition models to monitor everyday activities. However, such models rely on large amounts of annotated training data, which require considerable time and effort for collection. One has to recruit human subjects, define clear protocols for the subjects to follow, and manually annotate the collected data, along with the administrative work that goes into organizing such a recording. The result of this expensive data collection process is the scarcity of high-quality, diverse-enough IMU data for building robust activity recognition models. To mitigate the data scarcity issue, researchers have introduced new architectures and augmentation techniques. While these approaches may provide varying levels of performance improvement, they do not enhance the diversity of recognizable activities.