A Novel Human Activity Recognition Framework Based on Pre-Trained Foundation Model

Human activity recognition is closely related to human health and is a hot research topic. With the continuous development of AI technology, large models have shown great potential in various training tasks. However, there is limited analysis of human sensor motion data. In this study, we fine-tuned large models on five motion datasets, designed an adaptation layer suitable for time series data to extract action representations fully, and proposed a novel HAR framework. We conducted action recognition experiments on five foundation models and compared them with ten classical algorithm models. The experimental results show that the accuracy of action recognition in small-parameter pre-trained large models can reach 98.8%, indicating significant research potential.