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Found 4 papers, 2 papers with code
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition
With the emergence of generative AI models such as large language models (LLMs) and text-driven motion synthesis models, language has become a promising source data modality as well as shown in proof of concepts such as IMUGPT.
On the Benefit of Generative Foundation Models for Human Activity Recognition
In human activity recognition (HAR), the limited availability of annotated data presents a significant challenge.
Generating Virtual On-body Accelerometer Data from Virtual Textual Descriptions for Human Activity Recognition
We benchmarked our approach on three HAR datasets (RealWorld, PAMAP2, and USC-HAD) and demonstrate that the use of virtual IMU training data generated using our new approach leads to significantly improved HAR model performance compared to only using real IMU data.
Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data
In this work we first introduce a measure to quantitatively assess the subtlety of human movements that are underlying activities of interest--the motion subtlety index (MSI)--which captures local pixel movements and pose changes in the vicinity of target virtual sensor locations, and correlate it to the eventual activity recognition accuracy.
