DiffAR: Adaptive conditional diffusion model for temporal-augmented human activity recognition
File(s)
Accepted version
Author(s)
Huang, Shuokang
Chen, PoYu
McCann, Julie
Type
Conference Paper
Abstract
Human activity recognition (HAR) is a fundamental sensing and analysis technique that supports diverse applications, such as smart homes and health-care. In device-free and non-intrusive HAR, WiFi channel state information (CSI) captures wireless signal variations caused by human interference without the need for video cameras or on-body
sensors. However, current CSI-based HAR performance is hampered by incomplete CSI recordings due to fixed window sizes in CSI collection and human/machine errors that incur missing values in CSI. To address these issues, we propose DiffAR, a temporal-augmented HAR approach that
improves HAR performance by augmenting CSI. DiffAR devises a novel Adaptive Conditional Diffusion Model (ACDM) to synthesize augmented CSI, which tackles the issue of fixed windows by forecasting and handles missing values with imputation. Compared to existing diffusion models, ACDM improves the synthesis quality by guiding
progressive synthesis with step-specific conditions.
DiffAR further exploits an ensemble classifier for activity recognition using both raw and augmented CSI. Extensive experiments on four public datasets show that DiffAR achieves the best synthesis quality of augmented CSI and outperforms state-of-the-art CSI-based HAR methods in terms of recognition performance. The source code of DiffAR is available at https://github.com/huangshk/DiffAR.
sensors. However, current CSI-based HAR performance is hampered by incomplete CSI recordings due to fixed window sizes in CSI collection and human/machine errors that incur missing values in CSI. To address these issues, we propose DiffAR, a temporal-augmented HAR approach that
improves HAR performance by augmenting CSI. DiffAR devises a novel Adaptive Conditional Diffusion Model (ACDM) to synthesize augmented CSI, which tackles the issue of fixed windows by forecasting and handles missing values with imputation. Compared to existing diffusion models, ACDM improves the synthesis quality by guiding
progressive synthesis with step-specific conditions.
DiffAR further exploits an ensemble classifier for activity recognition using both raw and augmented CSI. Extensive experiments on four public datasets show that DiffAR achieves the best synthesis quality of augmented CSI and outperforms state-of-the-art CSI-based HAR methods in terms of recognition performance. The source code of DiffAR is available at https://github.com/huangshk/DiffAR.
Date Acceptance
2023-04-20
Copyright Statement
© 2023 The Author(s)
Source
The 32nd International Joint Conference on Artificial Intelligence
Publication Status
Accepted
Start Date
2023-08-19
Finish Date
2023-08-25
Coverage Spatial
Macao, China