Xiao, Chunjing;
Han, Yanhui;
Yang, Wei;
Hou, Yane;
Shi, Fangzhan;
Chetty, Kevin;
(2024)
Diffusion-Model-Based Contrastive Learning for Human Activity Recognition.
IEEE Internet of Things Journal
, 11
(20)
33525 -33536.
10.1109/JIOT.2024.3429245.
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Abstract
WiFi channel state information (CSI)-based activity recognition has sparked numerous studies due to its widespread availability and privacy protection. However, when applied in practical applications, general CSI-based recognition models may face challenges related to the limited generalization capability, since individuals with different behavior habits will cause various fluctuations in the CSI data and it is difficult to gather enough training data to cover all kinds of motion habits. To tackle this problem, we design a diffusion model-based contrastive learning framework for human activity recognition (CLAR) using WiFi CSI. On the basis of the contrastive learning framework, we primarily introduce two components for CLAR to enhance the CSI-based activity recognition. To generate diverse augmented data and complement limited training data, we propose a diffusion model-based time series-specific augmentation model. In contrast to typical diffusion models that directly apply conditions to the generative process, potentially resulting in distorted CSI data, our tailored model dissects these condition into the high-frequency and low-frequency components, and then applies these conditions to the generative process with varying weights. This can alleviate the data distortion and yield high-quality augmented data. To efficiently capture the difference of the sample importance, we present an adaptive weight algorithm. Different from the typical contrastive learning methods which equally consider all the training samples, this algorithm adaptively adjusts the weights of positive sample pairs for learning better data representations. The experiments suggest that the CLAR achieves significant gains compared to the state-of-the-art methods.
| Type: | Article |
|---|---|
| Title: | Diffusion-Model-Based Contrastive Learning for Human Activity Recognition |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/JIOT.2024.3429245 |
| Publisher version: | https://doi.org/10.1109/JIOT.2024.3429245 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Activity recognition; contrastive learning; diffusion probabilistic models; self-supervised learning; WiFi channel state information (CSI) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10194462 |
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