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RF-CM
- Source :
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 7:1-28
- Publication Year :
- 2022
- Publisher :
- Association for Computing Machinery (ACM), 2022.
-
Abstract
- Radio-Frequency (RF) based human activity recognition (HAR) enables many attractive applications such as smart home, health monitoring, and virtual reality (VR). Among multiple RF sensors, mmWave radar is emerging as a new trend due to its fine-grained sensing capability. However, laborious data collection and labeling processes are required when employing a radar-based sensing system in a new environment. To this end, we propose RF-CM, a general cross-modal human activity recognition framework. The key enabler is to leverage the knowledge learned from a massive WiFi dataset to build a radar-based HAR system with limited radar samples. It can significantly reduce the overhead of training data collection. In addition, RF-CM can work well regardless of the deployment setups of WiFi and mmWave radar, such as performing environments, users' characteristics, and device deployment. RF-CM achieves this by first capturing the activity-related variation patterns through data processing schemes. It then employs a convolution neural network-based feature extraction module to extract the high-dimensional features to be fed into the activity recognition module. Finally, RF-CM takes the generalization knowledge from WiFi networks as guide labels to supervise the training of the radar model, thus enabling a few-shot radar-based HAR system. We evaluate RF-CM by applying it to two HAR applications, fine-grained American sign language recognition (WiFi-cross-radar) and coarse-grained gesture recognition (WiFi-cross-RFID). The accuracy improvement of over 10% in both applications demonstrates the effectiveness of RF-CM. This cross-modal ability allows RF-CM to support more cross-modal applications.
Details
- ISSN :
- 24749567
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Accession number :
- edsair.doi...........43f18f4f6afe29b41cfa69aa2e12f892