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Abstract
This poster introduces SynMotion, a novel mmWave-based human motion sensing system addressing the scarcity of training datasets. By synthesizing mmWave signals using existing vision-based human motion datasets, this system overcomes the challenge of collecting and labeling mmWave data, facilitating wider adoption of mmWave technology for applications like activity recognition, skeleton tracking and radar placement recommendation. © 2024 IEEE.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2024) |
| Publisher | IEEE |
| Pages | 307-308 |
| ISBN (Electronic) | 979-8-3503-6201-5 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2024 - Hong Kong, China Duration: 13 May 2024 → 16 May 2024 |
Publication series
| Name | Proceedings - ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN |
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Conference
| Conference | 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2024 |
|---|---|
| Place | China |
| City | Hong Kong |
| Period | 13/05/24 → 16/05/24 |
Funding
This work is supported by the GRF grant from Research Grants Council of Hong Kong (CityU 11213622).
Research Keywords
- Activity Recognition
- Body Skeleton Tracking
- Human Motion Sensing
- Millimeter Wave
RGC Funding Information
- RGC-funded
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GRF: A Trinity Platform with Synthesized mmWaves for Human Motion Sensing
LI, Z. (Principal Investigator / Project Coordinator) & Zhang, J. (Co-Investigator)
1/09/22 → …
Project: Research