Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.16764 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909547135762432 |
|---|---|
| author | Nirmal, Isura Hu, Wen Hassan, Mahbub Aboutanios, Elias Khamis, Abdelwahed |
| author_facet | Nirmal, Isura Hu, Wen Hassan, Mahbub Aboutanios, Elias Khamis, Abdelwahed |
| contents | We introduce BeaMsteerX (BMX), a novel mmWave hand hygiene gesture recognition technique that improves accuracy in longer ranges (1.5m). BMX steers a mmWave beam towards multiple directions around the subject, generating multiple views of the gesture that are then intelligently combined using deep learning to enhance gesture classification. We evaluated BMX using off-the-shelf mmWave radars and collected a total of 7,200 hand hygiene gesture data from 10 subjects performing a six-step hand-rubbing procedure, as recommended by the World Health Organization, using sanitizer, at 1.5m -- over five times longer than in prior works. BMX outperforms state-of-the-art approaches by 31--43% and achieves 91% accuracy at boresight by combining only two beams, demonstrating superior gesture classification in low SNR scenarios. BMX maintained its effectiveness even when the subject was positioned 30 degrees away from the boresight, exhibiting a modest 5% drop in accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16764 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving mmWave based Hand Hygiene Monitoring through Beam Steering and Combining Techniques Nirmal, Isura Hu, Wen Hassan, Mahbub Aboutanios, Elias Khamis, Abdelwahed Human-Computer Interaction We introduce BeaMsteerX (BMX), a novel mmWave hand hygiene gesture recognition technique that improves accuracy in longer ranges (1.5m). BMX steers a mmWave beam towards multiple directions around the subject, generating multiple views of the gesture that are then intelligently combined using deep learning to enhance gesture classification. We evaluated BMX using off-the-shelf mmWave radars and collected a total of 7,200 hand hygiene gesture data from 10 subjects performing a six-step hand-rubbing procedure, as recommended by the World Health Organization, using sanitizer, at 1.5m -- over five times longer than in prior works. BMX outperforms state-of-the-art approaches by 31--43% and achieves 91% accuracy at boresight by combining only two beams, demonstrating superior gesture classification in low SNR scenarios. BMX maintained its effectiveness even when the subject was positioned 30 degrees away from the boresight, exhibiting a modest 5% drop in accuracy. |
| title | Improving mmWave based Hand Hygiene Monitoring through Beam Steering and Combining Techniques |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.16764 |