Summary of Non-contact Breath Rate Classification Using Svm Model and Mmwave Radar Sensor Data, by Mohammad Wassaf Ali et al.
Non-Contact Breath Rate Classification Using SVM Model and mmWave Radar Sensor Data
by Mohammad Wassaf Ali, Ayushi Gupta, Mujeev Khan, Mohd Wajid
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed system combines FMCW radar technology with a machine learning model to differentiate between normal and abnormal breath rates. The FMCW radar collects non-contact data based on breath rates, which is then classified using various support vector machine (SVM) kernels. The model demonstrates good accuracy in breath rate classification through prolonged experiments, achieving an accuracy of 95% with the quadratic polynomial kernel. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new system that can detect abnormal breathing patterns without touching you. They used radar technology and special computer algorithms to analyze your breathing rate. The system is very accurate, getting it right 95% of the time when using one specific type of algorithm. This could be helpful for doctors or hospitals who need to monitor people’s breathing in real-time. |
Keywords
» Artificial intelligence » Classification » Machine learning » Support vector machine
