BReML: A Breathing Rate Estimator Using Wi-Fi Channel State Information and Machine Learning
| Autor: | Jorge E. Ibarra-Esquer, Felix F. Gonzalez-Navarro, Albany Armenta-Garcia, Brenda L. Flores-Rios, Jesus Caro-Gutierrez |
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| Rok vydání: | 2021 |
| Předmět: |
Signal processing
Computer science business.industry Physics::Medical Physics Fast Fourier transform Feature extraction Estimator Machine learning computer.software_genre Support vector machine Channel state information Feature (computer vision) Frequency domain Artificial intelligence business computer |
| Zdroj: | 2021 Mexican International Conference on Computer Science (ENC). |
| Popis: | Breathing rate is one vital sign that might help identifying pathological conditions by its monitoring. This paper presents a novel breathing rate estimator that combines conventional Channel State Information (CSI) approaches with machine learning classifiers to provide a breathing rate estimation in a controlled environment. Results show that by extracting time and frequency domain features of CSI amplitude, as well as using a first estimation obtained with Fast Fourier Transform as a feature for feeding machine learning classifiers, the breathing rate can be accurately estimated. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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