Single channel continuous wave doppler radar for differentiating types of human activity
| Autor: | Miodrag Bolic, Sreeraman Rajan, Zachary Baird, Julio J. Valdés |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Channel (digital image)
Computer science business.industry Feature extraction Doppler radar 020206 networking & telecommunications Pattern recognition 02 engineering and technology Signal law.invention genetic algorithms feature selection law extreme learning machines GammaTest 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing multilayer perceptron Artificial intelligence Radar business random forest |
| Zdroj: | IJCNN |
| Popis: | In real life applications, it is crucial to monitor the different kinds of human activity without interfering with their regular occupations. Contactless physiological monitoring using radars is a valuable tool, but typically it is performed when the human subjects are immobile. This paper analyzes single channel Continuous Wave (CW) Doppler radar signals in relation to three levels of human activity: i) Sedentary and still, ii) Sedentary and moving and iii) Walking. A combination of computational intelligence techniques (GammaTest, neural networks, random forest and genetic algorithms) was used for assessing the predictive ability of 43 features derived from the radar return signal, as well as of subsets of them, which were composed of highly predictive attributes. It is shown that with about one half the number of attributes it is possible to achieve high levels of classification accuracy, in some cases improving false negative ratios. While several attributes were completely irrelevant and noisy, others were required by discriminating each of the classes. There are attributes required by certain classes in particular and there are others associated to the distinction of classes with subtle differences. 2018 International Joint Conference on Neural Networks (IJCNN), July 7-13, 2018, Rio de Janeiro, Brazil Series: Proceedings of International Joint Conference on Neural Networks |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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