Radar classifications of consecutive and contiguous human gross‐motor activities
| Autor: | Ronny G. Guendel, Moeness G. Amin |
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| Rok vydání: | 2020 |
| Předmět: |
Offset (computer science)
Contextual image classification Radon transform business.industry Signal reconstruction Computer science Doppler radar 020206 networking & telecommunications Pattern recognition 02 engineering and technology law.invention Time–frequency analysis law 0202 electrical engineering electronic engineering information engineering Artificial intelligence Electrical and Electronic Engineering Radar business Classifier (UML) |
| Zdroj: | IET Radar, Sonar & Navigation. 14:1417-1429 |
| ISSN: | 1751-8792 |
| Popis: | The authors consider radar classifications of activities of daily living, which can prove beneficial in fall detection, analysis of daily routines, and discerning physical and cognitive human conditions. They focus on contiguous motion classifications, which follow and commensurate with the human ethogram of possible motion sequences. Contiguous motions can be closely connected with no clear time gap separations. In the proposed approach, they utilise the Radon transform applied to the radar range-map to detect the translation motion, whereas an energy detector is used to provide the onset and offset times of in-place motions, such as sitting down and standing up. It is shown that motion classifications give different results when performed forward and backward in time. The number of classes, thereby classification rates, considered by a classifier, is made varying depending on the current motion state and the possible transitioning activities in and out of the state. Two different examples are given to delineate the performance of the proposed approach under typical sequences of human motions. |
| Databáze: | OpenAIRE |
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