| Autor: |
Guillermo Diaz, Bo Tan, Iker Sobron, Iñaki Eizmendi, Iratxe Landa, Manuel Velez |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Sensors, Vol 24, Iss 19, p 6388 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1424-8220 |
| DOI: |
10.3390/s24196388 |
| Popis: |
This paper investigates the feasibility of cross-domain recognition for human activities captured using low-resolution 8 × 8 infrared sensors in indoor environments. To achieve this, a novel prototype recurrent convolutional network (PRCN) was evaluated using a few-shot learning strategy, classifying up to eleven activity classes in scenarios where one or two individuals engaged in daily tasks. The model was tested on two independent datasets, with real-world measurements. Initially, three different networks were compared as feature extractors within the prototype network. Following this, a cross-domain evaluation was conducted between the real datasets. The results demonstrated the model’s effectiveness, showing that it performed well regardless of the diversity of samples in the training dataset. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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