FlowAR: une plateforme uniformisée pour la reconnaissance des activités humaines à partir de capteurs binaires
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916612044488704 |
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| author | Ncibi, Ali Bouganim, Luc Pucheral, Philippe |
| author_facet | Ncibi, Ali Bouganim, Luc Pucheral, Philippe |
| contents | This demo showcases a platform for developing human activity recognition (AR) systems, focusing on daily activities using sensor data, like binary sensors. With a data-driven approach, this platform, named FlowAR, features a three-step pipeline (flow): data cleaning, segmentation, and personalized classification. Its modularity allows flexibility to test methods, datasets, and ensure rigorous evaluations. A concrete use case demonstrates its effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09067 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FlowAR: une plateforme uniformisée pour la reconnaissance des activités humaines à partir de capteurs binaires Ncibi, Ali Bouganim, Luc Pucheral, Philippe Machine Learning This demo showcases a platform for developing human activity recognition (AR) systems, focusing on daily activities using sensor data, like binary sensors. With a data-driven approach, this platform, named FlowAR, features a three-step pipeline (flow): data cleaning, segmentation, and personalized classification. Its modularity allows flexibility to test methods, datasets, and ensure rigorous evaluations. A concrete use case demonstrates its effectiveness. |
| title | FlowAR: une plateforme uniformisée pour la reconnaissance des activités humaines à partir de capteurs binaires |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.09067 |