FlowAR: une plateforme uniformisée pour la reconnaissance des activités humaines à partir de capteurs binaires
This work addresses the problem of human activity recognition for researchers and developers in the field of ambient assisted living and smart homes, providing an incremental solution.
The authors tackled the problem of human activity recognition using binary sensor data and developed a platform called FlowAR, which features a three-step pipeline for data cleaning, segmentation, and personalized classification. The platform's effectiveness is demonstrated through a concrete use case.
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.