FlowAR: une plateforme uniformis'ee pour la reconnaissance des activit'es humaines `a partir de capteurs binaires

2025-02-13

AI Summary

This work addresses the lack of a unified, reproducible evaluation platform for human activity recognition (AR) using binary sensor data. Methodologically, we propose the first modular end-to-end AR pipeline, comprising a data-driven three-stage framework: robust data cleaning, sliding-window-based adaptive temporal segmentation, and lightweight personalized classification (using XGBoost or LSTM variants), enabling plug-and-play substitution of methods, datasets, and evaluation protocols. Our key contribution is the first modular AR architecture specifically designed for binary sensing—ensuring full pipeline reproducibility, customization, and rigorous evaluation. Extensive validation across multiple public datasets demonstrates significant improvements in cross-user accuracy and generalization robustness. The platform establishes a standardized experimental baseline for AR research and supports rapid prototyping and deployment.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• human activity recognition
• binary sensor data
• modular platform development

Innovation

• Modular activity recognition platform
• Three-step data processing pipeline
• Personalized classification for daily activities