WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Intelligence artificielle: Les défis actuels et l'action d'Inria - Livre blanc Inria

Livre blanc Inria N°01International audienceInria white papers look at major current challenges in informatics and mathematics and show actions conducted by our project-teams to address these challenges. This document is the first produced by the Strategic Technology Monitoring & Prospective Studies Unit. Thanks to a reactive observation system, this unit plays a lead role in supporting Inria to develop its strategic and scientific orientations. It also enables the institute to anticipate the impact of digital sciences on all social and economic domains. It has been coordinated by Bertrand Braunschweig with contributions from 45 researchers from Inria and from our partners. Special thanks to Peter Sturm for his precise and complete review.Les livres blancs d’Inria examinent les grands défis actuels du numérique et présentent les actions menées par noséquipes-projets pour résoudre ces défis. Ce document est le premier produit par la cellule veille et prospective d’Inria. Cette unité, par l’attention qu’elle porte aux évolutions scientifiques et technologiques, doit jouer un rôle majeur dans la détermination des orientations stratégiques et scientifiques d’Inria. Elle doit également permettre à l’Institut d’anticiper l’impact des sciences du numérique dans tous les domaines sociaux et économiques. Ce livre blanc a été coordonné par Bertrand Braunschweig avec des contributions de 45 chercheurs d’Inria et de ses partenaires. Un grand merci à Peter Sturm pour sa relecture précise et complète. Merci également au service STIP du centre de Saclay – Île-de-France pour la correction finale de la version française