MULTI-FI: Enhancing Wi-Fi Sensing Accuracy via Multi-view and Context Fusion

Abstract

Wi-Fi sensing enables innovative applications in healthcare and surveillance by providing continuous, contactless monitoring through existing infrastructure. Moreover, exploiting information from different receivers within an environment (i.e., different views) and using it as input to Deep Learning (DL) models facilitates sophisticated use cases. Previous works have demonstrated that this approach, also known as collaborative sensing, increases sensing coverage and significantly boosts accuracy and robustness. However, existing DL models for collaborative Wi-Fi sensing exhibit two critical limitations: (1) They do not consider the optimal fusion point for data from different receivers; (2) They assign equal importance to all receivers, irrespective of their sensing capacity. In this paper, we address the above gaps by proposing MULTI-FI, a novel model enhancement framework consisting of two modules. The first one benchmarks the appropriate fusion strategy, given the charac teristics of the sensing scenario. By leveraging physical context information like location and orientation and the appropriate fusion strategy, the second module instruments a model re-design strategy. Our validation of MULTI-FI spans across several SoTA DL models, three real-world datasets and two applications, and shows improvements over the original model version in every case, with average accuracy gains of 8.2% and up to 29.6%.