Uni-Fi: Integrated Multi-Task Wi-Fi Sensing

TL;DR

Uni-Fi is an extensible framework that unifies multi-task Wi-Fi sensing, improving performance in localization, activity recognition, and presence detection through a scalable, integrated pipeline.

Contribution

It introduces a unified theoretical framework and a scalable sensing pipeline for multi-task Wi-Fi sensing integration, addressing key challenges in the field.

Findings

Median localization error of 0.54 meters

98.34% accuracy in activity classification

98.57% accuracy in presence detection

Abstract

Wi-Fi sensing technology enables non-intrusive, continuous monitoring of user locations and activities, which supports diverse smart home applications. Since different sensing tasks exhibit contextual relationships, their integration can enhance individual module performance. However, integrating sensing tasks across different studies faces challenges due to the absence of: 1) a unified architecture that captures the fundamental nature shared across diverse sensing tasks, and 2) an extensible pipeline that accommodates future sensing methodologies. This paper presents UNI-FI, an extensible framework for multi-task Wi-Fi sensing integration. This paper makes the following contributions: 1) we propose a unified theoretical framework that reveals fundamental differences between single-task and multi-task sensing; 2) we develop a scalable sensing pipeline that automatically generates a multi-task sensing solver, enabling seamless integration of multiple sensing models. Experimental results show that UNI-FI achieves robust performance across tasks, with a median localization error of approximately 0.54 m, 98.34% accuracy for activity classification, and 98.57% accuracy for presence detection.