# Joint Activity Recognition and Indoor Localization with WiFi Fingerprints

## TL;DR

This paper introduces a deep learning framework that jointly recognizes human activities and localizes indoor positions using WiFi CSI fingerprints, demonstrating effective performance with real-world data.

## Contribution

The work presents a novel dual-task CNN model for simultaneous activity recognition and indoor localization using WiFi CSI fingerprints, with publicly available data and code.

## Key findings

- Achieves high accuracy in joint activity recognition and localization
- Uses standard WiFi protocol and collects extensive CSI data
- Demonstrates effectiveness through experimental results

## Abstract

Recent years have witnessed the rapid development in the research topic of WiFi sensing that automatically senses human with commercial WiFi devices. This work falls into two major categories, i.e., the activity recognition and the indoor localization. The former work utilizes WiFi devices to recognize human daily activities such as smoking, walking, and dancing. The latter one, indoor localization, can be used for indoor navigation, location-based services, and through-wall surveillance. The key rationale behind this type of work is that people behaviors can influence the WiFi signal propagation and introduce specific patterns into WiFi signals, called WiFi fingerprints, which can be further explored to identify human activities and locations. In this paper, we propose a novel deep learning framework for joint activity recognition and indoor localization task using WiFi Channel State Information~(CSI) fingerprints. More precisely, we develop a system running standard IEEE 802.11n WiFi protocol, and collect more than 1400 CSI fingerprints on 6 activities at 16 indoor locations. Then we propose a dual-task convolutional neural network with 1-dimensional convolutional layers for the joint task of activity recognition and indoor localization. Experimental results and ablation study show that our approach achieves good performances in this joint WiFi sensing task. Data and code have been made publicly available at https://github.com/geekfeiw/apl.