Joint Human Orientation-Activity Recognition Using WiFi Signals for Human-Machine Interaction

TL;DR

This paper introduces a novel approach for jointly recognizing human orientation and activity using WiFi signals, enhancing human-machine interaction capabilities in indoor environments.

Contribution

It proposes a new data collection setup and machine learning models for simultaneous orientation and activity recognition with WiFi signals from one or multiple access points.

Findings

High accuracy in joint orientation-activity recognition

Feasibility demonstrated in indoor environments

Applicable with single or multiple access points

Abstract

WiFi sensing is an important part of the new WiFi 802.11bf standard, which can detect motion and measure distances. In recent years, some machine learning methods have been proposed for human activity recognition from WiFi signals. However, to the best of our knowledge, none of these methods have explored orientation prediction of the user using WiFi signals. Orientation prediction is particularly critical for human-machine interaction in an environment with multiple smart devices. In this paper, we propose a data collection setup and machine learning models for joint human orientation and activity recognition using WiFi signals from a single access point (AP) or multiple APs. The results show feasibility of joint orientation-activity recognition in an indoor environment with a high accuracy.