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

arXiv: https://arxiv.org/abs/2210.05078 · HTML: https://mlsanity.com/paper/2210.05078/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2022.

## Built on by (most cited first, 4 of 4)

- [DoRF: Doppler Radiance Fields for Robust Human Activity Recognition Using Wi-Fi](https://mlsanity.com/paper/2507.12132/) (2025)
- [Hypertension Detection From High-Dimensional Representation of Photoplethysmogram Signals](https://mlsanity.com/paper/2308.02425/) (2023)
- [Doppler Radiance Field-Guided Antenna Selection for Improved Generalization in Multi-Antenna Wi-Fi-based Human Activity Recognition](https://mlsanity.com/paper/2509.15129/) (2025)
- [DoRF++: Spherical Representation Learning over Doppler Radiance Fields for Robust Wi-Fi Sensing](https://mlsanity.com/paper/2608.08381/) (2026)

## Abstract (arXiv)

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.