# Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned

arXiv: https://arxiv.org/abs/2306.17062 · HTML: https://mlsanity.com/paper/2306.17062/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 51 references that are in the map.
- First posted on arXiv 29 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach](https://mlsanity.com/paper/1603.07080/) (2016)
- [SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points](https://mlsanity.com/paper/2103.09924/) (2021)
- [Joint Activity Recognition and Indoor Localization with WiFi Fingerprints](https://mlsanity.com/paper/1904.04964/) (2019)
- [From Point to Space: 3D Moving Human Pose Estimation Using Commodity WiFi](https://mlsanity.com/paper/2012.14066/) (2020)
- [WiFi-based Real-time Breathing and Heart Rate Monitoring during Sleep](https://mlsanity.com/paper/1908.05108/) (2019)
- [CSI-Based Localization with CNNs Exploiting Phase Information](https://mlsanity.com/paper/2101.08983/) (2021)
- [Quantum Transfer Learning for Wi-Fi Sensing](https://mlsanity.com/paper/2205.08590/) (2022)
- [Crowd Counting Through Walls Using WiFi](https://mlsanity.com/paper/1711.05837/) (2017)
- [Mobile XR over 5G: A way forward with mmWaves and Edge](https://mlsanity.com/paper/1905.04599/) (2019)
- [Hand gesture recognition using 802.11ad mmWave sensor in the mobile device](https://mlsanity.com/paper/2211.07090/) (2022)

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

- [CSI4Free: GAN-Augmented mmWave CSI for Improved Pose Classification](https://mlsanity.com/paper/2406.18684/) (2024)
- [Millimeter-Wave Gesture Recognition in ISAC: Does Reducing Sensing Airtime Hamper Accuracy?](https://mlsanity.com/paper/2601.10733/) (2026)
- [mmHSense: Multi-Modal and Distributed mmWave ISAC Datasets for Human Sensing](https://mlsanity.com/paper/2509.21396/) (2025)
- [Beyond Sub-6 GHz: Leveraging mmWave Wi-Fi for Gait-Based Person Identification](https://mlsanity.com/paper/2510.08160/) (2025)

## Abstract (arXiv)

In recent years, channel state information (CSI) at sub-6 GHz has been widely exploited for Wi-Fi sensing, particularly for activity and gesture recognition. In this work, we instead explore mmWave (60 GHz) Wi-Fi signals for gesture recognition/pose estimation. Our focus is on the mmWave Wi-Fi signals so that they can be used not only for high data rate communication but also for improved sensing e.g., for extended reality (XR) applications. For this reason, we extract spatial beam signal-to-noise ratios (SNRs) from the periodic beam training employed by IEEE 802.11ad devices. We consider a set of 10 gestures/poses motivated by XR applications. We conduct experiments in two environments and with three people.As a comparison, we also collect CSI from IEEE 802.11ac devices. To extract features from the CSI and the beam SNR, we leverage a deep neural network (DNN). The DNN classifier achieves promising results on the beam SNR task with state-of-the-art 96.7% accuracy in a single environment, even with a limited dataset. We also investigate the robustness of the beam SNR against CSI across different environments. Our experiments reveal that features from the CSI generalize without additional re-training, while those from beam SNRs do not. Therefore, re-training is required in the latter case.