# Millimeter-Wave Gesture Recognition in ISAC: Does Reducing Sensing Airtime Hamper Accuracy?

arXiv: https://arxiv.org/abs/2601.10733 · HTML: https://mlsanity.com/paper/2601.10733/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study](https://mlsanity.com/paper/2002.00772/) (2020)
- [WiOpen: A Robust Wi-Fi-based Open-set Gesture Recognition Framework](https://mlsanity.com/paper/2402.00822/) (2024)
- [Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned](https://mlsanity.com/paper/2306.17062/) (2023)
- [Fundamental Trade-Offs in Monostatic ISAC: A Holistic Investigation Towards 6G](https://mlsanity.com/paper/2401.18011/) (2024)
- [Toward Interactive Multi-User Extended Reality Using Millimeter-Wave Networking](https://mlsanity.com/paper/2501.08100/) (2025)
- [Reshaping the ISAC Tradeoff Under OFDM Signaling: A Probabilistic Constellation Shaping Approach](https://mlsanity.com/paper/2312.15941/) (2023)

## Built on by (most cited first, 1 of 1)

- [Integrated Sensing and Communications for Real-time Avatar Control in XR over 5G](https://mlsanity.com/paper/2606.23771/) (2026)

## Abstract (arXiv)

Most Integrated Sensing and Communications (ISAC) systems require dividing airtime across their two modes. However, the specific impact of this decision on sensing performance remains unclear and underexplored. In this paper, we therefore investigate the impact on a gesture recognition system using a Millimeter-Wave (mmWave) ISAC system. With our dataset of power per beam pair gathered with two mmWave devices performing constant beam sweeps while test subjects performed distinct gestures, we train a gesture classifier using Convolutional Neural Networks. We then subsample these measurements, emulating reduced sensing airtime, showing that a sensing airtime of 25 % only reduces classification accuracy by 0.15 percentage points from full-time sensing. Alongside this high-quality sensing at low airtime, mmWave systems are known to provide extremely high data throughputs, making mmWave ISAC a prime enabler for applications such as truly wireless Extended Reality.