# WiOpen: A Robust Wi-Fi-based Open-set Gesture Recognition Framework

arXiv: https://arxiv.org/abs/2402.00822 · HTML: https://mlsanity.com/paper/2402.00822/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 15 of 15)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [SelfReg: Self-supervised Contrastive Regularization for Domain Generalization](https://mlsanity.com/paper/2104.09841/) (2021)
- [Adversarial Reciprocal Points Learning for Open Set Recognition](https://mlsanity.com/paper/2103.00953/) (2021)
- [Learning Placeholders for Open-Set Recognition](https://mlsanity.com/paper/2103.15086/) (2021)
- [Learning Open Set Network with Discriminative Reciprocal Points](https://mlsanity.com/paper/2011.00178/) (2020)
- [Conditional Gaussian Distribution Learning for Open Set Recognition](https://mlsanity.com/paper/2003.08823/) (2020)
- [Improving Generalization via Scalable Neighborhood Component Analysis](https://mlsanity.com/paper/1808.04699/) (2018)
- [Class-Specific Semantic Reconstruction for Open Set Recognition](https://mlsanity.com/paper/2207.02158/) (2022)
- [Joint Activity Recognition and Indoor Localization with WiFi Fingerprints](https://mlsanity.com/paper/1904.04964/) (2019)
- [WiGest: A Ubiquitous WiFi-based Gesture Recognition System](https://mlsanity.com/paper/1501.04301/) (2015)
- [ReSup: Reliable Label Noise Suppression for Facial Expression Recognition](https://mlsanity.com/paper/2305.17895/) (2023)
- [Wital: A COTS WiFi Devices Based Vital Signs Monitoring System Using NLOS Sensing Model](https://mlsanity.com/paper/2305.14490/) (2023)

## Built on by (most cited first, 16 of 16)

- [A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects](https://mlsanity.com/paper/2503.08008/) (2025)
- [Wi-CBR: Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition](https://mlsanity.com/paper/2506.11616/) (2025)
- [Verifiable Fine-Tuning for LLMs: Zero-Knowledge Training Proofs Bound to Data Provenance and Policy](https://mlsanity.com/paper/2510.16830/) (2025)
- [Breaking Coordinate Overfitting: Geometry-Aware WiFi Sensing for Cross-Layout 3D Pose Estimation](https://mlsanity.com/paper/2601.12252/) (2026)
- [Inducing Causal World Models in LLMs for Zero-Shot Physical Reasoning](https://mlsanity.com/paper/2507.19855/) (2025)
- [DySK-Attn: A Framework for Efficient, Real-Time Knowledge Updating in Large Language Models via Dynamic Sparse Knowledge Attention](https://mlsanity.com/paper/2508.07185/) (2025)
- [Trusted Uncertainty in Large Language Models: A Unified Framework for Confidence Calibration and Risk-Controlled Refusal](https://mlsanity.com/paper/2509.01455/) (2025)
- [Beyond Physical Labels: Redefining Domains for Robust WiFi-based Gesture Recognition](https://mlsanity.com/paper/2601.03825/) (2026)
- [Millimeter-Wave Gesture Recognition in ISAC: Does Reducing Sensing Airtime Hamper Accuracy?](https://mlsanity.com/paper/2601.10733/) (2026)
- [Computational Economics in Large Language Models: Exploring Model Behavior and Incentive Design under Resource Constraints](https://mlsanity.com/paper/2508.10426/) (2025)
- [MICACL: Multi-Instance Category-Aware Contrastive Learning for Long-Tailed Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2509.04344/) (2025)
- [Generalizing WiFi Gesture Recognition via Large-Model-Aware Semantic Distillation and Alignment](https://mlsanity.com/paper/2510.13390/) (2025)
- [Socialized Learning and Emergent Behaviors in Multi-Agent Systems based on Multimodal Large Language Models](https://mlsanity.com/paper/2510.18515/) (2025)
- [CoSense-LLM: Semantics at the Edge with Cost- and Uncertainty-Aware Cloud-Edge Cooperation](https://mlsanity.com/paper/2510.19670/) (2025)
- [ZK-SenseLM: Verifiable Large-Model Wireless Sensing with Selective Abstention and Zero-Knowledge Attestation](https://mlsanity.com/paper/2510.25677/) (2025)
- [Parameter-Efficient and Personalized Federated Training of Generative Models at the Edge](https://mlsanity.com/paper/2511.11585/) (2025)

## Abstract (arXiv)

Recent years have witnessed a growing interest in Wi-Fi-based gesture recognition. However, existing works have predominantly focused on closed-set paradigms, where all testing gestures are predefined during training. This poses a significant challenge in real-world applications, as unseen gestures might be misclassified as known classes during testing. To address this issue, we propose WiOpen, a robust Wi-Fi-based Open-Set Gesture Recognition (OSGR) framework. Implementing OSGR requires addressing challenges caused by the unique uncertainty in Wi-Fi sensing. This uncertainty, resulting from noise and domains, leads to widely scattered and irregular data distributions in collected Wi-Fi sensing data. Consequently, data ambiguity between classes and challenges in defining appropriate decision boundaries to identify unknowns arise. To tackle these challenges, WiOpen adopts a two-fold approach to eliminate uncertainty and define precise decision boundaries. Initially, it addresses uncertainty induced by noise during data preprocessing by utilizing the CSI ratio. Next, it designs the OSGR network based on an uncertainty quantification method. Throughout the learning process, this network effectively mitigates uncertainty stemming from domains. Ultimately, the network leverages relationships among samples' neighbors to dynamically define open-set decision boundaries, successfully realizing OSGR. Comprehensive experiments on publicly accessible datasets confirm WiOpen's effectiveness. Notably, WiOpen also demonstrates superiority in cross-domain tasks when compared to state-of-the-art approaches.