Behaviorally Diverse Data Augmentation for RFID-Based Gesture Recognition

LIU Jianwei; CHEN Jiatong; YAO Xinwei; HAN Jinsong

doi:10.12263/DZXB.20251202

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Behaviorally Diverse Data Augmentation for RFID-Based Gesture Recognition

RF Computing | 更新时间：2026-07-29

- Behaviorally Diverse Data Augmentation for RFID-Based Gesture Recognition
- ACTA ELECTRONICA SINICA Vol. 54, Issue 6, Pages: 131-143(2026)
- 作者机构：
  
  1.浙江大学计算机科学与技术学院，浙江杭州 310027
  2.浙江工业大学先进技术研究院，浙江杭州 310014
  3.浙江工业大学计算机科学与技术学院，浙江杭州 310014
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62372400);China Postdoctoral Science Foundation(2025M781520);Postdoctoral Fellowship Program of CPSF(GZC20241488);"Pioneer" and "Leading Goose" Research and Development Program of Zhejiang(2025C01054;2025C01212);Postdoctoral Research Excellence Funding Project of Zhejiang Province(ZJ2025024)
- DOI：10.12263/DZXB.20251202
  CLC： TP391;
- Received：02 January 2026，
  
  Accepted：31 January 2026，
  
  Online First：23 April 2026，
  
  Published：25 June 2026
- 稿件说明：
移动端阅览
刘建伟, 陈佳彤, 姚信威, 等. 基于行为多样性数据增强的RFID手势识别技术[J]. 电子学报, 2026, 54(06): 131-143.

LIU Jianwei, CHEN Jiatong, YAO Xinwei, et al. Behaviorally Diverse Data Augmentation for RFID-Based Gesture Recognition[J]. Acta Electronica Sinica, 2026, 54(06): 131-143.
刘建伟, 陈佳彤, 姚信威, 等. 基于行为多样性数据增强的RFID手势识别技术[J]. 电子学报, 2026, 54(06): 131-143. DOI：10.12263/DZXB.20251202

LIU Jianwei, CHEN Jiatong, YAO Xinwei, et al. Behaviorally Diverse Data Augmentation for RFID-Based Gesture Recognition[J]. Acta Electronica Sinica, 2026, 54(06): 131-143. DOI：10.12263/DZXB.20251202

摘要

手势识别是人机交互中的关键支撑技术，已广泛应用于智能医疗、虚拟现实和智能家居等场景。相较于依赖视觉信息的方法，基于射频识别（Radio Frequency IDentification，RFID）的手势识别具有非接触、低成本和隐私友好等优势，展现出良好的应用前景。然而，现有RFID手势识别模型的性能高度依赖于训练数据的规模与多样性，而射频数据的采集成本较高、数据分布受限，从而制约了模型在未知样本上的泛化能力。为缓解上述问题，数据增强被视为提升识别性能的有效手段。然而，现有数据增强方法往往忽略了人类行为本身的固有差异性，导致合成样本在分布上的多样性不足。事实上，即便属于同一手势类别，不同样本之间仍普遍存在显著差异，其根本原因在于人类难以以完全一致的方式重复执行同一动作。在手势识别场景中，这种行为差异在直觉上主要源于手部相对于传感器的距离变化以及运动速度的差异。本文通过建立信号传播模型并结合先验实验，对这一直觉假设进行了系统验证。基于上述分析，本文提出一种面向行为多样性的RFID数据增强方法。该方法首先从射频信号传播机理出发，将手势的距离与速度信息显式编码为条件输入。随后，本文设计了一种新颖的条件扩散模型，在生成过程中沿距离和速度对应的条件方向进行受控偏移，从而合成在物理一致性约束下、分布范围更广的高质量样本。通过在手势识别模型的训练集中引入富含行为多样性的合成数据，可以有效提升模型对未知样本的泛化能力以及手势识别的准确率。本文在真实应用场景中搭建了RFID手势识别原型系统，并开展了系统性的物理实验。实验结果表明，在10类手势识别任务中，所提出的方法相较于基线模型的识别准确率提升超过20%，且显著优于现有的数据增强策略。此外，该方法的数据增强效果不会随着感知任务规模的扩大而明显衰减。在身份识别任务上的实验结果进一步表明，本文提出的方法同样适用于其他人机交互应用场景。

Abstract

Gesture recognition is a fundamental enabling technology for humancomputer interaction and has been widely adopted in applications such as smart healthcare

virtual reality

and smart homes. Compared with vision-based approaches

radio frequency identification (RFID)-based gesture recognition offers several distinctive advantages

including contact-free operation

low deployment cost

and privacy preservation

making it a promising solution for practical deployments. However

the performance of existing RFID-based gesture recognition models heavily depends on the scale and diversity of training data. In practice

the acquisition of RF data is costly and the resulting data distributions are inherently limited

which significantly constrains the generalization capability of models to unseen samples. To address this challenge

data augmentation has been widely regarded as an effective means to enhance recognition performance. Nevertheless

most existing augmentation methods overlook the intrinsic variability of human behavior

leading to synthesized samples with insufficient distributional diversity. In fact

even within the same gesture category

substantial variations commonly exist across different instances

primarily because humans cannot reproduce the same action in a perfectly identical manner. In gesture recognition scenarios

such behavioral variability intuitively arises from changes in the hand’s distance relative to the sensor as well as variations in motion speed. This intuitive hypothesis is systematically validated in this work through the establishment of a signal propagation model combined with preliminary experimental analysis. Motivated by these observations

we propose a behavior diversity-aware RFID data augmentation framework. Specifically

grounded in the physical principles of RF signal propagation

the proposed method explicitly encodes gesture-related distance and speed as conditional inputs. A novel conditional diffusion model is then designed to perform controlled shifts along the distance- and speed-conditioned directions during the generation process

thereby synthesizing high-quality samples that are both physically consistent and distributed over a broader behavioral diversity space. By introducing synthetic data enriched with behavioral diversity into the training set of gesture recognition models

the generalization capability to unseen samples and the recognition accuracy can be significantly improved. We implement an RFID based gesture recognition prototype system in real world scenarios and conduct systematic physical experiments. Experimental results show that

in a ten-class gesture recognition task

the proposed method improves recognition accuracy by more than 20% compared with baseline models and significantly outperforms existing data augmentation strategies. In addition

the effectiveness of the proposed data augmentation approach does not degrade noticeably as the scale of the sensing task increases. Experimental results on identity recognition further demonstrate that the proposed method can be effectively applied to other human computer interaction applications.

关键词

Keywords

references

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