基于CSI小样本学习的场景鲁棒性跌倒检测系统

曾钰婷; 毕宿志; 郑莉莉; 林晓辉; 王晖

doi:10.11959/j.issn.2096-3750.2023.00339

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基于CSI小样本学习的场景鲁棒性跌倒检测系统

理论与技术 | 更新时间：2024-08-16

- 基于CSI小样本学习的场景鲁棒性跌倒检测系统
- Scenario-adaptive wireless fall detection system based on few-shot learning
- 物联网学报 2023年7卷第2期页码：118-132
- 作者机构：
  
  1. 深圳大学电子与信息工程学院，广东深圳 518060
  2. 鹏城实验室宽带通信研究部，广东深圳 518066
  3. 深圳信息职业技术学院，广东深圳 518172
- 作者简介：
  
  [ "曾钰婷（1997- ），女，深圳大学电子与信息工程学院硕士生，主要研究方向为无线感知" ]
  [ "毕宿志（1987- ），男，博士，深圳大学电子与信息工程学院副教授，主要研究方向为无线通信网络资源管理、移动计算与无线感知" ]
  [ "郑莉莉（1990- ），女，博士，深圳大学电子与信息工程学院博士后，主要研究方向为无线感知" ]
  [ "林晓辉（1975- ），男，博士，深圳大学电子与信息工程学院教授，主要研究方向为无人机通信网络的优化设计" ]
  [ "王晖（1969- ），男，博士，深圳信息职业技术学院教授，主要研究方向为无线通信与信号处理" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(62271325);鹏城实验室宽带通信研究部重点研究计划项目;The Major Key Project of PCL Department of Broadband Communication;广东省教育厅科技重点专项;The Key Project of Department of Education of Guangdong Province(2020ZDZX3050);广东省基础与应用基础研究基金资助项目;The Guangdong Basic and Applied Basic Research Foundation(2022A1515011219);广东省基础与应用基础研究基金资助项目;The Guangdong Basic and Applied Basic Research Foundation(2022A1515010973);深圳市科创委基础研究项目;The Shenzhen Science and Technology Program(20220810142637001);深圳市科创委基础研究项目;The Shenzhen Science and Technology Program(JCYJ20210324093011030);深圳市科创委基础研究项目;The Shenzhen Science and Technology Program(JCYJ20190808120415286);智慧城市物联网国家重点实验室（澳门大学）开放课题项目;The Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City (University of Macau)(SKL-IoTSC(UM)-2021-2023/ORPF/A03/2022)
- DOI：10.11959/j.issn.2096-3750.2023.00339
  中图分类号： TP391.7
- 纸质出版日期：2023-06-30，
  
  网络出版日期：2023-06，
- 稿件说明：
移动端阅览
曾钰婷, 毕宿志, 郑莉莉, 等. 基于CSI小样本学习的场景鲁棒性跌倒检测系统[J]. 物联网学报, 2023,7(2):118-132.

YUTING ZENG, SUZHI BI, LILI ZHENG, et al. Scenario-adaptive wireless fall detection system based on few-shot learning. [J]. Chinese journal on internet of things, 2023, 7(2): 118-132.
曾钰婷, 毕宿志, 郑莉莉, 等. 基于CSI小样本学习的场景鲁棒性跌倒检测系统[J]. 物联网学报, 2023,7(2):118-132. DOI： 10.11959/j.issn.2096-3750.2023.00339.

YUTING ZENG, SUZHI BI, LILI ZHENG, et al. Scenario-adaptive wireless fall detection system based on few-shot learning. [J]. Chinese journal on internet of things, 2023, 7(2): 118-132. DOI： 10.11959/j.issn.2096-3750.2023.00339.

摘要

采用小样本学习技术设计了基于CSI的场景鲁棒性跌倒检测系统（FDFL

fall detection system based on few-shot learning）。现有基于Wi-Fi无线信道状态信息（CSI

channel state information）的跌倒检测方法跨场景应用性能退化明显，通常需要在每个应用场景采集并标记大量的CSI样本，给大规模部署造成极高的成本。为此，引入了小样本学习的方法，可以在陌生场景标注样本数量不足的情况下仍然保持高准确率的跌倒检测性能。所提FDFL 主要分为源域的元训练和目标域的元学习两个阶段。源域的元训练阶段包含数据预处理和分类训练两个部分，数据预处理阶段将采集的原始CSI幅度和相位数据进行去噪、分段等操作；分类训练阶段利用大量处理好的源域数据样本训练一个基于卷积神经网络的CSI特征提取器。在目标域的元学习阶段，基于元训练模块中训练的特征提取器对目标域中采样的少量标注样本进行有效的特征提取，进而训练生成一个轻量型机器学习分类器对跨场景下的跌倒行为进行检测。通过多个不同场景下的实验，FDFL在只需要目标域少量样本下即可以实现对跌倒、坐着、步行、坐下的四分类任务达到95.52%的平均识别准确率，并且对测试环境、人员目标、设备位置等因素的变化保持鲁棒的检测准确性。

Abstract

A scenario robust fall detection system based on few-shot learning (FDFL) in wireless environment was designed.The performance of existing fall detection methods based on Wi-Fi channel state information (CSI) degrades significantly across scenarios

which requires collecting and marking a large number of CSI samples in each application scenario

resulting in high cost for large-scale deployment.Therefore

the method of few-shot learning was introduced

which can maintain the performance of fall detection with high accuracy when the number of annotated samples in unfa-miliar scenes is insufficient.The proposed FDFL was mainly divided into two stages

source domain meta-training and target domain meta-learning.The meta training stage of the source domain consists of two parts: data preprocessing and classification training.In the data preprocessing stage

the collected original CSI amplitude and phase data were denoised and segmented.In the classification training stage

a large number of processed source domain data samples were used to train a CSI feature extractor based on convolutional neural network.In the meta-learning stage of the target domain

the limited labeled data sampled in the target domain was effectively extracted based on the feature extractor trained in the meta-training module

and then a lightweight machine learning classifier was trained to detect the fall behavior under the cross-scene.Through several experiments in different scenarios

FDFL can achieve an average accuracy of 95.52% for the four classification tasks of falling

sitting

walking and sit down with only a small number of samples in the target domain

and maintain robust detection accuracy for changes in test environment

personnel target and equipment location.

关键词

无线感知跌倒检测CSI跨域检测小样本学习

Keywords

Wi-Fi sensingfall detectionCSIcross-domain detectionfew-shot learning

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