Scenario-adaptive wireless fall detection system based on few-shot learning

Yuting ZENG; Suzhi BI; Lili ZHENG; Xiaohui LIN; Hui WANG

doi:10.11959/j.issn.2096-3750.2023.00339

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Scenario-adaptive wireless fall detection system based on few-shot learning

Theory and Technology | 更新时间：2024-08-16

- Scenario-adaptive wireless fall detection system based on few-shot learning
- Chinese Journal on Internet of Things Vol. 7, Issue 2, Pages: 118-132(2023)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2023.00339
  CLC： TP391.7
- Online First：2023-06，
  
  Published：30 June 2023
- 稿件说明：
移动端阅览
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：

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

Keywords

references

预防老年人跌倒健康教育核心信息 [J ] . 江苏卫生保健 , 2022 ( 2 ): 50 - 52 .

Prevention of falls in the elderly health education core information [J ] . Jiangsu Health Care , 2022 ( 2 ): 50 - 52 .

HU Y Q , ZHANG F , WU C S , et al . DeFall:environment-independent passive fall detection using Wi-Fi [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 11 ): 8515 - 8530 .

WANG Y X , WU K S , NI L M . WiFall:device-free fall detection by wireless networks [J ] . IEEE Transactions on Mobile Computing , 2017 , 16 ( 2 ): 581 - 594 .

ZHANG D Q , WANG H , WANG Y S , et al . Anti-fall:A non-intrusive and real-time fall detector leveraging CSI from commodity Wi-Fi devices [C ] // Proceedings of International Conference on Smart Homes and Health Telematics . Cham:Springer , 2015 : 181 - 193 .

WANG H , ZHANG D Q , WANG Y S , et al . RT-fall:a real-time and contactless fall detection system with commodity Wi-Fi devices [J ] . IEEE Transactions on Mobile Computing , 2017 , 16 ( 2 ): 511 - 526 .

PALIPANA S , ROJAS D , AGRAWAL P , et al . FallDeFi [J ] . Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies , 2018 , 1 ( 4 ): 1 - 25 .

THAMMASAT E , CHAICHARN J . A simply fall-detection algorithm using accelerometers on a smartphone [C ] // Proceedings of 5th 2012 Biomedical Engineering International Conference . Piscataway:IEEE Press , 2013 : 1 - 4 .

KHALILI A , SOLIMANAH , ASADUZZAMANM , et al . Wi-Fi sensing:applications and challenges [J ] . The Journal of Engineering , 2020 , 2020 ( 3 ): 87 - 97 .

PATWARI N , WILSON J , ANANTHANARAYANAN S , et al . Monitoring breathing via signal strength in wireless networks [J ] . IEEE Transactions on Mobile Computing , 2014 , 13 ( 8 ): 1774 - 1786 .

XIANG P , JI P , ZHANG D . Enhance RSS-based indoor localization accuracy by leveraging environmental physical features [J ] . Wireless Communications and Mobile Computing , 2018 ( 1 ).

ZHENG X L , WANG J L , SHANGGUANL F , et al . Design and implementation of a CSI-based ubiquitous smoking detection system [J ] . IEEE/ACM Transactions on Networking , 2017 , 25 ( 6 ): 3781 - 3793 .

KONINGS D , GRACE R , ALAM F . A stacked neural network-based machine learning framework to detect activities and falls within multiple indoor environments using Wi-Fi CSI [J ] . IEEE Sensors Letters , 2021 , 5 ( 5 ): 1 - 4 .

LIN Z Z , XIE Y C , GUO X N , et al . WiEat:fine-grained device-free eating monitoring leveraging Wi-Fi signals [C ] // Proceedings of 2020 29th International Conference on Computer Communications and Networks (ICCCN) . Piscataway:IEEE Press , 2020 : 1 - 9 .

ZHANG J , WU F X , HU W , et al . WiEnhance:towards data augmentation in human activity recognition using Wi-Fi signal [C ] // Proceedings of 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) . Piscataway:IEEE Press , 2020 : 309 - 314 .

HE Y , CHEN Y , HU Y , et al . Wi-Fi vision:sensing,recognition,and detection with commodity MIMO-OFDM Wi-Fi [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 9 ): 8296 - 8317 .

CHENG X Y , HUANG B K , ZONG J . A device-free human fall detection system based on GMM-HMM using Wi-Fi signals [C ] // Proceedings of 2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE) . Piscataway:IEEE Press , 2022 : 87 - 92 .

KEENANR M , TRAN L N . Fall detection using Wi-Fi signals and threshold-based activity segmentation [C ] // Proceedings of 2020 IEEE 31st Annual International Symposium on Personal,Indoor and Mobile Radio Communications . Piscataway:IEEE Press , 2020 : 1 - 6 .

WANG Y C , YANG S , LI F , et al . FallViewer:a fine-grained indoor fall detection system with ubiquitous Wi-Fi devices [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 15 ): 12455 - 12466 .

WU M J , TANG Z L . Research on user action recognition method based on parallel CNN-BiLSTM neural network [C ] // Proceedings of 2021 IEEE 4th Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC) . Piscataway:IEEE Press , 2021 : 2003 - 2009 .

NAKAMURA T , BOUAZIZI M , YAMAMOTO K , et al . Wi-Fi-CSIbased fall detection by spectrogram analysis with CNN [C ] // Proceedings of GLOBECOM 2020 - 2020 IEEE Global Communications Conference . Piscataway:IEEE Press , 2021 : 1 - 6 .

CHOI H , FUJIMOTO M , MATSUI T , et al . Wi-CaL:Wi-Fi sensing and machine learning based device-free crowd counting and localization [J ] . IEEE Access , 2022 ( 10 ): 24395 - 24410 .

LIU Z X , YUANR H , YUANY Z , et al . A sensor-free crowd counting frame work for indoor environments based on channel state information [J ] . IEEE Sensors Journal , 2022 , 22 ( 6 ): 6062 - 6071 .

TANG Z L , LIU Q Q , WU M J , et al . Wi-Fi CS Igesture recognitionbased on parallel LSTM-FCN deep space-time neural network [J ] . China Communications , 2021 , 18 ( 3 ): 205 - 215 .

SCOTTT R , RIDGEWAY K , MOZERM C . Adapted deep embeddings:asynthesis of methods for k-shot inductive transfer learning [C ] // Proceedings of the 32nd International Conference on Neural Information Processing Systems . New York:ACM , 2018 : 76 - 85 .

ZHANG Y , CHEN Y , WANG Y J , et al . CSI-based human activity recognition with graph few-shot learning [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 6 ): 4139 - 4151 .

MA X R , ZHAO Y N , ZHANG L , et al . Practical device-free gesture recognition using Wi-Fi signals based on meta learning [J ] . IEEE Transactions on Industrial Informatics , 2020 , 16 ( 1 ): 228 - 237 .

SUN Q R , LIU Y Y , CHUA T S , et al . Meta-transfer learning for few-shot learning [C ] // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2020 : 403 - 412 .

SHI Z G , ZHANG J A , XU R Y , et al . Environment-robust device-free human activity recognition with channel-state-information enhancement and one-shot learning [J ] . IEEE Transactions on Mobile Computing , 2022 , 21 ( 2 ): 540 - 554 .

DING X , JIANG T , ZHONG Y , et al . Improving WiFi-based human activity recognition with adaptive initial state via one-shot learning [C ] // Proceedings of 2021 IEEE Wireless Communications and Networking Conference (WCNC) . Piscataway:IEEE Press , 2021 : 1 - 6 .

WANG Y J , YAO L , WANG Y , et al . Robust CSI-based human activity recognition with augment few shot learning [J ] . IEEE Sensors Journal , 2021 , 21 ( 21 ): 24297 - 24308 .

WANG D Z , YANG J F , CUIW , et al . CAUTION:a robust WiFi-based human authentication system via few-shot open-set recognition [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 18 ): 17323 - 17333 .

SADREAZAMI H , BOLIC M , RAJAN S . TL-FALL:contactless indoor fall detection using transfer learning from a pretrained model [C ] // Proceedings of 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA) . Piscataway:IEEE Press , 2019 : 1 - 5 .

FDIDA S , PAU G , KASERA S , et al . Proceedings of the 21st annual ACM International Conference on Mobile Computing and Networking (Mobicom) [EB ] . 2015 .

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