Human activity recognition system based on active learning and Wi-Fi sensing

Guangzhi ZHAO; Zhipeng ZHOU; Wei GONG; Shaoqing CHEN; Haoquan ZHOU

doi:10.11959/j.issn.2096-3750.2022.00262

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Human activity recognition system based on active learning and Wi-Fi sensing

Theory and Technology | 更新时间：2024-06-05

- Human activity recognition system based on active learning and Wi-Fi sensing
- Chinese Journal on Internet of Things Vol. 6, Issue 1, Pages: 44-52(2022)
- 作者机构：
  
  1. 中国科学技术大学，安徽合肥 230026
  2. 中国科学技术大学附属第一医院（安徽省立医院），安徽合肥 230001
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2022.00262
  CLC： TP393
- Published：30 March 2022，
  
  Published Online：2022-03，
- 稿件说明：
移动端阅览
GUANGZHI ZHAO, ZHIPENG ZHOU, WEI GONG, et al. Human activity recognition system based on active learning and Wi-Fi sensing. [J]. Chinese journal on internet of things, 2022, 6(1): 44-52.
DOI：

GUANGZHI ZHAO, ZHIPENG ZHOU, WEI GONG, et al. Human activity recognition system based on active learning and Wi-Fi sensing. [J]. Chinese journal on internet of things, 2022, 6(1): 44-52. DOI： 10.11959/j.issn.2096-3750.2022.00262.

摘要

基于深度学习和Wi-Fi感知的人体行为识别系统已逐步成为主流的研究方向，在近年来得到了长足的发展。然而，现有的系统严重依赖于大量带标记样本以达到良好的识别精度。这导致了大量的人力成本用于标记数据，同时现有系统也难以应用于实际场景。针对该问题，提出一种将主动学习应用于Wi-Fi感知的人体行为识别系统——ALSensing。该系统是第一个将主动学习和Wi-Fi人体行为识别相结合的系统，能够利用有限数量的已标记训练样本构建一个性能良好的行为识别器。利用商用的Wi-Fi设备实现了ALSensing系统，并且使用6个不同场景的实际数据集评估了它的性能。实验结果显示，ALSensing 利用 3.7%的已标记训练样本能够达到 52.83%的识别精度，利用 15%的已标记训练样本能够达到 58.97%的识别精度，而利用现有基于深度学习的人体行为识别系统测量的参考基准在100%的已标记训练样本的情况下达到62.19%的识别精度。可见，ALSensing能够实现与现有基于深度学习的人体行为识别系统接近的识别精度，但是所需要的已标记训练样本的数量大幅度减少。

Abstract

Human activity recognition system based on deep learning and Wi-Fi sensing has gradually become the mainstream research field and has been developed in recent years.However

related systems heavily rely on training with huge labeled samples to reach a high accuracy

which is labor-intensive and unrealistic for many real-world scenarios.To solve this problem

a system that combines active learning with Wi-Fi based human activity recognition—ALSensing was proposed

which was able to train a well-perform classifier with limited labeled samples.ALSensing was implemented with commercial Wi-Fi devices and evaluated in six real environments.The experimental results show that ALSensing achieves 52.83% recognition accuracy using 3.7% of total training samples

58.97% recognition accuracy using 15% of total training samples

while the existing full-supervised system reaches 62.19% recognition accuracy.It demonstrates that ALSensing has a similar performance with baseline but requires much less labeled samples.

关键词

主动学习人体行为识别Wi-Fi

Keywords

active learninghuman activity recognitionWi-Fi

references

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