+高级检索
首页 > 过刊浏览>2023年第46卷第23期 >146-152
PDF HTML阅读 XML下载 导出引用 引用提醒
基于惯性传感器的HAR数据采集系统设计
CSTR:
[cstr]
作者:
作者单位:

1.沈阳化工大学信息工程学院 沈阳 110142; 2.中国科学院网络化控制系统重点 实验室 沈阳 110016; 3.中国科学院沈阳自动化研究所 沈阳 110016

中图分类号:

TN96；TP212.9；TP368

基金项目:

国家重点研发计划项目（2022YFB3204501）资助


Design of HAR data acquisition system based on inertial sensor
Author:
Affiliation:

1.College of Information Engineering, Shenyang University of Chemical Technology，Shenyang 110142, China; 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences，Shenyang 110016, China; 3.Shenyang Institute of Automation, Chinese Academy of Sciences，Shenyang 110016,China

  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献
    • |
  • 相似文献
    • |
  • 引证文献
    • | |
  • 文章评论
    摘要:

    目前针对人体活动识别的数据采集硬件系统研究有限，且存在可参考的数据集单一和泛化性能较低的问题。本文设计一个低功耗、支持数据实时传输、模块化的数据采集系统，并提出一种具有随机性和交叉性的数据采集方法。首先搭建低功耗采集平台进行数据的采集、无线收发和预处理；其次制定全面且精确的采集方案，提高数据集的丰富度；最后用2D-CNN神经网络对不同模式下采集到的数据集进行模型训练。实验结果表明,该采集系统结构合理，具备低功耗特性，能够确保数据传输具备实时性能；该采集系统的应用极大地提高了数据集的质量；获得的数据集在深度学习模型上的准确率可达92.54％；相较于传统数据集，新数据集在人体活动识别任务中表现出更为显著的效果，该采集系统和数据集的开发为神经网络应用提供便利。

    Abstract:

    Currently, research on data acquisition hardware systems for human activity recognition is limited, and there is a problem of a lack of diverse and generalized reference datasets. In this paper, we design a low-power data acquisition system that supports real-time data transmission and propose a data acquisition method with randomness and crossover. Firstly, a low-power acquisition platform is built for data acquisition, wireless transmitting and receiving, and pre-processing; secondly, a comprehensive and accurate data acquisition scheme is developed to improve the generalization of the new dataset; and finally, a 2D-CNN neural network is used to train a model for the acquired dataset in different modes. The experimental results demonstrate that the designed data acquisition system has a reasonable structure and low power consumption, ensuring real-time data transmission. The application of this system greatly improves the quality of the dataset. The obtained dataset achieves an accuracy of 92.54% on deep learning models. Compared to traditional datasets, the new dataset shows significantly better performance in human activity recognition tasks. The development of this data acquisition system and dataset provides convenience for neural network applications.

    参考文献
    相似文献
    引证文献
引用本文

王颖,杨志家,谢闯,曾静,王彬燏.基于惯性传感器的HAR数据采集系统设计[J].电子测量技术,2023,46(23):146-152

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 在线发布日期: 2024-03-21
文章二维码
您是第 位访问者
主管单位：北京电子控股有限责任公司
主办单位：北京无线电技术研究所
地址：北京市东城区北河沿大街79号
电话：13810562337
电子测量技术 ® 2025 版权所有