Sleep Action Recognition Based on Software-Defined Intelligence

WANG Cheng-liang; ZHENG Cheng; ZENG Zhuo

doi:10.12263/DZXB.20200069

您当前的位置：

首页 >

文章列表页 >

Sleep Action Recognition Based on Software-Defined Intelligence

更新时间：2025-12-08

- Sleep Action Recognition Based on Software-Defined Intelligence
- Acta Electronica Sinica Vol. 49, Issue 1, Pages: 85-89(2021)
- 作者机构：
  
  重庆大学计算机学院,重庆,400044
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61672115);Research Innovation Project for Postgraduate of Chongqing Municipality (No.CYS19049)
- DOI：10.12263/DZXB.20200069
  CLC： TP181
- Published Online：25 January 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
WANG Cheng-liang, ZHENG Cheng, ZENG Zhuo. Sleep Action Recognition Based on Software-Defined Intelligence[J]. Acta Electronica Sinica, 2021, 49(1): 85-89.
DOI：

WANG Cheng-liang, ZHENG Cheng, ZENG Zhuo. Sleep Action Recognition Based on Software-Defined Intelligence[J]. Acta Electronica Sinica, 2021, 49(1): 85-89. DOI： 10.12263/DZXB.20200069.

摘要

基于软件定义智能层次化模型设计了一种睡眠动作识别系统，该系统可通过规则推理来应对智能环境中的各种变化因素.设计了一种时间队列实时提取动作特征来训练模型，提出了一种规则提取算法从该模型中提取系统所需规则.该系统基于这些规则可识别9种睡眠动作，每种动作的识别精确率均可达到96%以上，总识别准确率达到98.9%，且比其它系统适应性更强.实验结果表明，该系统通过更新规则可快速应对节点位置、节点数量和用户需求的变化.

Abstract

Based on the hierarchical model of SDI (Software-Defined Intelligence)

a system for action recognition during sleep is designed to deal with various changing factors in smart environment through rule-based reasoning. A time queue is designed to extract the characteristics of actions in real-time to train the model

and a rule extraction algorithm is proposed to extract the rules required by the system from the model. Depending on these rules

the proposed system can recognize nine types of sleep actions: The recognition precision of each type can exceed 96%; the total recognition accuracy can reach 98.9%. Importantly

it has more robust adaptability than other systems. Experimental results show that the system can update rules for quickly adapting to changes in node position

the number of nodes

and user requirements.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Study on Optimal Allocation of Inference Nodes for Fog Computing in Smart Environment

Study on Data Pre-Deployment Based on Inference and Computing of Edge Devices in Smart Environment

Related Author

WANG Cheng-liang

HUANG Xin-tian

WANG Cheng-liang

ZHAO Kai

LIU Jia-min

Cheng-liang WANG

Kai ZHAO

Jia-min LIU

Related Institution

College of Computer Science, Chongqing University

College of Optoelectronic Engineering,Chongqing University

College of Computer Science and Technology， Chongqing University

College of Optoelectronic Engineering， Chongqing University

⁰