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Indoor Personnel Activity Recognition Method and Human-Computer Interaction System Based on Channel State Information

A channel state information and activity recognition technology, applied in the field of human-computer interaction systems, can solve the problems that the system cannot adapt well to different environments, the activity recognition effect is poor, and the recognition rate is low.

Active Publication Date: 2021-04-16
XIDIAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] To sum up, the problems existing in the existing technology are: the current indoor occupant activity recognition technology lacks a matching model that can quantitatively correlate CSI statistical features with indoor occupant activities, resulting in the system not being well adapted to different environments, or when transplanted In different environments, it is necessary to reset the parameters, that is, the environment adaptability is poor; it is very sensitive to the influence of environmental random noise and indoor channel changes, which will lead to poor activity recognition effect and low recognition rate

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  • Indoor Personnel Activity Recognition Method and Human-Computer Interaction System Based on Channel State Information

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Embodiment Construction

[0147] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0148] Aiming at the fact that current indoor personnel activity recognition technology is easily affected by environmental noise, channel changes and CSI data statistical characteristics, the present invention overcomes the shortcoming of low reliability of recognition results in the prior art and improves activity recognition accuracy. It specifically relates to a method for identifying indoor human activities based on channel state information, which can be used in human-computer interaction, smart home, elderly care and emergency assistance.

[0149] The application principle of the present invention will be described i...

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Abstract

The invention belongs to the technical field of wireless communication, and discloses a method for identifying indoor personnel activities based on channel state information and a human-computer interaction system; preprocessing the collected CSI data to extract environmental change information including correlation coefficients and variances; and then using decision tree perception The surrounding environment monitors human activities, and if no human activities are detected, it will continue to collect CSI data and maintain the state of activity monitoring, otherwise it will trigger the activity feature extraction module; in this module, the collected CSI data will be removed Extreme value processing and low-pass filtering processing; then use the dynamic time planning algorithm based on principal component analysis for activity matching and the most important personnel activity identification. The present invention combines indoor personnel activity monitoring and activity recognition, and performs indoor personnel activity recognition through PCA-DTW, which saves the steps of offline data collection and training in the prior art, and improves system performance and recognition rate.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a method for identifying indoor personnel activities based on channel state information and a human-computer interaction system. Background technique [0002] Currently, the closest existing technologies: E-eyes and WiFinger, and WiGest and CARM. [0003] Defects of the existing technology: E-eyes and WiFinger cannot effectively adapt to different environments or need to modify system parameters in different environments, and their portability is poor, which reduces the efficiency of recognition. This is because E-eyes and WiFinger lack a matching model that can quantitatively correlate CSI statistical features with indoor human activities; WiGest and CARM are very sensitive to the influence of environmental noise and indoor channel changes when performing activity recognition. This is because they need to capture small changes in activities to be able...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W4/029H04W4/33H04W16/20H04B17/309
CPCH04W16/20H04B17/309H04W4/029H04W4/33
Inventor 王勇丁建阳
Owner XIDIAN UNIV
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