Method for predicting human activity positions in smart home environment

A technology for smart home and human activities, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of unwanted video camera devices and infringing on household privacy, reduce mutual interference, ensure prediction accuracy, data large amount of effect

Inactive Publication Date: 2013-09-04
HOHAI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] At present, most of the research on human activity recognition at home and abroad is concentrated in the field of vision-based recognition research. However, for smart homes, many residents think that installing video cameras will violate the privacy of residents, so they do not want to install video cameras in their homes.

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  • Method for predicting human activity positions in smart home environment
  • Method for predicting human activity positions in smart home environment
  • Method for predicting human activity positions in smart home environment

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Embodiment Construction

[0034] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0035] figure 2 It is the sensor layout diagram of the smart home environment test bench. The installed sensors include the motion sensor M and the item sensor I. The item sensors include the temperature sensor T, the light switch sensor L, the fan switch sensor F and the door switch sensor D. Since the present invention predicts the location of human activities, only the data collected by the motion sensor is needed.

[0036] The program flow chart of the model training process of the present invention is as image 3 As shown, in order to give a clear description, we take 10 kinds of human activities in daily life as examples to conduct experiments. The experimenters repeat these 10 kinds of activities in the smart home environment test bench as required. The experiment lasted for 56 days. , a total of 600 experimental activity sample data and ...

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Abstract

The invention discloses a method for predicting human activity positions in a smart home environment. According to data of different human activities collected by a motion sensor mounted in a test bench in the smart home environment, the method based on a period timeliness combined with a text compression algorithm (TimeLeZi and TLZ) is proposed for predicting the positions of the human activities. The method is suitable for being applied to the technical fields of smart homes, pattern recognition and the like. According the to the method for predicting the human activity positions in the smart home environment and based on the TLZ algorithm, compared with a result obtained through an LZ78 model and an ActiveLeZi model method, the predicting result of the method has higher prediction accuracy.

Description

technical field [0001] The invention discloses a method for predicting human activity locations in a smart home environment, in particular to a method for predicting human activity locations based on cycle timeliness combined with a text compression algorithm (Time LeZi, TLZ), which is applied to smart homes, pattern recognition, etc. technology field. Background technique [0002] Smart home is an efficient, comfortable, safe, convenient and environment-friendly living environment based on the residence as a platform, combining construction, network communication, information appliances, equipment automation, and integrating systems, structures, services, and management. Smart home uses advanced computer technology, network communication technology, and integrated wiring technology to organically combine various subsystems related to home life, and through overall management, home life is more comfortable, safe and effective. Compared with ordinary homes, smart homes not o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 方红庆阮金金
Owner HOHAI UNIV
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