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An Indoor Fall Detection Method for the Elderly Based on Weight Fusion Depth and Skeletal Features

A deep feature and technology for the elderly, applied in the field of behavior recognition and computer vision, can solve problems such as reducing the accuracy of recognition methods

Active Publication Date: 2020-10-27
CHANGZHOU UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

If all behavioral data are included in behavioral signatures, redundant information may reduce the accuracy of the recognition method

Method used

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  • An Indoor Fall Detection Method for the Elderly Based on Weight Fusion Depth and Skeletal Features
  • An Indoor Fall Detection Method for the Elderly Based on Weight Fusion Depth and Skeletal Features
  • An Indoor Fall Detection Method for the Elderly Based on Weight Fusion Depth and Skeletal Features

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Experimental program
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Embodiment Construction

[0065] 1. Implementation process

[0066] The main steps of the method provided by the present invention are as follows: use the Kinect device to obtain the depth image and bone image of the human body; carry out feature extraction to the depth image; carry out coordinate conversion to the skeleton map nodes; calculate mutual information to obtain key frames, and represent specific behaviors; The three models of the key frame form a frame of bone features; the depth feature and the bone feature are weighted and fused to obtain a recognition classification model; judgments are made based on the behavior recognition model; after a fall is judged, a fall alarm is issued.

[0067] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the following in conjunction with the appended figure 2 , to provide a complete and clear description of the technical solutions in the embodiments of the present invention:

[0068] St...

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Abstract

The invention discloses a method for indoor fall detection of the elderly based on weight fusion of depth images and skeleton key frames. The method includes the following steps: using a Kinect device to obtain a depth image and a skeleton image of a human body; performing feature extraction on the depth image; Skeleton map nodes perform coordinate conversion; calculate mutual information to obtain key frames to represent specific behaviors; extract three models of key frames to form bone features; combine depth features and bone features to obtain a recognition classification model; judge according to the behavior recognition model ; After a fall is judged, a fall alarm is issued. The effective effect of the present invention is that redundant information is reduced, and the detection rate of falling is high, which can be realized only by simple equipment, and is cheap and easy to realize.

Description

technical field [0001] The invention relates to the fields of computer vision, behavior recognition and the like, in particular to a method for detecting falls in an elderly person's room. Background technique [0002] Falls are a major health threat faced by older adults. Falls can lead to serious consequences such as psychological trauma, fractures, and soft tissue injuries, directly affecting the physical and mental health of the elderly, and indirectly increasing the burden on families and society. It has become a very important topic in geriatric clinical medicine. The use of reasonable and effective detection methods can analyze the fall situation in time, and then deal with the falls of the elderly. Since the falling behavior is similar to ordinary behavior, it can be detected by the method of human behavior recognition. [0003] Human behavior recognition has a wide range of applications, and it is more prominent in applications such as video surveillance, human-co...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/36G06F18/22
Inventor 侯振杰莫宇剑林恩许艳夏宇杰林锦雄王涛
Owner CHANGZHOU UNIV
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