Elder falling detection method and system based on multiple classifier integration

A technology with multiple classifiers and detection methods, applied in the direction of instruments, alarms, etc., can solve the problem of low accuracy and achieve high accuracy

Inactive Publication Date: 2015-05-20
GUANGZHOU HUAJIU INFORMATION TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, although many research institutions and companies have launched fall detection products, the main problem of the current fall detection system is that the accuracy of detection is not high, and there is a certain rate of misjudgment.

Method used

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  • Elder falling detection method and system based on multiple classifier integration
  • Elder falling detection method and system based on multiple classifier integration
  • Elder falling detection method and system based on multiple classifier integration

Examples

Experimental program
Comparison scheme
Effect test

Embodiment example 1

[0065] Implementation Example 1: Using a Support Vector Machine Classifier to Identify Falls

[0066] Support vector machine (Support Vector Machine, SVM) realizes automatic facial expression recognition. SVM is a classification method that has just been developed in recent years. It is based on the principle of structural risk minimization and has good generalization ability. Given a training sample set ,in is the input vector, For the corresponding category, SVM looks for the optimal boundary hyperplane that can correctly separate the two types of samples in the feature space. For a vector in the input space , if using Represents its corresponding eigenvector in the feature space, then the optimal boundary hyperplane is expressed as . The corresponding decision equation is . In any case, SVM does not require knowledge of the mapping Φ. Introduce kernel function , the dot product between vectors in the feature space can be expressed by the kernel function...

Embodiment example 2

[0081] Implementation Case 2: Using an Ensemble Classifier AdaBoost Identify falls

[0082] AdaBoost classifier is one of the top ten classification algorithms in data mining. It has the advantages of fast speed and simplicity. It does not need to adjust parameters except for the number of iterations, and does not require prior knowledge of weak classifiers. Given enough data and a moderately accurate weak classifier, it can promote the weak classifier to a strong classifier, thereby improving the recognition effect.

[0083] Different training sets in the AdaBoost classifier are achieved by adjusting the weights corresponding to each sample. At the beginning, the weight corresponding to each sample is the same, that is, for n samples, a weak classifier is trained under this sample distribution. For misclassified samples, increase their corresponding weights; and for correctly classified samples, reduce their weights, so that misclassified samples are highlighted, and a n...

Embodiment example 3

[0098] Implementation Example 3: Identifying Falls Using a Rotational Forest Classifier

[0099] Rotation forest is an ensemble learning method based on feature extraction method proposed by Juan J. Rodriguez et al. (Rodrignaz J J et al, Rotation forest: a new classifier ensemble method, TPAMI, 2006). The method first randomly divides the feature set into k subsets, where k is a parameter of the algorithm. Then apply principal component analysis (Principal component Analysis, PCA) on each partitioned subset. In order to preserve the information of the data in the method, all principal components will be retained. Using the PCA-based axis method has two purposes: to improve the performance of individual classifiers and to increase the diversity of all classifiers. The decision tree method is selected as the base classifier method, so the integrated method is called "rotation forest". The reason for choosing the decision tree as the base classifier is that it is sensitive to...

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Abstract

The invention relates to an elder falling detection method and system based on multiple classifier integration. The method includes acquiring information from sensors; performing falling direction, pressure, sound and misjudgment predictions; establishing falling characteristic vectors composed of the four prediction results; calling classifiers to implement integral falling prediction; outputting a falling judgment result. The system is characterized by comprising a sensor information acquisition module, a falling direction prediction module, a falling pressure prediction module, a falling sound prediction module, a falling misjudgment prediction module, a falling characteristic vector establishing module, an integral fall predicting module, an integral falling prediction model learning module, a falling early warning module and a falling file management module. The method and system has the advantages of high falling detection accuracy, low cost and convenience for carrying, and the alarming is performed timely when falling.

Description

technical field [0001] The invention relates to an elderly fall detection method and system based on multi-classifier integration, and belongs to the technical fields of medical health, machine learning and mobile Internet. Background technique [0002] The aging problem in our society is increasing day by day, and the demand for health and safety monitoring of the elderly is increasing day by day. According to the "China Injury Prevention Report" released by the Ministry of Health in 2007, the primary cause of accidental injuries to the elderly is falls. According to recent surveys, 49.7% of urban elderly people live alone; 25% of elderly people over the age of 70 fall at home every year. After a fall, people will face double dangers. The first is the physical injury directly caused by the fall itself, and the second is that if the fall cannot be rescued in time, it may lead to more serious consequences. It seriously affects the daily life ability, physical health and men...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08B21/04
CPCG08B21/043G08B21/0446
Inventor 不公告发明人
Owner GUANGZHOU HUAJIU INFORMATION TECH
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