Old man falling detecting method and system based on multi-sensor fusion

A multi-sensor fusion and detection method technology, which is applied in the fields of sensors, diagnostic recording/measurement, medical science, etc., can solve the problems of low accuracy rate and misjudgment rate, and achieve the effect of high accuracy rate

Inactive Publication Date: 2015-05-06
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, there are still many problems in the research of fall detection systems. The main problem is that the accuracy of detection is not high, and there is a certain rate of misjudgment.

Method used

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  • Old man falling detecting method and system based on multi-sensor fusion
  • Old man falling detecting method and system based on multi-sensor fusion
  • Old man falling detecting method and system based on multi-sensor fusion

Examples

Experimental program
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Effect test

Embodiment example 1

[0066] Implementation Example 1: Using a Support Vector Machine Classifier to Identify Falls

[0067] 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 training samples set, of which 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 x in the input space, if z = Ф(x) is used to represent its corresponding feature vector in the feature space, then the optimal boundary hyperplane is expressed as w · z + b = 0. The corresponding decision equation is f(x) = sign (w · z + b). In any case, SVM does not require knowledge of the mapping Φ. Introducing the kernel function k( ), the dot product between vectors in...

Embodiment example 2

[0082] Implementation Case 2: Using an Ensemble Classifier AdaBoost Identify falls

[0083] 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.

[0084] 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

[0099] Implementation Example 3: Identifying Falls Using a Rotational Forest Classifier

[0100] 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 preserved. 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 t...

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Abstract

The invention relates to an old man falling detecting method based on multi-sensor fusion. The method includes the steps of automatically collecting information of sensors, constructing feature vectors of the sensors; conducting fusion to form an overall falling feature vector; completing feature selection; and calling a classifier to complete falling identification. The invention relates to a falling alarm system based on the multi-sensor fusion. The falling alarm system is characterized by comprising a sensor information collecting module, a sensor feature vector construction module, a falling feature vector construction module, a feature selecting module, a falling identification module, a falling identification model learning module, a falling alarming module and a falling file management module. The old man falling detecting method and system have the beneficial effects that the falling detection accuracy is high; falling help calling is in time; the functions are comprehensive; the cost is low; and carrying is convenient.

Description

technical field [0001] The invention relates to an elderly fall detection method and system based on multi-sensor fusion, 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 the survey, 49.7% of the urban elderly live alone; 25% of the elderly over the age of 70 fall at home every year. After a fall, you will face double dangers. The first is the physical injury directly caused by the fall itself, and the second is that if you cannot get timely assistance after the fall, it may lead to more serious consequences. Therefore, falls are the cause of disability, disability and death for the eld...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/11
CPCA61B5/1117A61B5/725A61B5/7257
Inventor 不公告发明人
Owner GUANGZHOU HUAJIU INFORMATION TECH
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