A wearable fall detection method and system based on hierarchical classification

Abstract

The invention relates to a wearable fall detection method and system based on hierarchical classification. The method comprises a step of collecting the daily behavior data of a user, a step of carrying out synthesis and filtering processing on the daily behavior data to generate original data, a step of using a sliding window mechanism to extract the time domain feature of the original data, generating samples, and combining the samples to form a sample set, a step of using a first layer first class model to identify each sample in the sample set, and sending an identified result to a weighted two-class classification model of a second layer, a step of allowing the weighted two-class classification model of the second layer to be responsible for weighted distribution processing, generating a weighted fall sample, and sending the weighted fall sample to a rule two-class classification model of a third layer, and a step of allowing the rule two-class classification model of a third layer to judge whether a user falls according to a condition that whether the weighted fall sample is in accordance with a fall rule or not. Through the above method, the accurate judgment of a user fall behavior is realized.

Description

technical field [0001] The invention relates to the fields of pervasive computing and health monitoring, in particular to a wearable fall detection method and system based on hierarchical classification. Background technique [0002] On January 22, 2016, Li Zhong, spokesman of the Ministry of Human Resources and Social Security, pointed out that by 2014, China’s elderly population over 60 years old had reached 210 million, accounting for 15.5% of the total population, while the United Nations standard, the elderly population over 60 years old reached 10%. It is regarded as an aging society. As the age increases, the physiological function of the elderly gradually declines, the response to accidents becomes slower and slower, and falls are more likely to occur. Falls have become the first cause of injury death among the elderly, with a high incidence and serious injuries, which bring great burdens to individuals, families and society, and have gradually become a social publi...

AI Technical Summary

Problems solved by technology

On the other hand, frequent false alarms will arouse the disgust of users and reduce their trust in the detection system, which is not conducive to the practical application of the method, and the false alarm rate of the model is required to be as low as possible

Although there are many methods for fall detection, the existing methods are difficult to meet the requirements of low false alarm rate and low false alarm rate at the same time.

There are three main reasons for this problem: 1. The existing methods do not comprehensively consider the false alarm rate and false alarm rate of the model, but use a single evaluation standard (such as accuracy); 2. Using conventional machine learning classification methods, there is no Consider the particularity of the abnormal behavior of falling; 3. Due to the high similarity between the instantaneous process of some daily behaviors (such as running, going down the stairs, etc.) and the falling behavior, the impact of noise on the data reduces the accuracy of model detection Rate

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