A method and system for detecting human falls based on big data

Abstract

The invention discloses a human body tumble detection method and a human body tumble detection system based on big data. The human body tumble detection method is characterized in that characteristic vectors can be established according to information acquired by cell phone built-in sensors; a machine learning model can be used to identify whether the tumble occurs; when the user is in the tumbling state, the acquired information data can be transmitted to a big data platform in a real-time manner, and can be stored according to individual similarities; the platform is used to analyze the uploaded data by adopting the similarity metric algorithm, and is used to determine whether the updated data exists; when the updated data exists, the new data sample can be generated by the platform, and at the same time, a new machine learning classification model can be generated according to the data sample. When the tumbling state of the human body is determined by the system, the cell phone is used to trigger an alarming system automatically, and therefore the tumbling user can be rescued timely, and at the same time, the accuracy of the system can be increased continuously with the increasing sample number. The human body tumble detection method and the human body tumble detection system can be used for monitoring the activity safety of the children, the elderly, and the patients.

Description

technical field [0001] The invention relates to the fields of big data, machine learning, medical health and mobile Internet, and in particular to a method and system for detecting human falls based on big data. Background technique [0002] With the passage of time, the problem of "Baby Boomers" caused by the rapid population growth after World War II has become more and more serious. At the same time, with the change of lifestyle and the change of young people's life concept, the problem of "empty nest families" has become more and more serious. . According to statistics from relevant departments, the number of empty-nest families in my country has been on the rise, and it is expected that by 2030, the proportion of empty-nest elderly families will reach 90%. Falls are a common injury event in the elderly population, which will cause the elderly to suffer physical injuries such as fractures, bleeding, and central nervous system damage. If not treated in time, it may lead...

AI Technical Summary

Problems solved by technology

[0004] 1. It is difficult to obtain the real fall data of the elderly as the basic training data of the fall detection method. In the test, the fall data of students is generally used, which leads to the inability to improve the accuracy of the fall detection algorithm

[0005] 2. The classification models of the existing fall detection algorithms are fixed and oriented to the general public, and cannot be adjusted according to the characteristics of the human body

[0006] 3. In the traditional research on fall detection, the fall data is often only used as the basis for judging the state of the fall, and the data is released after the judgment

This results in a waste of data resources and loss of continuity in fall detection

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