Tumble detection system and method based on machine learning

Abstract

The invention discloses a tumble detection system based on machine learning. The tumble detection system based on machine learning comprises at least one tumble detector and a cloud platform; the tumble detectors are used for detecting the movement condition and / or the physiological features of a user, generating tumble judgment data according to the movement condition and / or physiological feature information and judging whether the user tumbles or not; when the user is judged to be in a tumble state, data detected by the tumble detectors are transmitted to the cloud platform to be stored. The cloud platform executes a machine learning algorithm to analyze the data detected by all the tumble detectors to judge whether update data exist, if yes, the cloud platform generates a new tumble detection algorithm sample, and meanwhile the tumble detectors generate a new tumble detection algorithm according to the new tumble detection algorithm sample. The tumble detection system based on machine learning can check whether the new tumble judgment algorithm sample exist or not in use, if yes, the new algorithm is operated, and through the close-loop mode, the tumble detecting accuracy rate of tumble detection equipment in the hand of the user can be improved all the time.

Description

technical field [0001] The invention relates to the field of fall detection, in particular to a fall detection system based on machine learning. Background technique [0002] As our country gradually enters an aging society, the safety precautions in the daily life of the elderly have increasingly become a hot spot of social concern. According to reports in Shanghai, in 2010 alone, 1,983 people died of falls, of which 87.1% were elderly people over the age of 65. In addition, more than half of the cases of hospitalization due to injuries among the elderly are falls; more than 80% of the cases of hospitalization due to injuries are falls. [0003] After investigation and research, it is found that the rescue of the elderly within 20 minutes after the fall can minimize the injury, which is the golden time for rescue. Therefore, an accurate fall detection technology will definitely play a huge social and economic benefit in my country's aging society; [0004] However, fall ...

AI Technical Summary

Problems solved by technology

[0005] 1. It is difficult to obtain real fall data of the elderly as the basic data for the design and verification of fall detection methods; 2. The data obtained by existing sensors overlaps between falls and non-falls, making it difficult to distinguish, which is likely to cause misjudgments and omissions 3. In the whole world, there is no data sample for the verification of the fall algorithm, which virtually increases the difficulty of algorithm verification, which is a groundbreaking work; 4. The differences in individual characteristics of each person, such as gender, Height, weight, etc. will cause some data differences in the fall monitoring process, thus increasing the difficulty of developing and verifying the fall algorithm

Images