Tumble detection method and system

Abstract

The invention provides a tumble detection method and system. The method comprises an off-line training phase and on-line detection phase. In the off-line training phase, a plurality of sub-classifiers are trained combined with random sample selection and random classification attribute. In the on-line tumble detection phase, a final determination result is given based on the classification results of the plurality of sub-classifiers. Experiments show that through the provided tumble detection method, an accuracy rate of 95.2%, sensitivity of 90.6% and specificity of 93.5% can be obtained, the provided tumble detection method is better than a tumble detection method based on SVM and BP neural networks obviously, tumble behaviors are detected accurately, and the tumble detection method has strong generalization capability and robustness.

Description

technical field [0001] The invention belongs to the field of detection and intelligent monitoring, and in particular relates to a fall detection method and system. Background technique [0002] With the increasing population aging and the increasing number of empty-nesters, the problem of personal injury caused by falls of the elderly has become increasingly prominent. According to statistics, falls have become the fourth leading cause of injury death in my country, and the first cause of injury death in the elderly over 65 years old, and the fall mortality rate further increases with the increase of the age of the elderly. In addition to directly causing the death of the elderly, falls also lead to a large number of disabilities, reduce the activity and range of activities of the elderly, and seriously affect the quality of life and physical and mental health of the elderly. [0003] In recent years, in order to reduce the further injury caused by accidental falls to the e...

AI Technical Summary

Problems solved by technology

However, due to the scarcity of real elderly fall data and the inability to use the elderly for fall data collection, most research work uses young people to simulate and collect experimental data sets for testing. Due to the small sample size and low representativeness, it often leads to classification. The device is over-fitting, and the accuracy and robustness of fall detection are not high

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