Fall detection method and device, electronic equipment and storage medium

Abstract

The invention provides a fall detection method and device, electronic equipment and a storage medium. The method comprises the following steps: generating a distance-velocity diagram of a human body according to a radar reflection signal; generating a distance-vertical angle diagram of the human body and a distance-horizontal angle diagram of the human body according to the radar reflection signal; extracting a first feature from the distance-velocity diagram, extracting a second feature from the distance-vertical angle diagram, and extracting a third feature from the distance-horizontal angle diagram; and inputting the first feature, the second feature and the third feature into a machine learning classifier to obtain a detection result. According to the method, the distance-velocity diagram of the human body, the distance-vertical angle diagram and the distance-horizontal angle diagram of the human body are generated according to radar reflection signals and are analyzed to obtain a detection result, interference of environmental factors can be avoided by adopting signals related to the human body, and information contained in the radar reflection signals can be more comprehensively utilized. And the accuracy of fall detection can be improved.

Description

technical field [0001] The present invention relates to the technical field of action recognition, in particular to a fall detection method, device, electronic equipment and storage medium. Background technique [0002] Falls are common among older adults, with falls occurring more than 20 percent of the time in those over 60 and more than 33 percent in those over 80. Falls are one of the leading causes of disability, long-term pain and death in older adults and the fifth leading cause of death in older adults. Falls in the elderly can seriously affect their quality of life, and they may even lose their ability to take care of themselves, or even die. For the elderly living alone, they may not be discovered until several hours after falling to the ground, causing them to miss timely treatment. So far, many studies on fall detection are based on RGB cameras and some wearable devices. However, these methods are inconvenient or unacceptable to the elderly, as they may forget...

AI Technical Summary

Problems solved by technology

[0003] (1) Due to the lack of informative richness, it is difficult to distinguish actions such as sitting down quickly or sitting on the ground from falls, because their changes in speed or distance are similar to falls

[0004] (2) The system continues to run at any time, instead of judging the type of action only after detecting human motion, which wastes energy and computing resources of the device

[0005] (3) The signal contains environmental information, which is easily affected by environmental changes during the classification process

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