Falling detection and early-warning method based on smart wearable device

Abstract

The invention discloses a falling detection and early-warning method based on a smart wearable device. The method comprises the following steps: reading training data; extracting characteristic parameters; training an SVM classifier; collecting sensing data; testing the collected data by using an SVM classifier, determining whether a falling risk occurs and carrying out prompting; and detecting whether falling occurs and carrying out early warning. According to the invention, the SVM classifier is trained based on characteristic parameters extracted from gait data of the healthy person and abnormal gait data; and whether the gait is in an abnormal state is distinguished and identified accurately, so that the possible falling situation is prompted in advance; and if the falling may occur, real-time falling detection is carried out on the user to protect the safety of the user. The falling detection and early-warning method based on a smart wearable device can be applied to the field ofdata processing widely.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a fall detection and early warning method based on smart wearable devices. Background technique [0002] With the aging of the population, the population over the age of 60 in my country has exceeded 230 million. The health and safety of the elderly has attracted widespread attention from the society. The daily life ability of the elderly has declined, and problems such as falls, trends, sudden illnesses, and forgetting to take medicine are common. . Existing smart wearable devices can detect static parameters such as blood pressure and heart rate, and detect dynamic parameters such as falls, and then get more feedback during product use. For the elderly, a fall may cause joint damage or even a fracture. The current fall detection technology can only realize the detection after the fall, and it is difficult to meet other functional requirements. [0003] In terms of gait recogniti...

AI Technical Summary

Problems solved by technology

First of all, the data collection uses the pressure sensor on the sole of the foot; secondly, there is a big difference between the gait characteristics of Parkinson's patients and the gait characteristics of the elderly who are prone to falls, and it is difficult to distinguish the fall state, in a dangerous state where a fall may occur and in a normal state

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