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A fall detection method and system based on convolutional neural network

A convolutional neural network and detection method technology, applied in the field of fall detection and system based on convolutional neural network, can solve the problems of low accuracy, inability to describe complex behavior, high complexity of detection methods, and achieve high discrimination accuracy , the effect of identifying and maintaining structural relevance

Active Publication Date: 2020-01-21
SHANDONG UNIV
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Problems solved by technology

The various detection methods mentioned above have their own advantages and disadvantages. For example, the detection method based on the threshold is simple and low in complexity, but the accuracy is not high; the detection method based on machine learning is complex and can be well identified, but cannot describe some complex behaviors.

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  • A fall detection method and system based on convolutional neural network
  • A fall detection method and system based on convolutional neural network
  • A fall detection method and system based on convolutional neural network

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Embodiment Construction

[0037] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0038] like figure 1 As shown, the picture is a schematic diagram of the fall detection system. The whole system consists of two parts, A: fall detection equipment, B: remote equipment, A and B communicate through a wireless transmission unit.

[0039] In device A, it includes a main control unit, a sensor unit, a wireless transmission unit and an alarm unit. The sensor unit includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer.

[0040] The three-axis accelerometer is used to detect the acceleration of the user in the three directions of XYZ, the three-axis gyroscope is used to detect the tilt angle of the user in the three directions of XYZ, and the three-axis magnetometer is used to detect the direction of the user's movement.

[0041] The main control unit is respectively connected with the sensor unit, the wir...

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Abstract

The invention discloses a fall detection method and system based on a convolutional neural network. The invention collects three-axis acceleration, body inclination angle and motion direction, and performs data denoising; divides the data, and gives each axis data Carry out labeling and pre-coding sorting, and then perform discrete Fourier transform; build a convolutional neural network based on the transformed data, and perform convolutional neural network training to obtain a behavioral network model; perform pattern matching on the convolutional neural network model , judging whether the user has fallen is suitable for home health and safety monitoring, and can identify complex behaviors through convolutional neural networks, and make accurate judgments and real-time alarms for the falls of the elderly.

Description

technical field [0001] The invention relates to a fall detection method and system based on a convolutional neural network. Background technique [0002] In recent years, social life is developing towards digitization, networking, and intelligence, and people have begun to pay attention to intelligent home health and safety monitoring and protection. Among them, fall detection technology is an indispensable part of the family monitoring system. Accurate and effective fall detection methods are of great significance to safety monitoring. It can not only effectively prevent the elderly from falling and reduce a series of impacts after the fall (such as Paralysis, death, etc.), can also reduce the occupation of medical resources, which has far-reaching significance for the whole family and society. [0003] At present, fall detection systems are mainly based on video monitoring, audio monitoring, and wearable sensor monitoring. Among them, the cost of video monitoring is relat...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08B21/04G06N3/08G06N3/04
CPCG06N3/08G08B21/043G08B21/0446G06N3/045
Inventor 刘治宋佳花王承祥
Owner SHANDONG UNIV
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