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Fall detection method based on convolutional neural network and mobile phone sensor data

A technology of convolutional neural network and detection method, which is applied in the field of fall detection based on convolutional neural network and mobile phone sensor data, can solve the problem of inapplicability of large-scale data and achieve high-precision results

Active Publication Date: 2017-09-12
ZHEJIANG FORESTRY UNIVERSITY
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AI Technical Summary

Problems solved by technology

Human-designed feature engineering requires rich prior knowledge, which is not applicable in the feature construction process of large-scale data

Method used

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  • Fall detection method based on convolutional neural network and mobile phone sensor data
  • Fall detection method based on convolutional neural network and mobile phone sensor data
  • Fall detection method based on convolutional neural network and mobile phone sensor data

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Embodiment Construction

[0032] Below in conjunction with accompanying drawing and embodiment the present invention will be further described:

[0033] figure 1 It is the flow chart of the implementation of fall detection in the present invention.

[0034] Step S001, placing a smartphone with a built-in acceleration sensor and a gyroscope on the chest and outer thighs of the human body, and recording data and behavior information during human activities. The details are as follows: Place two sets of sensors on the body of the application object, generally choose the jacket pocket and trousers pocket, the mobile phone acceleration sensor in the jacket pocket is marked as Acc1, the gyroscope is marked as Gyro1, the trouser pocket acceleration sensor is marked as Acc2, and the gyroscope is marked as Gyro2. Each sensor records data on the x, y, and z axes, for a total of 12 features. Record the corresponding human behavior tags for each sensor data, record a fall as 1, and record a fall as 0, and corres...

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Abstract

The invention discloses a fall detection method based on a convolutional neural network and mobile phone sensor data. A convolutional neural network model is trained with mobile phone sensor data. The method comprises the following steps: acceleration and angular velocity data is collected through a smart phone carried by a human body, and the data is sorted and input to the convolutional neural network model, wherein the designed convolutional neural network model includes feature abstraction, identification, classification, and other task layers, and the parameters are adjusted gradually through stochastic gradient descent optimization; and in the real-time detection stage, the sorted mobile phone sensor data is input to the convolutional neural network model, and whether the human body falls down is predicted through model output. Compared with the traditional threshold detection and the general machine learning detection, the precision is improved. The method is especially suitable for daily safety monitoring of the elderly and the young children.

Description

technical field [0001] The invention relates to a fall detection method based on a convolutional neural network and mobile phone sensor data. The method establishes a human body fall detection model in a data-driven manner, fully excavates the human body behavior information in the mobile phone sensor data, and is especially suitable for the elderly Daily monitoring of the safety of people and young children. Background technique [0002] With the increase of age, the various functions of the human body decline, and the possibility of accidental falls of the elderly increases. If an accidental fall occurs and cannot be treated in time, it will cause serious physical and mental harm to the elderly. Accidental falls have become an One of the direct causes of human injury or even death, timely detection and treatment is an important part of ensuring the health of the elderly. With a large elderly population base and a high growth rate in my country, effective fall detection ha...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06K9/62
CPCG06N3/084G06N3/045G06F18/24G06F18/214
Inventor 吕艳张萌倪益华
Owner ZHEJIANG FORESTRY UNIVERSITY
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