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Physiological function detection method based on fog calculation model

A technology of physiological functions and detection methods, applied in the fields of image processing, fog computing, and deep learning, to achieve the effect of improving efficiency

Inactive Publication Date: 2018-09-28
EAST CHINA NORMAL UNIVERSITY
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  • Application Information

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Problems solved by technology

[0002] The development of the Internet of Things and cloud computing technology has enabled various industries to enter new fields, and smart medical care under the Internet of Things has also made breakthroughs. However, with the increasing amount of data in practical applications, in the medical system, data The processing and transmission of

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  • Physiological function detection method based on fog calculation model
  • Physiological function detection method based on fog calculation model

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Embodiment 1

[0052] The subject stood about 3.5 meters in front of the camera, facing the camera, and the height of the camera was about 1.2 meters from the ground. Turn on the camera, collect images of human physiological functions, including joint activity images and walking images, and select 400 images as samples to be tested. The image is transmitted to the fog server, and the image is preprocessed in the fog server; the ROI area of ​​the image is extracted and normalized, and the image is normalized to a size of 128*128, which is used as the input of the deep learning network model.

[0053] In the fog server, the neural network with deep learning is used for training, and the trained human joint detection and recognition model is obtained. The human body posture database used in the training samples contains about 25,000 pictures and information of each node of 40,000 people with different postures, covering more than 410 kinds of activities.

[0054] Use the trained human joint de...

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Abstract

The invention discloses a physiological function detection method based on a fog calculation model. The method uses the deep learning method to detect the position of the main joint of a human body inthe fog calculation model, and uses the joint position to detect the physiological function activity. The method comprises following steps: a camera collects human physiological function activity images and uploads the images to a fog server; human joints are detected using the deep learning method in the fog server; Joint activity degree detection and gait detection are carried out by using joint position information; finally, the result data is stored in the fog server, which can be used for later data analysis. The physiological function detection comprises: joint activity degree detectionand gait detection. The invention combines deep learning and fog calculation, improves data processing capability and efficiency, and overcomes the shortcomings of traditional physiological functiondetection methods in time, place and medical personnel.

Description

technical field [0001] The invention relates to fog computing, image processing, and deep learning, and in particular to a physiological function detection method based on a fog computing model. Background technique [0002] The development of the Internet of Things and cloud computing technology has enabled various industries to enter new fields, and smart medical care under the Internet of Things has also made breakthroughs. However, with the increasing amount of data in practical applications, in the medical system, data The processing and transmission of them face a huge challenge. With the rapid development of deep learning and the introduction of the concept of fog computing in recent years, smart medical care has a new research direction. Under smart medical care, applying the model of deep learning + fog computing to physiological function detection is a new attempt in the Internet of Things environment and has broad development prospects. [0003] Using deep learn...

Claims

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Application Information

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IPC IPC(8): A61B5/11A61B5/00G06K9/62
CPCA61B5/1118A61B5/112A61B5/4528A61B5/7264G06V40/25G06V40/20G06F18/214
Inventor 曹桂涛宋新宇曹文明
Owner EAST CHINA NORMAL UNIVERSITY
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