Analysis and processing system for mass plantar pressure data
A plantar pressure, analysis and processing technology, applied in the field of analysis and processing systems for massive plantar pressure data, can solve problems such as unsatisfactory plantar pressure data analysis and processing methods, and achieve the effect of standardized and effective processing methods
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Embodiment 1
[0055] This embodiment provides an analysis and processing system for a large amount of plantar pressure data, including:
[0056] 1. Mass footprint data acquisition module:
[0057] This module starts with the collection of user footprint data, which mainly includes dynamic and static footprint data; then obtains the personal information entered by the user, including the user's gender, age, height, weight, etc.; and finally acquires a large amount of data.
[0058] 2. Data attribute unified module;
[0059] 1) The data type is unified:
[0060] (1) Real-time dynamic data needs to be converted into static data through average processing for a certain period of time. During the averaging process, the dynamic data that cannot be obtained is used for feature extraction, and one-dimensional data is directly formed for training or testing;
[0061] (2) For the data that can obtain the characteristic information of the stride in the walking process, it is necessary to use automat...
Embodiment 2
[0090] This embodiment provides the situation that the data processed in Embodiment 1 is used for the adjustment and training of the deep learning neural network:
[0091] The preprocessed data is applied to classification (such as gender, age, height, and weight determination): (Here, the preprocessed overall image and each local area image can be trained separately to obtain models for different plantar regions)
[0092] (1) Training data preparation: the two-dimensional image data of the training set and the verification set after preprocessing are artificially divided into N groups according to the classification requirements;
[0093] (2) Group training based on the CNN network. The improved AlexNet network is used here. The network improvement is as follows:
[0094] A. Initial network:
[0095] Network composition: 4 layers of convolutional layers, 2 layers of pooling layers, and 2 layers of fully connected layers.
[0096] Internet connection:
[0097] conv1+pooling...
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