Abstract: An action evaluation model building apparatus and an action evaluation model building method thereof are provided. The action evaluation model building apparatus stores a plurality of raw data sets and a plurality of standard action labels corresponding thereto. Based on machine learning algorithms, the action evaluation model building apparatus computes the raw data sets and performs a supervised learning to build a feature vector creation model and a classifier model. The action evaluation model building apparatus determines a representation action feature vector of each standard action label by randomly generating a plurality of action feature vectors and inputting them into the classifier model. The action evaluation model building apparatus builds an action evaluation model based on the feature vector creation model, the classifier model and the representation action feature vectors.

Abstract: A model building server and model building method thereof are provided. The model building server stores a model building program having a configuration combination. The model building server randomly generates a plurality of first configuration combination codes for feature categories, model algorithm categories and hyperparameters to set the configuration combination, and runs the model building program based on a first optimization algorithm to determine a first model. According to at least one determined feature category and at least one determined model algorithm category indicated by the configuration combination code corresponding to the first model, the model building server randomly generates a plurality of second configuration combination codes for features, model algorithms and hyperparameters to set the configuration combination, and runs the model building program based on a second optimization algorithm to determine an optimization model.

Abstract: An action evaluation model building apparatus and an action evaluation model building method thereof are provided. The action evaluation model building apparatus stores a plurality of raw data sets and a plurality of standard action labels corresponding thereto. Based on machine learning algorithms, the action evaluation model building apparatus computes the raw data sets and performs a supervised learning to build a feature vector creation model and a classifier model. The action evaluation model building apparatus determines a representation action feature vector of each standard action label by randomly generating a plurality of action feature vectors and inputting them into the classifier model. The action evaluation model building apparatus builds an action evaluation model based on the feature vector creation model, the classifier model and the representation action feature vectors.

Abstract: A model building server and model building method thereof are provided. The model building server stores a model building program having a configuration combination. The model building server randomly generates a plurality of first configuration combination codes for feature categories, model algorithm categories and hyperparameters to set the configuration combination, and runs the model building program based on a first optimization algorithm to determine a first model. According to at least one determined feature category and at least one determined model algorithm category indicated by the configuration combination code corresponding to the first model, the model building server randomly generates a plurality of second configuration combination codes for features, model algorithms and hyperparameters to set the configuration combination, and runs the model building program based on a second optimization algorithm to determine an optimization model.

Abstract: An identification method includes: sensing movement data; capturing multiple feature data from the movement data; cutting the first feature data into a plurality of first feature segments, dividing the first feature segments into a plurality of first feature groups, and calculating multiple first similarity parameters of the first feature groups respectively corresponding to a plurality of channels; making the first feature groups correspond to the channels according to the first similarity parameters; simplifying the first feature groups corresponding to the channels respectively by a convolution algorithm to obtain a plurality of first convolution results corresponding to the first feature groups; simplifying the first convolution results corresponding to the first feature groups respectively by a pooling algorithm to obtain multiple first pooling results corresponding to the first feature groups; and combining the first pooling results corresponding to the first feature groups to generate a first feature m

Abstract: An identification method includes: sensing movement data; capturing multiple feature data from the movement data; cutting the first feature data into a plurality of first feature segments, dividing the first feature segments into a plurality of first feature groups, and calculating multiple first similarity parameters of the first feature groups respectively corresponding to a plurality of channels; making the first feature groups correspond to the channels according to the first similarity parameters; simplifying the first feature groups corresponding to the channels respectively by a convolution algorithm to obtain a plurality of first convolution results corresponding to the first feature groups; simplifying the first convolution results corresponding to the first feature groups respectively by a pooling algorithm to obtain multiple first pooling results corresponding to the first feature groups; and combining the first pooling results corresponding to the first feature groups to generate a first feature m