Abstract
At present, human action recognition technology has been widely used in intelligent monitoring, fatigue driving warning, fall detection, family rehabilitation training and other fields. In order to accurately identify human actions, this paper uses a variety of machine learning models. At the same time, in order to improve the accuracy of recognition, this paper uses a variety of feature data sets to train the model. Through experiments, it is found that the model trained by the feature data set after PCA dimensionality reduction has the best comprehensive effect. The prediction accuracy of logistic regression algorithm, KNN algorithm and LightGBM algorithm has been significantly improved. Compared with the models trained by other feature data sets, the recognition accuracy has been improved by 6% -20%, reaching 0.89, 0.87 and 0.83 respectively.
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