Construction and Application of Identification Model for Children's Physical Activity Types Based on Accelerometer Data

  Objective  To construct a high-accuracy and high-time resolution model for the identification of children's physical activity type, in order to create conditions for exerting the role of the type of physical activity in physical activity evaluation.Meanwhile, the model is applied to the self-designed program for the evaluation of children's physical activity, to be used for displaying the evaluation method of physical activity in which activity type, time and intensity were integrated.

  Methods  Open data sets of ten kinds of children's physical activity were used.Residual convolution neural network model was constructed and evaluated with Python 3.8 and Tensorflow 2.4. The model was applied to the self-designed program for physical activity evaluation for the output of the results of physical activity evaluation based on the activity type identification, activity count and intensity grade division results of the case.

  Results  The accuracy of residual convolution neural network model in distinguishing 9 types of children's physical activities(jump rope and stair up, etc.) is 99.3%, the accuracy in physical activity's case is 99.1%, and the time resolution is about 2.8 seconds.

  Conclusion  The high-accuracy and high-time resolution model plays a vital role in the identification of children's physical activity type of physical activity evaluation, which may help a more comprehensive, straight and accurate evaluation method of physical activity.