2.9 Statistical methods
This work utilizes the Berkeley Multi-Channel Human Action Detection (MHAD) dataset to evaluate the effectiveness of the BPNN algorithm for action recognition. The MHAD dataset includes reference background and depth images, which are used to extract the first-layer feature vectors. The dataset consists of 11 different movements performed by a group of seven men and five women. Each movement is repeated five times, resulting in a total of 660 action sequences. The recording duration for the dataset is approximately 82 minutes. This dataset provides a valuable resource for training and evaluating the BPNN algorithm’s performance in action recognition tasks.The player training data is analyzed using IBM’s statistical software SPSS 26.0 (Statistical Product and Service Solutions 26.0), developed by IBM Corporation in New York, USA. In the analysis of the players’ basic situation, the data were presented using a percentile system. A single-factor analysis was conducted to examine the differences between different situations. In statistical analysis, a commonly used threshold for determining statistical significance is a p-value of less than 0.05. If the calculated p-value is less than 0.05, it indicates that the observed difference between the groups is statistically significant. In other words, there is a significant difference between the situations being compared.