# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 3 Results
### Validation of the Trained Models  
To validate the accuracy of the combined acceleration threshold analysis model on new data, this study collected 20,000 data points for verification experiments. By comparing the accuracy of the trained models in actual application stages, the following results were found (Figure 10): the interval counting method achieved an accuracy of 82.49% on new data and 87.55% for the trained model; the variance analysis method achieved an accuracy of 90.13% on new data and 90.87% for the trained model; the proximity classification method achieved an accuracy of 91.55% on new data and 91.27% for the trained model; and the threshold analysis method achieved an accuracy of 92.71% on new data and 92.74% for the trained model.  
Figure 9: Comparing the correlation between the data collection window and the classification accuracy in (**a**) Stage 1: combined acceleration interval counting method and (**b**) Stage 2: combined acceleration variance analysis method.  
The threshold analysis method combines the advantages of the three stages to achieve precise predictions of horse activity levels. Although the accuracy on new data slightly decreased, it still remained at 92.71%, almost consistent with the 92.74% accuracy of the trained model. This indicates that the threshold analysis method not only performs well during the training phase but also exhibits high robustness and reliability in practical applications.  
In this study, horse activity levels were analyzed using the combined acceleration threshold analysis model and compared with the KNN model (Figure 11). The results indicate that the overall accuracy of the combined acceleration threshold analysis model is 92.91%, slightly higher than the 92.31% of the KNN model (Figure 11c). Specifically, the combined acceleration threshold analysis model performed better in predicting categories 0 and 1, with accuracies of 95.03% and 92.55% (Figure 11a), respectively. In contrast, the KNN model excelled in predicting category 2, achieving an accuracy of 98.06% (Figure 11b). Although the combined acceleration threshold analysis model exhibited some misclassification in category 2 (8.91% misclassified as category 1), it had lower misclassification rates for categories 0 and 1, at 4.76% and 3.76%, respectively. In comparison, the KNN model had a higher misclassification rate in category 1, with 11.11% misclassified as category 2.  
Compared to existing methods, the interval counting method and variance analysis method achieved accuracies of 82.49% and 90.13%, respectively, on new data, both lower than those of the combined acceleration threshold analysis model and the KNN model. The combined acceleration threshold analysis model, by leveraging the advantages of multiple  
Figure 11: (**a**) Confusion matrix of the combined acceleration threshold analysis model, (**b**) confusion matrix of the KNN model, (**c**) accuracy comparison between the two models.  
Figure 10: Comparison of the accuracies between the interval counting, variance analysis, proximity classification, and combined threshold analysis methods on both training models and practical applications.  
stages, achieved higher accuracy and stability, making it suitable for wide application. Although the KNN model performed exceptionally well in certain categories, its overall balance was slightly inferior.