# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 5 Conclusions  
This study proposes a wearable device-based model for evaluating horse activity levels, optimally capturing activity level changes with a window size of 26. By using combined acceleration threshold analysis, an accuracy of 92.74% was achieved. Experiments on new datasets validated the feasibility of the model. This method provides a practical solution for resource-constrained wearable devices, enabling convenient monitoring of horse activity levels and recording activity durations, offering new analytical perspectives. Although slightly less accurate than more complex deep learning methods, its simplicity, efficiency, and ease of deployment make it a practical monitoring solution. Future research should focus on developing more adaptive algorithms, integrating more sensor data, and employing advanced machine learning methods to improve the model's accuracy and applicability. Conceptualization, X.C., W.Z. and L.G. ; methodology, X.C. and F.L. ; software, X.C. and F.L. ; validation, L.G. and W.Z. ; formal analysis, L.G. ; investigation, Q.F. and J.L. ; resources, L.G. and W.Z. ; data curation, J.L. and Q.F. ; writing--original draft preparation, X.C. and L.G. ; writing--review and editing, P.K. and L.G. ; visualization, X.C. and P.K. ; supervision, L.G. and F.L. ; project administration, W.Z. and L.G. ; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Key R&D Program of Xinjiang Uygur Autonomous Region (2022B02027, 2023B02013). All procedures were approved by the Xinjiang Institute of Animal Husbandry and Veterinary Sciences for experimental animal welfare and ethics (IAHQS2022-027, Approval Date: 29 June 2022). **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy. **Acknowledgments:** We would like to express our gratitude to Xinjiang Wild Horse Culture Development Co., Ltd., as well as the support from [PERSON] and [PERSON], for providing the experimental facilities and assisting in data acquisition. **Conflicts of Interest:** The authors declare no conflicts of interest. **Appendix A**```
acc_data: Array of combined accelerations samplingQuantity: Data collection window function main() { if(Interval_notation (acc_data)==0){//Interval counting method for analysis return0//low activity } mean = calculate_mean(acc_data, samplingQuantity) variance = calculate_variance(acc_data, samplingQuantity, mean) returndetermine_behavior(variance) } // Variance analysis method for analysis function determine_behavior(variance){ if(variance <= 1.2){ return0//low activity } elseif(variance <= 29){ return1//medium activity } else return2//high activity } //Variance-Based Proximity Classification int*high_activity_predict(floatX_train[][],inty_train[],inttrain_size,floatX_test[],inttest_size,intk,intp) { int*y_pred=malloc(test_size*sizeof(int)); for(inti=0;i<test_size;i++){ floatdistances[train_size]; for(intj=0;j<train_size;j++){ distances[j]=minkowski_distance(X_test[i],X_train[j],p); } //Sort distances and select the k nearest labels int*k_nearest_labels=find_k_nearest_labels(distances,y_train,k); y_pred[i]=most_common_label(k_nearest_labels,k); free(k_nearest_labels); } returny_pred; }
```

**Algorithm A1: Combined Acceleration Threshold Analysis**