# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
## 4 Experiment and Results
### Experimental Results
#### 4.4.2 Experimental Results of the Suggested Model Using LOSO Cross-Validation  
To further validate the model's performance, we conducted a more comprehensive evaluation using LOSO (Leave-One-Subject-Out) cross-validation. This method involves using data from one subject for testing while the remaining subjects' data are used for training. This approach simulates a realistic application scenario, providing a more rigorous evaluation. Table 6 presents the performance of various models using LOSO cross-validation on the WISDM dataset, which includes data from 36 subjects performing six activities. The training set comprises data from 30 subjects, while the test set includes data from the remaining six subjects.  
We compared the proposed model with the traditional HAR model and examined the accuracy of activity recognition. In this comparative evaluation, we consider the following approaches: \"Ensemble of AE\"; \"Bidirectional LSTM\"; \"CNN-LSTM \"; \"LSTM-CNN\"; \"Convolutional AE\"; \"CNN + Handcrafted features\"; and two shallow machine learning methods, RF and SVM. We performed a five-fold cross-validation by taking the mean and standard deviation of all accuracy measures to assess the accuracy of the model at a 95% confidence level.  
The following conclusions can be drawn. Regarding accuracy, although each baseline model has its merits, the proposed method demonstrates certain advantages. Specifically, while Ensemble of AE (88.01%) and Bidirectional LSTM (89.08%) effectively processed the data and exhibited competitiveness, their average accuracy confidence intervals were relatively wide, indicating potential instability in the results and a limited upper accuracy bound. The hybrid CNN and LSTM models and the Convolutional AE model, although  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **95\% Confidence Interval** & **Accuracy** \\ \hline RF [30] & (0.8191, 0.8228) & 86.23\% \\ SVM [31] & (0.8592, 0.8647) & 89.08\% \\ Ensemble of AE [32] & (0.7407, 0.7511) & 88.01\% \\ Bidirectional LSTM [33] & (0.8896, 0.8997) & 89.08\% \\ CNN-LSTM [34] & (0.8703, 0.8845) & 88.01\% \\ LSTM-CNN [8] & (0.9095, 0.9154) & 91.34\% \\ Convolutional AE [35] & (0.8997, 0.9058) & 90.28\% \\ CNN + Handcrafted features[36] & (0.8609, 0.8712) & 86.75\% \\ Proposed & (0.9245, 0.9391) & 93.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: LOSO cross-validation for model performance evaluation on the WISDM dataset.  
theoretically strong in feature extraction, had wider confidence intervals, suggesting slightly lower performance stability and reliability. The CNN model combined with handcrafted features (86.75%) was also limited by the quality and generalization ability of feature engineering, with its confidence interval reflecting dependency on specific datasets. While shallow machine Learning methods such as Random Forest (86.23%) and SVM (89.08%) showed good stability and reliability, their wide confidence intervals in terms of accuracy indicate potential limitations when dealing with highly variable datasets.  
In contrast, the proposed method not only achieved an average accuracy of 93.18% but also had a confidence interval of [0.9245, 0.9391], demonstrating high consistency and stability. In summary, the proposed method not only achieves a significant improvement in accuracy, it also demonstrates superior performance in terms of stability and generalization ability.