# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
## 4 Experiment and Results
### Experimental Results  
#### 4.4.1 Evaluation Study of Datasets  
In this study, to undertake a rigorous validation of the BSTCA-HAR model's performance, we trained and then tested it on datasets with different characteristics. As shown in Table 5, the BSTCA-HAR model performed excellently across all three datasets, particularly achieving an accuracy of 96.1% on the ISLD dataset. This demonstrates the model's high generalization capability in handling tasks with a wide range of activity types, including both static and dynamic activities. Overall, the model maintained high levels of accuracy, recall, and precision, all above 92%, indicating its ability to accurately recognize user activities. These results are based on data evaluation in the final testing phase, reflecting the model's performance on independent test sets after the training process. Figures 2-4 show the confusion matrices of the model on the test datasets, using heatmaps to clearly reveal the specific performance of the model in classification tasks. To distinguish the data representation, the horizontal axis depicts the forecasted outcomes, whereas the vertical axis mirrors the observed or genuine values. On the WISDM dataset, the model correctly classified 6612 instances. To highlight the concern regarding imbalanced datasets, it is crucial to recognize that they can potentially skew the model's predictions in favor of the prevalent class, resulting in a bias towards the majority group and leading to decreased performance on some minority classes (e.g., \"Downstairs\" and \"Upstairs\"). Despite these confusions, the overall performance of the model remains quite impressive, with an average accuracy of around 93%, indicating that the model can accurately classify activities into the correct categories in most cases. When evaluated on the UCI-HAR dataset, which includes approximately 2947 new instances, the model achieved an overall accuracy of around 92%, indicating a commendable performance. This demonstrates that the model can accurately classify human activities in most cases. However, despite the overall excellent performance, the model still shows  
Figure 4: Classification confusion matrix on ISLD. Each activity is represented by an abbreviation, with its full name as follows: Bd, Bending; Ig, Jogging; Bx, Boxing; Jm, Jumping; Wc, Watch-checking; Kc, Kicking; Ac, Crossing the arms; JP, Jumping in place; Gu, Getting up; Pt, Pointing; Hc, Clapping hands; Rn, Running; Ow, Waving one hand; Hs, Head scratching; Tw, Waving two hands; Sd, Sitting down; St, Standing; Wk, Walking. Figure 3: Classification confusion matrix on UCI-HAR. some tendency for misclassification between certain activities. For instance, there may be misclassifications between the activities \"Walking\" and \"Downstairs\" as these activities share similarities in human movement patterns, particularly in terms of changes in acceleration and angular velocity. When an individual walks, especially on uneven surfaces such as stairs or slopes, the rhythm of their steps and the tilt of their body can create periodic fluctuations similar to those observed when descending stairs. This similarity can cause the data recorded by accelerometers and gyroscopes to overlap between the two activities, increasing the difficulty for the model in distinguishing between them. Additionally, the model's performance on the \"Standing\" activity may also be influenced by other similar static activities such as \"Sitting\", as both are relatively static and lack significant dynamic changes. The sensor data might not have enough distinction to separate them, although this error is not very prominent; In the case of ISD gesture recognition, upon excluding the Null class from the classification tasks, our approach achieved an overall accuracy of around 96% on the task test. Notably, the ISD model achieved 100% accuracy in recognizing 14 specific behaviors with significant Doppler effects, such as bending and crossing the arms.