# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
## 4 Experiment and Results
### Experimental Results
#### 4.4.3 Comparative Evaluation Using Various Network Architectures  
As shown in Table 7, using six model structures (A, B, C, D, E, and BSTCA-HAR) devised specifically for the purpose of experimental analysis and comparisons, we examined the effects of various network structures on the overall efficacy and functionality of the model. This evaluation of the classification outcomes focuses on two crucial aspects: first, the calibration and optimization of model parameters; and second, computation of the F\({}_{1}\)-Score on the designated test set, offering an alternative and thorough assessment of model performance. These evaluations were conducted on the WISDM test set. Using the conventional CNN architecture, Model A achieved an F\({}_{1}\)-Score of 82.10% on the WISDM test set. With almost 502,000 parameters, primarily in the dense layer, its performance is expensive and needs 1681 milliseconds of training time per round. Notwithstanding its correctness, Model A's quantity of parameters and training duration are significant drawbacks. In Model B, time series functionality is introduced. The data are initially fed into a two-layer LSTM and subsequently processed by a convolutional component for the purpose of extracting salient features. This approach is necessitated by the fact that activity recordings captured by mobile sensors inherently consist of time series data where actions evolve dynamically over time; the LSTM architecture deptly captures these temporal nuances. The fully connected layer that comes after the convolutional layers in Model B is swapped out with a GAP layer. By applying the GAP layer to every feature map, this replacement normalizes the overall network topology and streamlines the model, resolving the overfitting problem. Notably, Model B has 34,438 parameters, which is a considerable drop of 93.15% compared to Model A. Despite this substantial parameter reduction, the model's efficiency remains virtually unchanged, suggesting that the combination of LSTM for capturing temporal information and the GAP layer is both feasible and effective. Model C incorporates a BN layer subsequent to the GAP layer, aiming to normalize the output from a previous layer and accelerate model convergence. Furthermore, in order to lessen the computational and memory load, the classic max-pooling layer is employed to shrink the spatial dimensions of the feature maps; nevertheless, this results in the loss of some information, particularly significant features that could be lost when working with time series data. For more effective feature extraction and temporal modeling, Model D \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & \multicolumn{4}{c}{**Building Blocks**} & & & **Params** & **F\({}_{1}\)-Score** \\ \hline A & Conv\_1 & Max\_pooling & Conv\_2 & Flatten & Dense & Output & 502,726 & 82.10\% \\ B & LSTM\_1 & Conv\_1 & Max\_pooling & Conv\_2 & GAP & Output & 34,438 & 82.48\% \\ C & LSTM\_1 & Conv\_1 & Max\_pooling & Conv\_2 & GAP & BN & Output & 41,286 & 84.05\% \\ D & LSTM\_1 & Conv\_1 & Residual\_Block & & GAP & BN & Output & 34,598 & 88.82\% \\ E & LSTM\_1,LSTM\_2 & Conv\_1 & Residual\_Block & ATT & GAP & BN & Output & 35,527 & 92.84\% \\ Proposed & Bi-LSTM & Conv\_1 & Residual\_Block & ATT & GAP & BN & Output & 34,631 & 93.17\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluating the efficacy of different network architectures on the WISDM dataset. substitutes a TCN layer with three residual blocks for the max-pooling layer. In addition to preserving more time series data and features, replacing the max-pooling layer with the TCN layer greatly enhances the model's accuracy and performance, with Model D surpassing Model C by an average of 4.77%. Model E builds on the foundation of Model D by adding another LSTM layer and an attention mechanism, further enhancing the model's feature extraction capabilities, dynamic weight allocation, and ability to capture long-term dependencies. In this paper, our model is based on Model E, utilizing the characteristics of BiLSTM.