## 6 Results and Discussion
### Interpretation of Results  
The bar plots depicted in Figure 29 provide a concise overview of the findings discussed in Section 6.3. Notably, SVM demonstrated the highest accuracy when employing LDA for dimensionality reduction, achieving an accuracy rate of 86.98% across 215 samples. The GRABMyo dataset presents several distinctive features and applications compared to other surface EMG datasets, which significantly contribute to the current state of the art in EMG analysis. One of the main differences lies in the variety and complexity of gestures included in the GRABMyo dataset. The dataset encompasses a wide range of gestures with varying degrees of complexity, enabling the development of models that generalize better across different types of movements. In contrast, other EMG datasets may focus on a limited set of gestures, which can be sufficient for specific applications but less effective for creating general-purpose models. Another notable difference is the diversity of participants in the GRABMyo dataset. It includes data from many participants with diverse backgrounds, providing a robust training set for machine learning models. Other EMG datasets might have fewer participants or lack diversity, which can limit the models' applicability to broader populations. Additionally, the recording conditions in the GRABMyo dataset are varied, encompassing different muscle contraction levels, speeds, and environments. This  
Figure 28: Confusion matrix for the SVM model using data from the complete dataset of three sessions of data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation Metrics** & \multicolumn{3}{c}{**Classes (Five Hand Gestures)**} & \multicolumn{3}{c}{**Macro Average**} \\ \hline \multicolumn{6}{c}{**Support Vector Machine (Three Sessions)**} \\ \hline True Positive & 94 & 100 & 98 & 105 & 98 & 99 \\ False Positive & 20 & 32 & 42 & 17 & 39 & 30 \\ False Negative & 35 & 29 & 31 & 24 & 31 & 30 \\ True Negative & 496 & 484 & 474 & 499 & 477 & 486 \\ Precision & 0.82456 & 0.75758 & 0.7 & 0.86066 & 0.71533 & 0.77162 \\ Sensitivity & 0.72868 & 0.77519 & 0.75969 & 0.81395 & 0.75969 & 0.76744 \\ Accuracy & 0.91473 & 0.90543 & 0.88682 & 0.93643 & 0.89147 & 0.90698 \\ F-measure & 0.77366 & 0.76628 & 0.72862 & 0.83665 & 0.73684 & 0.76841 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics of the LDA for machine learning models, KNN, NB, and the SVM, using the full dataset comprising data from three sessions. variety helps in developing more resilient models, whereas other EMG datasets might be recorded under more controlled conditions, making their models less adaptable to real-world variations. The quality and annotation detail of the GRABMyo dataset also stand out, emphasizing high-quality signal recordings and detailed annotations that are crucial for precise machine learning training and validation. In contrast, the quality and annotation detail in other EMG datasets can vary, impacting the ease of use and accuracy of the models trained on them. In terms of applications, the GRABMyo dataset is highly suitable for developing advanced prosthetic control systems that need to recognize a wide range of gestures and adapt to different users. It is also useful for designing rehabilitation protocols that require detailed monitoring of various muscle activities and for creating human-computer interfaces requiring complex gesture recognition and adaptability to different users. Other EMG datasets might be more appropriate for specific types of prosthetics, simpler control systems, targeted rehabilitation exercises, or interfaces requiring fewer gestures. Figure 30 bar plots illustrate a comparison between the results obtained from the LDA-SVM model for a single session and three sessions. The model exhibited an enhancement in accuracy by 3.72%, rising from 86.97% to 90.69% as the sample size increased to 645. Figure 30: Comparing performance metrics for SVM classifiers utilizing data from one session and the complete dataset of three sessions of data.