## 4 Experimental Results
### Experiment IV: Comparison with Available State-of-the-Art Techniques  
Considering all the datasets that we used, we compared the performance of the SSRDC system with other available SOTA methods. There are various human locomotion activity recognition algorithms that have been applied on MobiAct_v2.0, Real-World HAR, and Real-Life HAR. Table 6 advocates the fact that the proposed SSRDC system outperformed all of them by a good margin. In [67], they used a random forest model with two different modes of the system while working on the Real-World HAR dataset. In the first mode, the system was aware of the position of device on the body of subject; while in other mode, the system was unaware of the device position. In the position-unaware case, their system produced 80.2% accurate results, while in the position-aware case, they predicted locomotion activity with 83.4% accuracy. In [68], a model that was based on cross-subject activity recognition was used, and it scored an 83.1% accuracy. A work referenced at [69], used signal visualization alongside a CNN to produce 92.4% accurate results over Real-World HAR. Finally, another framework [70] that was designed over a Real-World HAR used a CNN and scored 93.8% accuracy. In comparison to these works, the proposed system attained 94.2% accuracy and left the others behind.  
Regarding MobiAct_v2.0, works referenced at [71; 72; 73] used SVM, CNN, and thresholding techniques to predict locomotion activities with 77.9%, 80.7%, and 81.3% accuracy. While our system proved to be 84.5% accurate. We also compared the performance of our system with state-of-the-art works over Real-Life HAR dataset. In [74], they utilized an SVM while working on different combinations of the sensors that are described in Table 6. While using an accelerometer and a GPS, they achieved 60.1% accuracy; with the addition of magnetometer to these two sensors, the accuracy increased to 62.6%; and while using gyroscope data at the same time, they obtained 67.2% accurate results. Another work that can be found at [38], employed an attention-based hybrid model that consisted of a CNN and an LSTM. They also experimented using different sensors. Using a magnetometer, they achieved 70.3% accuracy; with gyroscope data, they managed to obtain 95.2% accurate results; and they predicted with 95.7% accuracy when they used an accelerometer. Our SSRDC system produced 95.9% accurate results for the Real-Life HAR dataset. The accu  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Activity Classes** & \multicolumn{2}{c}{**K-Nearest Neighbors (KNN)**} & \multicolumn{2}{c}{**AdaBoost**} & \multicolumn{3}{c}{**Multilayer Perceptron**} \\ \hline
**Activities** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline WAL & 1.00 & 0.84 & 0.91 & 0.53 & 1.00 & 0.69 & 1.00 & 0.95 & 0.97 \\ INAC & 0.79 & 1.00 & 0.88 & 1.00 & 1.00 & 1.00 & 0.95 & 1.00 & 0.97 \\ ACT & 1.00 & 0.74 & 0.85 & 1.00 & 0.16 & 0.27 & 1.00 & 0.95 & 0.97 \\ DRI & 0.86 & 1.00 & 0.93 & 1.00 & 0.95 & 0.97 & 0.95 & 1.00 & 0.97 \\ Mean & 0.91 & 0.90 & 0.89 & 0.88 & 0.78 & 0.73 & 0.98 & 0.98 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of multilayer perceptron classifier with KNN and AdaBoost classifiers on Real-Life HAR in terms of precision, recall, and F1-score.  
racy of our system beat the work in [38] by a small margin. The major difference between both approaches was that we extracted hand-crafted features, selected the best featured, and then used a novel technique of power transformation for feature optimization, while they simply preprocessed the data and trained their deep learning model. Our approach evidently proved better than their approach on Real-Life HAR dataset.