## 5 Discussion  
In the proposed SSRDC framework, we extracted different features from denoised smartphone sensor data. Then, we performed feature selection; power-transformed the chosen features; and using Multilayer Perceptron, performed the classification. To get started, the proposed system needs the smartphone sensor data. For this purpose, we chose three challenging RS-HLAR datasets that depict real-life scenarios and provide data from various sensors. The very first step of our system was to denoise the data using a Chebyshev type-I filter. From the denoised data, we extracted a total of 15 features including statistical features from the time domain and frequency domain and other features. We rejected the four least informative features with the help of LFE and LS-based feature selection. As a result, we determined the 11 most informative features. These 11 features were selected because a set of these 11 features provided the best accuracy. If we added or eliminated any feature in this set, it had a negative effect on the system's performance. Raw data produces bad results, so, we employed power transform for data optimization. Finally, we labeled the data and sent it to a multilayer perceptron (MLP) for classification. After tuning the hyperparameters of the MLP, we achieved excellent classification results.  
Although the system classifies human locomotion activities very well, it still has some limitations. When the system has to detect locomotion activities that are a combination of multiple locomotion activities, it finds the task more difficult as compared to the recognition of a single locomotion activity. An example of such a combination of locomotion activities can be a person performing acrobatics in which they rapidly go from one position to another. As the SSRDC system operates on remotely sensed data, there is a great chance of signal distortion, which can result in complete loss of useful data in some cases. The failure cases of the SSRDC system are picturized in Figure 15.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author/Method** & \multicolumn{3}{c}{**Mean Accuracy \%**} \\ \hline  & **MobiAct\_v2.0** & **Real-World HAR** & **Real-Life HAR** \\ \hline RF + Position Unaware [67] & - & 80.2 & - \\ Personalized Cross-Subject [68] & - & 83.1 & - \\ RF + Position Aware [67] & - & 83.4 & - \\ Signal Visualization + CNN [69] & - & 92.4 & - \\ Iss2 Image + CNN [70] & - & 93.8 & - \\ Support Vector Machine [71] & 77.9 & - & - \\ CNN [72] & 80.7 & - & - \\ Threshold [73] & 81.3 & - & - \\ SVM + Acc. + GPS [74] & - & - & 60.1 \\ SVM + Acc. + GPS + Magn. [74] & - & - & 62.6 \\ SVM + Acc. + GPS + Magn. + Gyro. [74] & - & - & 67.2 \\ Att-CNN-LSTM + Magn. [38] & - & - & 70.3 \\ Att-CNN-LSTM + Gyro. [38] & - & - & 95.2 \\ Att-CNN-LSTM + Acc. [38] & - & - & 95.7 \\ \hline
**Proposed** & **84.5** & **94.2** & **95.9** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of proposed SSRDC system with available state-of-the-art methods.  
The results and analysis of the proposed system are as follows. The mean accuracy of the proposed system over Real-World HAR was 94.16%, while a previous implementation that include position-unaware and position-aware system modes with a random forest model [67] achieved 80.2% and 83.4% accuracy for each modes, respectively. SSRDC system's mean accuracy over MobiAct_v2.0 was 84.49%, while the approaches used in [71; 72; 73] show a mean accuracy of 77.9%, 80.7%, and 81.3%, respectively. Finally, the techniques used over Real-Life HAR in [38; 74], scored 70.3% and 95.7% respectively in terms of accuracy, while our system gave up to 95.89% accurate results. More detailed comparisons can be found in Table 6. These statistics show that the proposed system outperforms the available state-of-the-art techniques.