## 3 Results
### Feature Selection  
Investigation regarding the effect each variable has on the performance of the model was also conducted. The accelerometer, gyroscope, and magnetometer data were used both individually and combined, in order to compare with the result, the model achieved when all variables were utilized. Next, the accuracy of each approach was measured along with its error, and the results are presented in Table 7.  
As can be deduced from Table 7, the combination of all sensors performed better as compared to the case where each sensor was used individually. This was an expected result that has been highlighted by several relative studies, such as [24; 68]. On the other hand, considering the usage of a single sensor, gyroscopes appear to slightly outweigh accelero  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Activity** & **Precision** & **Recall** & **F1-Score** \\ \hline Standing & 0.887 & 0.919 & 0.902 \\ Walking (without crate) & 0.904 & 0.969 & 0.937 \\ Bending & 0.741 & 0.790 & 0.763 \\ Lifting crate & 0.888 & 0.788 & 0.833 \\ Walking (with crate) & 0.834 & 0.897 & 0.864 \\ Placing crate & 0.860 & 0.710 & 0.777 \\
**Accuracy** & & 0.856 \\
**Macro average** & 0.853 & 0.843 & 0.845 \\
**Weighted average** & 0.860 & 0.856 & 0.854 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification report for the proposed model’s performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Accelerometer (x,y,z)**} & \multirow{3}{*}{**Gyroscope (x,y,z)**} & \multirow{3}{*}{**Magnetometer (x,y,z)**} & \multirow{3}{*}{**Accuracy (\(\pm\)Error)**} \\ \hline \multirow{3}{*}{x} & & & & 82.708\% (\(\pm\)1.468) \\  & & & & 82.987\% (\(\pm\)1.380) \\ \cline{1-1}  & & & x & 73.412\% (\(\pm\)2.180) \\ \cline{1-1} \cline{2-6} \multirow{3}{*}{x} & \multirow{3}{*}{x} & & 83.619\% (\(\pm\)0.877) \\ \cline{1-1} \cline{2-6}  & & & x & 81.200\% (\(\pm\)1.261) \\ \cline{1-1} \cline{2-6}  & & & x & 85.532\% (\(\pm\)0.604) \\ \cline{1-1} \cline{2-6} \multirow{3}{*}{x} & \multirow{3}{*}{x} & & x & 85.677\% (\(\pm\)0.972) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Feature selection for the model’s variables and their respective accuracies.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Confusion Matrix**} & \multirow{3}{*}{**Standing**} & \multirow{3}{*}{**Walking (Without Crate)**} & \multirow{3}{*}{**Bending**} & \multirow{3}{*}{**Lifting Crate**} & \multirow{3}{*}{**Walking (With Crate)**} & \multirow{3}{*}{**Placing**} \\ \cline{3-3} \cline{5-6}  & & Standing & **135** & 5 & 0 & 0 & 1 & 7 \\ \cline{2-6} \multirow{3}{*}{**F1-score**} & Walking (without crate) & 2 & **227** & 5 & 0 & 0 & 0 \\ \cline{2-6} \multirow{3}{*}{**F1-score**} & Bending & 1 & 16 & **101** & 9 & 1 & 0 \\ \cline{2-6} \multirow{3}{*}{**F1-score**} & Lifting crate & 1 & 1 & 30 & **165** & 11 & 0 \\ \cline{2-6} \multirow{3}{*}{**F1-score**} & Walking (with crate) & 1 & 1 & 11 & **215** & 0 \\ \cline{2-6} \cline{2-6}  & Placing crate & 12 & 0 & 0 & 0 & 31 & **106** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The confusion matrix for the present model’s performance.  
eters, demonstrating an accuracy of 82.987% and 82.708%, respectively. Concerning the magnetometer, it was observed to have the poorest performance, while its supplemental usage, as part of a case considering only two types of sensors, is suggested only in combination with a gyroscope, leading to approximately 3.07% increase of the accuracy. In contrast, the synergy of accelerometers and gyroscopes resulted in approximately 1.10% and 0.76% increase of the accuracy as compared to the purely usage of accelerometers and gyroscopes, respectively.