# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 4 Discussion  
Table 18 summarizes the results obtained after all previously discussed experiments. Thus, 12 different experiments with respect to the different combinations of sensors and tests were used to analyze the effect of data imputation and data normalization, along with different combinations of sensors, as illustrated in Table 18. These results are presented in Figures 5-7, based on the sensors combinations, i.e., accelerometer (Ac) only, accelerometer and magnetometer (Ac + Mg), and accelerometer, magnetometer and gyroscope (Ac + Mg + Gy). Similarly, Figure 6 shows the results when utilizing the accelerometer and magnetometer (Ac + Mg) sensors values to test the trained deep learning model with respect to four scenarios of data normalization and data imputation combinations. Each scenario is evaluated across four performance metrics, i.e., accuracy, precision, recall, and F-measure. It can be noticed that the deep learning model performance across all metrics is highest with the application of normalization and imputation on the given dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario** & **Sensors** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline \multirow{4}{*}{Non-Normalized, Non-imputed} & Ac & 22.9 & 19.65 & 22.9 & 21.15 \\ \cline{2-6}  & Ac + Mg & 40.69 & 56.4 & 40.69 & 47.27 \\ \cline{2-6}  & Ac + Mg + Gy & 74.46 & 78.24 & 74.46 & 76.3 \\ \hline \multirow{4}{*}{Non-Normalized, Imputed} & Ac & 20 & 20 & 20 & 20 \\ \cline{2-6}  & Ac + Mg & 20.1 & 73.34 & 20.1 & 31.55 \\ \cline{2-6}  & Ac + Mg + Gy & 20.19 & 60.02 & 20.19 & 30.22 \\ \hline \multirow{4}{*}{Normalized, Non-imputed} & Ac & 85.89 & 86.21 & 85.89 & 86.05 \\ \cline{2-6}  & Ac + Mg & 86.49 & 86.75 & 86.49 & 86.62 \\ \cline{2-6}  & Ac + Mg + Gy & 89.52 & 89.74 & 89.51 & 89.62 \\ \hline \multirow{4}{*}{Normalized, Imputed} & Ac & 94.56 & 94.63 & 94.56 & 94.59 \\ \cline{2-6}  & Ac + Mg & 98.24 & 98.28 & 98.24 & 98.26 \\ \cline{2-6}  & Ac + Mg + Gy & 99.82 & 99.82 & 99.82 & 99.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Summarized results of all previous experiments. Figure 5: Performance of deep learning model when utilizing only accelerometer data to classify the human daily living activities across four data normalization and data imputation scenarios. Likewise, Figure 7 displays the results when utilizing all three sensors data--i.e., accelerometer, magnetometer, and gyroscope--to test the trained deep learning model with respect to four scenarios of data normalization and data imputation combinations. Each scenario is evaluated across four performance metrics, i.e., accuracy, precision, recall, and F-measure. It can be observed that the deep learning model performance across all metrics is highest with the application of normalization and imputation on the given dataset. As results of this study, it was verified that the pattern of the imputed data is similar to the original data. However, its frequency and amplitude are higher than in the original data. Regarding the data classification, depending on the number of sensors, the accuracy was between 22.9% and 74.46% for non-normalized data, and between 85.89% and 89.51% for normalized data. After the data imputation  
Figure 6: Performance of deep learning model when utilizing accelerometer and magnetometer data to classify the human daily living activities across four data normalization and data imputation scenarios. Figure 7: Performance of deep learning model when utilizing accelerometer, magnetometer, and gyroscope data to classify the human daily living activities across four data normalization and data imputation scenarios. process, depending on the number of sensors, the accuracy changed to between 20.00% and 20.19% for non-normalized data, and between 94.56% and 99.82% for normalized data. In summary, all the experimental results depict that the deep learning model better distinguishes daily living activities when both data normalization and data imputation techniques were applied.