# Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
## 4 Results
### Performance Evaluation on the Actitracker Dataset  
The Actitracker dataset was gathered in an uncontrolled environment, in contrast to the WISDM dataset, but has the same data structure, and thus, the same model structure can be used. Consequently, the model size and throughput will be the same, which means that another online \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average Accuracy** & **Standard Deviation** \\ \hline  & **50** & **200** & **50** & **200** \\ \hline RCNN [35] & 89.94\% & 93.68\% & 2.93\% & 1.62\% \\ DCNN & 90.28\% & 94.18\% & 2.48\% & 2.15\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Average accuracy scores and their standard deviation of the 10-fold cross-validation of the WISDM dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Downstairs** & **Jogging** & **Sitting** & **Standing** & **Upstairs** & **Walking** \\ \hline Downstairs & 1167.9/75.1\% & 55.5/3.6\% & 0.6/\textless{}0.1\% & 2.3/0.1\% & 137.3/8.8\% & 191.4/12.3\% \\ Jogging & 24.1/0.5\% & 4410.1/97.0\% & 0/0.0\% & 0/0.0\% & 19.1/0.4\% & 92.7/2.0\% \\ Sitting & 0/0.0\% & 0/0.0\% & 813/82.0\% & 133.3/13.4\% & 45.7/4.6\% & 0/0.0\% \\ Standing & 0.3/\textless{}0.1\% & 0/0.0\% & 27.8/3.2\% & 815.4/93.4\% & 28.9/33.1\% & 0.6/\textless{}0.1\% \\ Upstairs & 179.7/10.6\% & 199.2/11.8\% & 12.4/0.7\% & 7.3/0.4\% & 1180.9/69.7\% & 115.5/6.8\% \\ Walking & 96.6/1.6\% & 40.5/0.7\% & 0/0.0\% & 0/0.0\% & 116.8/1.9\% & 5838.1/95.8\% \\ \hline  & & **200** & & & & \\ \hline  & **Downstairs** & **Jogging** & **Sitting** & **Standing** & **Upstairs** & **Walking** \\ \hline Downstairs & 1176.3/88.7\% & 2.4/0.2\% & 0/0.0\% & 0/0.0\% & 50.7/3.8\% & 96.6/7.3\% \\ Jogging & 1/\textless{}0.1\% & 4325.1/97.8\% & 0/0.0\% & 0/0.0\% & 10.2/0.2\% & 87.7/2.0\% \\ Sitting & 0/0.0\% & 0/0.0\% & 775/82.6\% & 151.2/16.1\% & 11.8/1.3\% & 0/0.0\% \\ Standing & 0/0.0\% & 0/0.0\% & 46.5/5.8\% & 755.7/93.5\% & 5.8/0.7\% & 0/0.0\% \\ Upstairs & 95/6.5\% & 131.8/9.0\% & 6.9/0.4\% & 0/0.0\% & 1137/77.8\% & 91.3/6.2\% \\ Walking & 48.2/0.8\% & 0.8/\textless{}0.1\% & 0/0.0\% & 0/0.0\% & 43.1/0.7\% & 5869.9/98.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Confusion matrix of the original train/test split of the WISDM dataset. Cross-Validation  
Consistent with previous results, the average accuracy of the model in Table 11 is considerably higher for both window sizes. It validates that the performance of the model is better regardless of the specific dataset being used. The increase in accuracy ranges from almost 1% to 2%. The standard deviation of the accuracies is quite high due to the varying difficulty of the dataset splits, which means that we cannot know with certainty that this will be the actual accuracy produced by the networks for an arbitrary split of the dataset. However, it is quite expensive to re-run a 10-fold cross-validation with different data splits for a dataset as big as Actitracker, and more importantly, the comparison of the networks was on the same dataset splits; this is enough to prove the superiority of DCNN. In Figure 5, there is an overview of the precision scores of the models. Both suffer greatly in the Stairs activity, but the model has considerably higher score. They also perform poorly in the Lying Down activity, and the model is a bit worse than the reference one. In the rest of the activities, their performance is quite comparable. The recall scores in Figure 6 indicate that the models perform poorly in the Stairs and Lying Down activities regarding this metric as well. However, the model achieves an improvement of around 300% compared to the scores of the reference one. Regarding the rest of the activities, the results are similar. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Average Accuracy** & \multicolumn{2}{c}{**Standard Deviation**} \\ \hline  & **50** & **200** & **50** & **200** \\ \hline RCNN [35] & 75.43\% & 77.10\% & 9.45\% & 10.76\% \\ DCNN & 76.01\% & 79.12\% & 10.20\% & 8.60\% \\ \hline \hline \end{tabular}
\end{table}