# Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
## 5 Discussion and Conclusions
Firstly, the number of parameters and the size of the model are five to eight times smaller than the previous state-of-the-art model, for instance 1.383 million model parameters instead of 10.092 million for a window size of 200. The throughput is also improved, since it is two to three times higher, namely 405 inferences per second compared to 206.88 of the previous state-of-the-art and a window size of 50. Moreover, the classification performance is better and more robust, since both models are tested on two datasets and 10-fold cross-validation is also used. Indicatively, the average accuracy on the 10-fold cross-validation on the WISDM and Actitracker datasets for a window size of 200 is 94.18% and 79.12%, 0.5% and 2% higher than the previous state-of-the-art. This means that this solution is better in terms of size, throughput, and performance, all of which are crucial for a real-time on-device application, as can be seen in Figure 9. As an additional contribution, we investigated the impact of the statistical features in order to validate their importance and offer a comparison between the 40 statistical features and the ones created using PCA. Among the various limitations acknowledged in this work, certain factors have been identified as fields of further research to be pursued towards improving the results of the proposed system. Even deeper convolutional neural network structures can be explored, but the size of the network \begin{table}
\begin{tabular}{c c c} \hline  & **DCNN** & **RCNN** \\ \hline Convolutional Layer 1 & 0.007 M & 0.009 M \\ Convolutional Layer 2 & 0.22 M & - \\ Fully-connected layer & 1.15 M & 10.08 M \\ Softmax layer & 0.003 M & 0.006 M \\ Total & 1.38 M & 10.095 M \\ \hline \end{tabular}
\end{table} 
Table 15: Parameters per layer of the convolutional neural networks. needs to always be in check. A probable solution would be to also use \(1\times 1\) convolutions. Another convolutional neural network with a higher receptive field could also be an alternative for substituting the global features. Finally, an implementation that also uses the gyroscope data would be interesting. **Author Contributions:** Conceptualization, K.P., A.C.T. and S.K. ; methodology, K.P. ; software, K.P. ; validation, K.P. and S.K. ; formal analysis, K.P. ; investigation, K.P. ; data curation, K.P. ; writing, original draft preparation, K.P. ; writing, review and editing, A.C.T. and S.K. ; project administration, A.C.T., S.K. and D.T. ; resources, D.T. ; funding acquisition, D.T. All authors read and agreed to the published version of the manuscript. **Funding:** This work was co-funded by the European Union and the General Secretariat of Research and Technology, Ministry of Development & Investments, under the project STI4 Energy of the Bilateral S&T Cooperation Program Greece--Germany 2017. This support is gratefully acknowledged. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.