# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 5 Conclusions  
This work has presented a simple testbed approach to multiple IoT sensor data gathering and emergency detection using machine learning algorithms. Three sensors were used to collect vibration, breathing, movement, and object distance data. The dataset  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**CNN and ML Models** & **Vibration** & **Movement** & **Breathing** & **RP-LIDAR Dataset** \\ \hline SVM & 57.03\% & 96.75\% & 100\% & 100\% \\ LR & 88.42\% & 97.54\% & 100\% & 100\% \\ NB & 78.87\% & 97.65\% & 100\% & 100\% \\ KNN & 94.20\% & 98.60\% & 100\% & 100\% \\ CNN & 99.45\% & 99.78\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model accuracy summary.  
Figure 14: Training and validation loss of the CNN model.  
was collected using the facilities of Daegu Mechatronics and Materials Institute in Korea. The collected dataset were processed and used for the detection and classification of human activity for emergency response. The data comprised of breathing, movement, falling, vibration and normal activities in the smart factory environment. The availability of the dataset will further enrich the research works in this area where data is needed. In addition, various ML algorithms were compared to have a fair view of the best candidate. Results showed that a simple CNN algorithm outperformed other ML algorithms, such as KNN, SVM, LR, and NB, in terms of classification accuracy. It is our future research objective to explore the possibility of using ensemble neural networks over traditional ML. Ensemble neural network can help in enhancing the accuracy of the detection and classification of the vibration and movement data. Improving the data fusion performance by reducing the difference between fused decisions and observations using high-dimensional space to map the correlation of complex relationships between the fused decisions is also a future research direction.  
Conceptualization, formal analysis, investigation, data curation, writing--original draft preparation, methodology, software, C.I.N., F.B.I., M.P.M., and J.-M.L.; validation, C.I.N., J.-M.L., and D.-S.K.; writing--review and editing, C.I.N., and J.-M.L.; visualization, supervision, resources, project administration, funding acquisition, J.-M.L. and D.-S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2018R1A6A1A03024003). It is also supported by National Research Foundation of Korea (NRF) grant funded by Korea government (MSIT) (2019R1F1A1064055) and also supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2020-2020-0-01612) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).  
Not Applicable.  
The data and codes presented in this study are available on request from the corresponding author. This is according to the laboratory rules.  
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, or in the decision to publish the results.