# A CSI-Based Multi-Environment Human Activity Recognition Framework
## 5 Conclusions  
Determining what activities are being performed without hindering the movements of the subjects is now much easier due to the utilization of the overflowing Wi-Fi signals in almost all places. This work proposed an approach to determine the activity being performed, including fall and non-fall activities, by extracting and analyzing the information contained within the CSI values of the OFDM subcarriers associated with the exchanged Wi-Fi signals. All the experiments in this work were applied to real data that are acquired from real subjects. We also present a segmentation algorithm to determine the portion of the signal which contains the performed activity. By processing the acquired signals, before and after the segmentation process, we achieved high activity recognition rates. Furthermore, we have compared the performance of the proposed method with the performance of well-known state-of-the-art activity recognition systems. The experimental results show that a 91% overall activity recognition can be achieved using the proposed method which includes denoising and segmentation processes.  
Conceptualization, B.A.A. and R.A.; methodology, B.A.A. and R.A.; software, M.M.A.; validation, B.A.A. and M.M.A.; formal analysis, B.A.A. and M.M.A.; investigation, B.A.A. and M.M.A.; resources, B.A.A. and M.M.A.; data curation, M.M.A.; writing--original draft preparation, B.A.A.; writing--review and editing, B.A.A., R.A., S.A. and M.I.D.; supervision, B.A.A.; project administration, B.A.A.; funding acquisition, B.A.A. and R.A.; Visualization, B.A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jordan University of Science and Technology Grant No. 20180032.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Number of Subjects** & **Number of Environments** & **Number of Activities** & **Data Type** & **Classifier** & **Overall Activity Detection Accuracy** & **Fall Detection Accuracy** \\ \hline
[66] & Not Specified & 2 & 10 & CSI & CNN & Not specified & 90\% \\ \hline
[67] & 10 & 3 & 6 & CSI & RNN & 90\% & 93\% \\ \hline
[63] & 3 & 3 & 10 & CSI & SVM & Not specified & 93\% \\ \hline
[64] & 8 & 3 & 4 & CSI & SVM & Not specified & 90\% \\ \hline
[65] & 7 & 1 & 11 & CSI & CNN-SVM & 90.90\% & Not specified \\ \hline
[68] & 10 & 2 & 6 & CSI & CNN & 91.2\% & Not specified \\ \hline proposed approach & 20 & 2 & 6 & CSI & SVM & 91.27\% & 96.16\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparison with other existing approaches.  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of King Abdullah University Hospital (KAUH) and the Jordan University of Science and Technology (JUST) (19/110/2017).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are openly available in [Mendeley] at [10.17632/v38 wjmz6f6.2].  
**Conflicts of Interest:** The authors declare no conflict of interest.