# Cascading Pose Features with CNN-LSTM for Multiview Human Action Recognition
## 6 Conclusions  
The present research in HAR has been aimed at addressing the issues of complexity and this can be witnessed through the endeavor of the previous works. In this paper, we proposed a method that extracts the 2D skeleton data from the 2D RGB data using the OpenPose technique, and classifies the given action using a proposed deep learning based CNN-LSTM model. As a result, our proposed method significantly decreases the complexity of computation by reducing the feature dimension. In this context, the proposed method was compared with state-of-the-art methods, and the outcomes confirm the potential of our technique. In future, the proposed approach will be used in different applications such as ambient assisted living, patients with neurocognitive disorder i.e., Parkinson's Disease to monitor their daily life activities (DLAs) or predict the falls in elderly. This study is limited by the fact that it uses multi-modality datasets collected in uncontrolled environments where limited number of actions are performed. The same approach can be used on large and challenging datasets for recognizing every person's actions in crowded environments.  
Conceptualization, N.u.R.M. and S.A.R.A.-B.; methodology, N.u.R.M.; software, N.u.R.M.; validation, S.A.R.A.-B. and U.U.S.; formal analysis, N.P.; investigation, A.C. and N.u.R.M.; resources, N.u.R.M.; data curation, N.u.R.M. and U.U.S.; writing--original draft preparation, N.u.R.M.; writing--review and editing, A.C.; visualization, N.P.; supervision, S.A.R.A.-B. and U.U.S.; project administration, S.A.R.A.-B.; funding acquisition, A.C. and N.P. All authors have read and agreed to the published version of the manuscript  
This research was funded by the European Union's Horizon 2020 Research and Innovation program under the Marie Skodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints).  
The authors would like to thank the Ministry of Higher Education Malaysia (KPT) and Universiti Teknologi Malaysia (UTM) for their support under the UTM Fundamental Research Grant (UTMFR), grant number Q.J130000.3823.22H29. The authors also gratefully acknowledge funding from European Union's Horizon 2020 Research and Innovation program under the Marie Skodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, [[http://www.a-wear.eu/](http://www.a-wear.eu/)]([http://www.a-wear.eu/](http://www.a-wear.eu/)) (accessed on 15 July 2022)) and a grant from the Romanian National Authority for Scientific Research and Innovation, UEFISCDI project PN-III-P3-3.6-H2020-2020-0124.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Year** & **Short Descriptions** & **Data Used** & **Feature Dimension** \\ \hline HON4D [46] & CVPR 2013 & Handcrafted (global descriptor) & Depth & [17, 880, 151, 200] \\ \hline HDG [47] & WACV 2014 & Handcrafted (local + global descriptor) & Depth+ Skeleton & [1662, 1819] \\ \hline P-LSTM [48] & CVPR 2016 & Deep learning (LSTM) & Skeleton & No. of joints \(\times\)\(3\times\) 8 \\ \hline HYM+TM [49] & CVPR 2016 & Deep learning (CNN) & Depth & 4096 \\ \hline Clips+CNN+ MTLN [50] & CVPR 2017 & Deep learning (pre-trained VGG19,MTLN) & Skeleton & 7168 \\ \hline RNN [51] & CVPR 2018 & Deep learning (RNN) & Skeleton & 512 \\ \hline ST-GCN [52] & AAAI 2018 & Deep learning (Graph ConvNet) & Skeleton & 256 \\ \hline Proposed & 2022 & OpenPose + CNNLSTM & RGB & \(25\times 2\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of feature dimension.