# Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems
## 7 Conclusions  
In this research, the authors implemented an FS technique, a pre-trained model, and an ML algorithm to earn features simultaneously from human posture (HP) images, and the learned features were hybridized for posture classification. Future work will expand on this technology to detect additional postures in images and image sequences to help interpret behavior in surveillance recordings. Using human posture images from the Silhouettes of Human Posture collection as a baseline, which contains four types of HP: bending, laying, sitting, and standing, the authors conducted extensive trials to verify our theory. To classify HP accurately and precisely, the authors examine the efficacy of employing hybridized models. The findings of our investigation were provided in depth along with their relationship to the number of classes needed to classify HP. The findings of the proposed model indicated that HP classification using the suggested model increases both the training and validation accuracy. The accuracy of HP classification issues improved by between 2% and 14% when our results were examined in contrast to those of three other existing conventional DCNN approaches implemented. In conclusion, the suggested technique was shown to achieve much better results than the other three techniques when tested using the 20% test data set aside.  
Future work will expand on this technology to detect additional postures in images and image sequences to help interpret behavior in surveillance recordings. In future research, model uncertainties and external data validation are proposed. The model predictions will also be conducted in the future using the test dataset set aside.  
Conceptualization, R.M.; methodology, R.O.O.; software, R.O.O.; validation, R.O.O., R.M. and R.D.; formal analysis, R.O.O., R.M., R.D. and S.M.; investigation, R.O.O., R.M. and R.D.; resources, R.M.; data curation, R.O.O.; writing--original draft preparation, R.O.O. and R.M.; writing--review and editing, S.M. and the study R.D.; visualization, R.O.O.; supervision, R.M.; project administration, S.M. and R.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available in the Kaggle repository [[https://ieee-dataport.org/](https://ieee-dataport.org/)]([https://ieee-dataport.org/](https://ieee-dataport.org/)) (accessed on 23 August 2022). [[https://www.kaggle.com/datasets/deepshah16/silhouettes-of-human-posture](https://www.kaggle.com/datasets/deepshah16/silhouettes-of-human-posture)]([https://www.kaggle.com/datasets/deepshah16/silhouettes-of-human-posture](https://www.kaggle.com/datasets/deepshah16/silhouettes-of-human-posture)) (accessed on 23 August 2022). The codes required to execute this study have already been posted to the GitHub repository and can be found in the repository: [[https://github.com/[PERSON]/LASSO-InceptionV3-SVM.git](https://github.com/[PERSON]/LASSO-InceptionV3-SVM.git)]([https://github.com/[PERSON]/LASSO-InceptionV3-SVM.git](https://github.com/[PERSON]/LASSO-InceptionV3-SVM.git)) (accessed on 23 August 2022).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors** & **Model** & **Accuracy (\%)** \\ \hline [PERSON] \& [PERSON] [71] & Rule-based & 93.00 \\ \hline [PERSON], [PERSON] et al. [72] & Artificial Neural Network & 81.00 \\ \hline [PERSON] et al. [73] & SVM & 93.00 \\ \hline [PERSON] et al. [69] & Gaussian Mixture Model & 92.50 \\ \hline [PERSON] et al. [70] & Supervised learning algorithm & 76.88 \\ \hline
**Proposed Model** & **InceptionV3-SVM** & **95.42** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Evaluation of the test dataset.