# Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model
## 4 Conclusions  
An automatic CC evaluation approach was developed by using video imagery for people who want to learn how to perform CCs, such as students, drivers and police. We applied OpenPose to extract multiple joints and links of the human body in 2D images and then analyzed critical body parts that influence quality. In order to consider the individual physical differences of each person, a total of 1200 samples were generated from 40 videos recorded by monocular cameras. Five criteria were defined based on our research and on rules from major CPR guidelines. The angles of elbows, depth, frequency, position and chest rebound were parameterized to discriminate between correct and incorrect actions.  
Our approach has certain advantages by comparing several indicators, such as comprehensiveness, applicability, cost, performance and flexibility.  
1. In addition to detecting the general indicators of compression, we propose automated discrimination of operator posture for the first time.
2. Our algorithm is not affected by the surrounding environment. It uses a typical monocular RGB camera as a sensor to collect data, which has better applicability than a depth camera and has a lower cost.
3. The performance of our algorithm is 94.58%, 71.57%, 94.00%, and 81.27% in accuracy, precision, recall and F-measure, respectively.
4. The application of the HPE method in CPR assessment can not only reduce the problem of incorrect scores caused by an instructor's lack of energy, but it can also allow trainees to understand their own mistakes in private practice to better master CPR techniques.  
For future studies, improvements can be made in the following two aspects. Firstly, experimentation can be conducted to explore better methods to eliminate the local minimum problem, such as using the median filter. Furthermore, 3D analysis of the human body as well as deep learning approaches are to be further researched to improve its reliability in public CPR popularization.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12104847/s1](https://www.mdpi.com/article/10.3390/app12104847/s1)]([https://www.mdpi.com/article/10.3390/app12104847/s1](https://www.mdpi.com/article/10.3390/app12104847/s1)), Figure S1: A frame of CCs video processed by OpenPose model; Figure S2: An output curve drawn by the plt.plot function; Video S1: A test video of CCs.  
Conceptualization, S.Z. and J.J.; methodology, S.Z.; software, S.Z., J.J. and C.W.; validation, S.Z., J.J. and C.W.; formal analysis, S.Z.; investigation, J.J. and C.W.; resources, W.D. and B.F.; data curation, J.J.; writing--original draft preparation, S.Z. and J.J.; writing--review and editing, S.Z., J.J. and B.F.; visualization, S.Z. and C.W.; supervision, W.D. and B.F.; project administration, W.D. and B.F.; funding acquisition, W.D. and B.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Research on artificial intelligence cardiopulmonary resuscitation training and assessment system, TJUWYY2022014\", the \"National key research and development plan project of China, 2018 YFC1504405-3\" and the \"Tianjin University Independent Fund Project, 2020 XZS-0029\".  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline TP & 0.118 & Accuracy & 94.58\% \\ FP & 0.047 & Precision & 71.57\% \\ FN & 0.008 & Recall & 94.00\% \\ TN & 0.828 & F-measure & 81.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance of the proposed method.