Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model
Aiming at the problems of the low evaluation efficiency of the existing traditional cardiopulmonary resuscitation (CPR) training mode and the considerable development of machine vision technology, a quality evaluation algorithm for chest compressions (CCs) based on the OpenPose human pose estimation...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/10/4847 |
| _version_ | 1797501922458992640 |
|---|---|
| author | Siqi Zhang Jie Jin Chaofang Wang Wenlong Dong Bin Fan |
| author_facet | Siqi Zhang Jie Jin Chaofang Wang Wenlong Dong Bin Fan |
| author_sort | Siqi Zhang |
| collection | DOAJ |
| description | Aiming at the problems of the low evaluation efficiency of the existing traditional cardiopulmonary resuscitation (CPR) training mode and the considerable development of machine vision technology, a quality evaluation algorithm for chest compressions (CCs) based on the OpenPose human pose estimation (HPE) model is proposed. Firstly, five evaluation criteria are proposed based on major international CPR guidelines along with our experimental study on elbow straightness. Then, the OpenPose network is applied to obtain the coordinates of the key points of the human skeleton. The algorithm subsequently calculates the geometric angles and displacement of the selected joint key points using the detected coordinates. Finally, it determines whether the compression posture is standard, and it calculates the depth, frequency, position and chest rebound, which are the critical evaluation metrics of CCs. Experimental results show that the average accuracy of network behavior detection reaches 94.85%, and detection speed reaches 25 fps. |
| first_indexed | 2024-03-10T03:25:32Z |
| format | Article |
| id | doaj.art-9095701cccf241658d0593fcc42105d1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:25:32Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9095701cccf241658d0593fcc42105d12023-11-23T09:54:03ZengMDPI AGApplied Sciences2076-34172022-05-011210484710.3390/app12104847Quality Evaluation Algorithm for Chest Compressions Based on OpenPose ModelSiqi Zhang0Jie Jin1Chaofang Wang2Wenlong Dong3Bin Fan4Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, ChinaInstitute of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, ChinaInstitute of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, ChinaInstitute of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, ChinaInstitute of Disaster and Emergency Medicine, Tianjin University, Tianjin 300072, ChinaAiming at the problems of the low evaluation efficiency of the existing traditional cardiopulmonary resuscitation (CPR) training mode and the considerable development of machine vision technology, a quality evaluation algorithm for chest compressions (CCs) based on the OpenPose human pose estimation (HPE) model is proposed. Firstly, five evaluation criteria are proposed based on major international CPR guidelines along with our experimental study on elbow straightness. Then, the OpenPose network is applied to obtain the coordinates of the key points of the human skeleton. The algorithm subsequently calculates the geometric angles and displacement of the selected joint key points using the detected coordinates. Finally, it determines whether the compression posture is standard, and it calculates the depth, frequency, position and chest rebound, which are the critical evaluation metrics of CCs. Experimental results show that the average accuracy of network behavior detection reaches 94.85%, and detection speed reaches 25 fps.https://www.mdpi.com/2076-3417/12/10/4847human pose estimationcardiopulmonary resuscitationOpenPosechest compressionsjoint key points |
| spellingShingle | Siqi Zhang Jie Jin Chaofang Wang Wenlong Dong Bin Fan Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model Applied Sciences human pose estimation cardiopulmonary resuscitation OpenPose chest compressions joint key points |
| title | Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model |
| title_full | Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model |
| title_fullStr | Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model |
| title_full_unstemmed | Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model |
| title_short | Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model |
| title_sort | quality evaluation algorithm for chest compressions based on openpose model |
| topic | human pose estimation cardiopulmonary resuscitation OpenPose chest compressions joint key points |
| url | https://www.mdpi.com/2076-3417/12/10/4847 |
| work_keys_str_mv | AT siqizhang qualityevaluationalgorithmforchestcompressionsbasedonopenposemodel AT jiejin qualityevaluationalgorithmforchestcompressionsbasedonopenposemodel AT chaofangwang qualityevaluationalgorithmforchestcompressionsbasedonopenposemodel AT wenlongdong qualityevaluationalgorithmforchestcompressionsbasedonopenposemodel AT binfan qualityevaluationalgorithmforchestcompressionsbasedonopenposemodel |