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PubMed Publications| Keyword search (4,163 papers available) |  |
"Computer vision" Keyword-tagged Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Inferring concussion history in athletes using pose and ground reaction force estimation and stability analysis of plyometric exercise videos | Alves W; Babouras A; Martineau PA; Schutt D; Robbins S; Fevens T; | 40632382 ENCS |
| 2 | Real-time motion detection using dynamic mode decomposition | Mignacca M; Brugiapaglia S; Bramburger JJ; | 40421310 MATHSTATS |
| 3 | Comparing novel smartphone pose estimation frameworks with the Kinect V2 for knee tracking during athletic stress tests | Babouras A; Abdelnour P; Fevens T; Martineau PA; | 38730186 ENCS |
| 4 | Breamy: An augmented reality mHealth prototype for surgical decision-making in breast cancer | Najafi N; Addie M; Meterissian S; Kersten-Oertel M; | 38638506 ENCS |
| Title: | Comparing novel smartphone pose estimation frameworks with the Kinect V2 for knee tracking during athletic stress tests | ||||
| Authors: | Babouras A, Abdelnour P, Fevens T, Martineau PA | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/38730186/ | ||||
| DOI: | 10.1007/s11548-024-03156-5 | ||||
| Publication: | International journal of computer assisted radiology and surgery | ||||
| Keywords: | Computer vision; Knee kinematics; Pose estimation; Sports medicine; | ||||
| PMID: | 38730186 | Category: | Date Added: | 2024-05-11 | |
| Dept Affiliation: |
ENCS
1 Experimental Surgery, McGill University, Montréal, QC, H3A 0G4, Canada. athanasios.babouras@mail.mcgill.ca. 2 Experimental Surgery, McGill University, Montréal, QC, H3A 0G4, Canada. 3 Computer Science and Software Engineering, Concordia University, Montréal, QC, H3G 1M8, Canada. |
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Description: |
Purpose: To compare the accuracy of the Microsoft Kinect V2 with novel pose estimation frameworks, in assessing knee kinematics during athletic stress tests, for fast and portable risk assessment of anterior cruciate ligament (ACL) injury. Methods: We captured 254 varsity athletes, using the Kinect V2 and a smartphone application utilizing Google's MediaPipe framework. The devices were placed as close as possible and used to capture a person, facing the cameras, performing one of three athletic stress tests at a distance of 2.5 ms. Custom software translated the results from both frameworks to the same format. We then extracted relevant knee angles at key moments of the jump and compared them, using the Kinect V2 as the ground truth. Results: The results show relatively small angle differences between the two solutions in the coronal plane and moderate angle differences on the sagittal plane. Overall, the MediaPipe framework results seem to underestimate both knee valgus angles and knee sagittal angles compared to the Kinect V2. Conclusion: This preliminary study demonstrates the potential for Google's MediaPipe framework to be used for calculating lower limb kinematics during athletic stress test motions, which can run on most modern smartphones, as it produces similar results to the Kinect V2. A smartphone application similar to the one developed could potentially be used for low cost and widespread ACL injury prevention. |
