Comparing the Drop Vertical Jump Tracking Performance of the Azure Kinect to the Kinect V2.

Traditional motion analysis systems are impractical for widespread screening of non-contact anterior cruciate ligament (ACL) injury risk. The Kinect V2 has been identified as a portable and reliable alternative but was replaced by the Azure Kinect. We hypothesize that the Azure Kinect will assess drop vertical jump (DVJ) parameters associated with ACL injury risk with similar accuracy to its predecessor, the Kinect V2. Sixty-nine participants performed DVJs while being recorded by both the Azure Kinect and the Kinect V2 simultaneously. Our software analyzed the data to identify initial coronal, peak coronal, and peak sagittal knee angles. Agreement between the two systems was evaluated using the intraclass correlation coefficient (ICC). There was poor agreement between the Azure Kinect and the Kinect V2 for initial and peak coronal angles (ICC values ranging from 0.135 to 0.446), and moderate agreement for peak sagittal angles (ICC = 0.608, 0.655 for left and right knees, respectively). At this point in time, the Azure Kinect system is not a reliable successor to the Kinect V2 system for assessment of initial coronal, peak coronal, and peak sagittal angles during a DVJ, despite demonstrating superior tracking of continuous knee angles. Alternative motion analysis systems should be explored.

Comparing novel smartphone pose estimation frameworks with the Kinect V2 for knee tracking during athletic stress tests.

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.

Comparing a Portable Motion Analysis System against the Gold Standard for Potential Anterior Cruciate Ligament Injury Prevention and Screening.

Knee kinematics during a drop vertical jump, measured by the Kinect V2 (Microsoft, Redmond, WA, USA), have been shown to be associated with an increased risk of non-contact anterior cruciate ligament injury. The accuracy and reliability of the Microsoft Kinect V2 has yet to be assessed specifically for tracking the coronal and sagittal knee angles of the drop vertical jump. Eleven participants performed three drop vertical jumps that were recorded using both the Kinect V2 and a gold standard motion analysis system (Vicon, Los Angeles, CA, USA). The initial coronal, peak coronal, and peak sagittal angles of the left and right knees were measured by both systems simultaneously. Analysis of the data obtained by the Kinect V2 was performed by our software. The differences in the mean knee angles measured by the Kinect V2 and the Vicon system were non-significant for all parameters except for the peak sagittal angle of the right leg with a difference of 7.74 degrees and a p-value of 0.008. There was excellent agreement between the Kinect V2 and the Vicon system, with intraclass correlation coefficients consistently over 0.75 for all knee angles measured. Visual analysis revealed a moderate frame-to-frame variability for coronal angles measured by the Kinect V2. The Kinect V2 can be used to capture knee coronal and sagittal angles with sufficient accuracy during a drop vertical jump, suggesting that a Kinect-based portable motion analysis system is suitable to screen individuals for the risk of non-contact anterior cruciate ligament injury.

Using an Affordable Motion Capture System to Evaluate the Prognostic Value of Drop Vertical Jump Parameters for Noncontact ACL Injury.

BACKGROUND: Knee kinematic parameters during a drop vertical jump (DVJ) have been demonstrated to be associated with increased risk of noncontact anterior cruciate ligament (ACL) injury. However, standard motion analysis systems are not practical for routine screening. Affordable and practical motion sensor alternatives exist but require further validation in the context of ACL injury risk assessment. PURPOSE/HYPOTHESIS: To prospectively study DVJ parameters as predictors of noncontact ACL injury in collegiate athletes using an affordable motion capture system (Kinect; Microsoft). We hypothesized that athletes who sustained noncontact ACL injury would have larger initial and peak contact coronal abduction angles and smaller peak flexion angles at the knee during a DVJ. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: 102 participants were prospectively recruited from a collegiate varsity sports program. A total of 101 of the 102 athletes (99%) were followed for an entire season for noncontact ACL injury. Each athlete performed 3 DVJs, and the data were recorded using the motion capture system. Initial coronal, peak coronal, and peak sagittal angles of the knee were identified by our software. RESULTS: Five of the 101 athletes sustained a noncontact ACL injury. Peak coronal angles were significantly greater and peak sagittal flexion angles were significantly smaller in ACL-injured athletes (P = .049, P = .049, respectively). Receiver operating characteristic (ROC) analysis demonstrated an area under the curve of 0.88, 0.92, and 0.90 for initial coronal, peak coronal, and peak sagittal angle, respectively. An initial coronal angle cutoff of 2.96° demonstrated 80% sensitivity and 72% specificity, a peak coronal angle cutoff of 6.16° demonstrated 80% sensitivity and 72% specificity, and a peak sagittal flexion cutoff of 93.82° demonstrated 80% sensitivity and 74% specificity on the study cohort. CONCLUSION: Increased peak coronal angle and decreased peak sagittal angle during a DVJ were significantly associated with increased risk for noncontact ACL injury. Based on ROC analysis, initial coronal angle showed good prognostic ability, whereas peak coronal angle and peak sagittal flexion provided excellent prognostic ability. Affordable motion capture systems show promise as cost-effective and practical options for large-scale ACL injury risk screening.