Validation of OpenCap: A low-cost markerless motion capture system for lower-extremity kinematics during return-to-sport tasks.

Low-cost markerless motion capture systems offer the potential for 3D measurement of joint angles during human movement. This study aimed to validate a smartphone-based markerless motion capture system's (OpenCap) derived lower extremity kinematics during common return-to-sport tasks, comparing it to an established optoelectronic motion capture system. Athletes with prior anterior cruciate ligament reconstruction (12-18 months post-surgery) performed three movements: a jump-landing-rebound, single-leg hop, and lateral-vertical hop. Kinematics were recorded concurrently with two smartphones running OpenCap's software and with a 10-camera, marker-based motion capture system. Validity of lower extremity joint kinematics was assessed across 437 recorded trials using measures of agreement (coefficient of multiple correlation: CMC) and error (mean absolute error: MAE, root mean squared error: RMSE) across the time series of movement. Agreement was best in the sagittal plane for the knee and hip in all movements (CMC > 0.94), followed by the ankle (CMC = 0.84-0.93). Lower agreement was observed for frontal (CMC = 0.47-0.78) and transverse (CMC = 0.51-0.6) plane motion. OpenCap presented a grand mean error of 3.85° (MAE) and 4.34° (RMSE) across all joint angles and movements. These results were comparable to other available markerless systems. Most notably, OpenCap's user-friendly interface, free software, and small physical footprint have the potential to extend motion analysis applications beyond conventional biomechanics labs, thus enhancing the accessibility for a diverse range of users.

Impact Magnitude and Symmetry in Females During Return to Sport Tasks Measured With Inertial Sensors.

CONTEXT: Impact magnitude, such as peak tibial acceleration, may be associated with lower extremity injury risk and can be measured with an inertial sensor. An understanding of impact magnitude across functional tasks could guide clinicians in exercise prescription during rehabilitation of lower extremity injuries. OBJECTIVES: To determine (1) differences in impact magnitude based on task and (2) which tasks have asymmetrical impact magnitude based on limb dominance. DESIGN: Observational cohort design. Thirty-three healthy, recreationally active adult females participated in 1 testing session on a basketball court. METHODS: Participants wore inertial sensors with embedded accelerometers on bilateral distal shanks. Participants completed 9 plyometric, speed, and agility tasks commonly utilized during the return to sport phase of lower extremity rehabilitation. MAIN OUTCOME MEASURES: Average impact magnitude (peak tibial acceleration in multiples of gravity, g) for each limb for each task. ANALYSES: We used a repeated-measures analysis of variance (factor: task) to determine the differences in impact magnitude based on task. We categorized tasks by magnitude of impact into low, medium, high, and very high impact. We utilized paired t tests for each task to compare limbs (dominant vs nondominant). RESULTS: Impact magnitude differed based on task (P < .001). We classified tasks as low impact (≤10g; single-leg [SL] lateral jump, double-leg [DL] lateral jump); medium impact (11-20g; SL vertical jump, box drill); high impact (21-30g; modified T test, DL forward jump, SL forward jump); and very high impact (≥31g; sprint, DL tuck jump). Impact magnitude differed by limb in 3 tasks (DL forward jump, DL lateral jump, and box drill), with a higher impact on the dominant limb in each task. CONCLUSIONS: Impact magnitude differed based on task. While most tasks had symmetric impact magnitude between limbs, 3 tasks had a higher impact magnitude on the dominant limb.

Are Elite Collegiate Female Athletes PRIME for a Safe Return to Sport after ACLR? An Investigation of Physical Readiness and Integrated Movement Efficiency (PRIME).

Background: Elite female athletes who successfully return to sport after anterior cruciate ligament reconstruction (ACLR) represent a high-risk group for secondary injury. Little is known about how the functional profile of these athletes compares to their teammates who have not sustained ACL injuries. Purpose: To compare elite collegiate female athletes who were able to successfully return to sport for at least one season following ACLR to their teammates with no history of ACLR with regard to self-reported knee function, kinetics, and kinematics during a double limb jump-landing task. Study Design: Cross-Sectional Study. Level of Evidence: Level 3. Methods: Eighty-two female collegiate athletes (17 ACLR, 65 control) completed the knee-specific SANE (single assessment numeric evaluation) and three trials of a jump-landing task prior to their competitive season. vGRF data on each limb and the LESS (Landing Error Scoring System) score were collected from the jump-landing task. Knee-SANE, vGRF data, and LESS scores were compared between groups. All athletes were monitored for the duration of their competitive season for ACL injuries. Results: Athletes after ACLR reported worse knee-specific function. Based on vGRF data, they unloaded their involved limb during the impact phase of the landing, and they were more asymmetrical between limbs during the propulsion phase as compared to the control group. The ACLR group, however, had lower LESS scores, indicative of better movement quality. No athletes in either group sustained ACL injuries during the following season. Conclusion: Despite reporting worse knee function and demonstrating worse kinetics, the ACLR group demonstrated better movement quality relative to their uninjured teammates. This functional profile may correspond to short-term successful outcomes following ACLR, given that no athletes sustained ACL injuries in the competition season following assessment.

Think outside the box: Incorporating secondary cognitive tasks into return to sport testing after ACL reconstruction.

The optimal set of return to sport (RTS) tests after anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) remains elusive. Many athletes fail to pass current RTS test batteries, fail to RTS, or sustain secondary ACL injuries if they do RTS. The purpose of this review is to summarize current literature regarding functional RTS testing after ACLR and to encourage clinicians to have patients "think" (add a secondary cognitive task) outside the "box" (in reference to the box used during the drop vertical jump task) when performing functional RTS tests. We review important criteria for functional tests in RTS testing, including task-specificity and measurability. Firstly, tests should replicate the sport-specific demands the athlete will encounter when they RTS. Many ACL injuries occur when the athlete is performing a dual cognitive-motor task (e.g., attending to an opponent while performing a cutting maneuver). However, most functional RTS tests do not incorporate a secondary cognitive load. Secondly, tests should be measurable, both through the athlete's ability to complete the task safely (through biomechanical analyses) and efficiently (through measures of performance). We highlight and critically examine three examples of functional tests that are commonly used for RTS testing: the drop vertical jump, single-leg hop tests, and cutting tasks. We discuss how biomechanics and performance can be measured during these tasks, including the relationship these variables may have with injury. We then discuss how cognitive demands can be added to these tasks, and how these demands influence both biomechanics and performance. Lastly, we provide clinicians with practical recommendations on how to implement secondary cognitive tasks into functional testing and how to assess athletes' biomechanics and performance.

Combining Inertial Sensors and Machine Learning to Predict vGRF and Knee Biomechanics during a Double Limb Jump Landing Task.

(1) Background: Biomechanics during landing tasks, such as the kinematics and kinetics of the knee, are altered following anterior cruciate ligament (ACL) injury and reconstruction. These variables are recommended to assess prior to clearance for return to sport, but clinicians lack access to the current gold-standard laboratory-based assessment. Inertial sensors serve as a potential solution to provide a clinically feasible means to assess biomechanics and augment the return to sport testing. The purposes of this study were to (a) develop multi-sensor machine learning algorithms for predicting biomechanics and (b) quantify the accuracy of each algorithm. (2) Methods: 26 healthy young adults completed 8 trials of a double limb jump landing task. Peak vertical ground reaction force, peak knee flexion angle, peak knee extension moment, and peak sagittal knee power absorption were assessed using 3D motion capture and force plates. Shank- and thigh- mounted inertial sensors were used to collect data concurrently. Inertial data were submitted as inputs to single- and multiple- feature linear regressions to predict biomechanical variables in each limb. (3) Results: Multiple-feature models, particularly when an accelerometer and gyroscope were used together, were valid predictors of biomechanics (R2 = 0.68-0.94, normalized root mean square error = 4.6-10.2%). Single-feature models had decreased performance (R2 = 0.16-0.60, normalized root mean square error = 10.0-16.2%). (4) Conclusions: The combination of inertial sensors and machine learning provides a valid prediction of biomechanics during a double limb landing task. This is a feasible solution to assess biomechanics for both clinical and real-world settings outside the traditional biomechanics laboratory.