(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.

Context: : A comprehensive systematic review of the literature on the use of augmented information in anterior cruciate ligament (ACL) injury-prevention programs to improve jump-landing technique was conducted. The use of motor-learning concepts could provide more robust means of preventing ACL injuries. Objective: : To systematically summarize the effectiveness of augmented information in improving the biomechanical factors associated with an increased risk for ACL injury. Data Sources: : Articles were retrieved using the electronic databases of PubMed, MEDLINE, CINAHL, and Google Scholar and 3 lines of truncated search words: (a) lower extremity, knee, ACL, and anterior cruciate ligament; (b) prevention, injury prevention, and prehab; and (c) augmented information, augmented feedback, feedback, cue, and instruction. We also performed a hand search of the reference lists of the screened articles. Data Extraction: : We independently assessed the methodologic quality using the Cochrane Group on Screening and Diagnostic Test Methods list. Articles were placed in 1 of 3 augmented-information categories: prescriptive, feedback, or transition. Articles were also categorized based on whether the information likely encouraged an internal or external focus of attention. Data Synthesis: : The searches identified a total of 353 studies, of which 18 were included. Most researchers found that augmented information could lead to technique changes to reduce the biomechanical risk factors associated with ACL injury. The average methodologic quality of the studies was 11.8 out of 17, with a range from 8 to 15. The authors of only 7 studies examined retention of the improved techniques. Conclusions: : The evidence suggests that augmented information can be used to significantly improve the biomechanical indicators associated with ACL injury and to enhance current ACL injury-prevention programs. Combined prescriptive and feedback information that encouraged both internal and external foci led to the largest retention effect sizes.