The Utility of Functional Data Analyses to Reveal Between-Limbs Asymmetries in Those With a History of Anterior Cruciate Ligament Reconstruction

Lower extremity energy absorption in individuals with lower extremity musculoskeletal injury history during functional tasks: A scoping review

BACKGROUND

Recent evidence has utilized energetic absorption as an alternative method to evaluate biomechanical profiles associated with lower extremity injury risk. The aim of scoping review is to summarize the literatures that utilized energetic analysis in individuals with lower extremity injury history during functional tasks.

METHODS

A literature search, conducted in August 2023, involved four databases-PubMed, SPORTDiscus, Scopus, and Web of Science. A manual search was performed to identify additional articles.

FINDINGS

Among the 17 included studies, we identified 11 and 6 articles investigated lower extremity energetic absorption in individuals with anterior cruciate ligament reconstruction and chronic ankle instability history during jump-related tasks, receptively. Individuals with anterior cruciate ligament reconstruction displayed a reduction of energetic absorption in the involved knee, coupled with increased energetic absorption in the involved hip, as compared to the uninvolved limb or the reference group. The findings in those with chronic ankle instability were varied. Most studies suggested that individuals with chronic ankle instability displayed a compensatory movement pattern to off-load their ankle joint, while concurrently increasing energy absorbed at the knee joint compared to the reference group. Conversely, one study suggested that individuals with chronic ankle instability might rely more on their ankle joint for energy absorption.

INTERPRETATION

Our findings indicated that both individuals with anterior cruciate ligament reconstruction and chronic ankle instability displayed a distinctive compensatory strategy during landing. This strategy is characterized by increasing energy dissipation on the proximal joints, compensating for a reduction on the distal joint to dissipate less energy.

Comparison of discrete and continuous analysis approaches for evaluating gait biomechanics in individuals with anterior cruciate ligament reconstruction

BACKGROUND

Aberrant gait biomechanics contribute to post-traumatic knee osteoarthritis development following anterior cruciate ligament reconstruction (ACLR). Walking gait biomechanics are typically evaluated post-ACLR by identifying discrete, peak values in the load acceptance phase of gait (i.e. first 50 %). As these approaches evaluate a single time instant during the gait cycle, functional data analysis (FDA) techniques that evaluate the entire stance phase waveform are becoming more common in the literature. However, it is unclear if these analysis approaches identify the same biomechanical phenomena.

RESEARCH QUESTION

The purpose of this study was to determine whether four gait biomechanics analysis approaches identify the same aberrant gait characteristics in individuals with ACLR.

METHODS

Twenty-four individuals with ACLR and 24 healthy controls completed gait analyses on an instrumented treadmill. Four analysis approaches were employed to compare the vertical ground reaction force and sagittal knee angles and moments during the first 50 % of the stance phase between groups and between limbs in the ACLR cohort: 1) comparison of peak values from individual trials (Peak), 2) comparison of peak values from time-normalized ensemble waveforms (Ensemble Peak), 3) FDA via functional ANCOVA (FANCOVA), and 4) FDA evaluating overlap of the 95 % confidence intervals for each waveform (FDA-CI).

RESULTS

The Peak, Ensemble Peak, and FANCOVA approaches identified highly similar group and limb differences in the biomechanics outcomes with respect to both magnitude and temporal location. However, the FANCOVA approach indicated that these differences were distributed across large portions of the load acceptance phase and that differences existed outside the first 50 % of stance. The FDA-CI approach was generally not effective for identifying aberrant gait biomechanics.

SIGNIFICANCE

Peak and FANCOVA approaches to gait analysis provide similar findings. Future research is necessary to determine if the additional information afforded by FANCOVA provides insight regarding the mechanical pathogenesis of post-traumatic knee osteoarthritis.

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.