(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.

Markerless motion capture systems are promising for the assessment of movement in more real world research and clinical settings. While the technology has come a long way in the last 20 years, it is important for researchers and clinicians to understand the capacities and considerations for implementing these types of systems. The current review provides a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis related to the successful adoption of markerless motion capture technology for the assessment of lower-limb musculoskeletal kinematics in sport medicine and performance settings. 31 articles met the a priori inclusion criteria of this analysis. Findings from the analysis indicate that the improving accuracy of these systems via the refinement of machine learning algorithms, combined with their cost efficacy and the enhanced ecological validity outweighs the current weaknesses and threats. Further, the analysis makes clear that there is a need for multidisciplinary collaboration between sport scientists and computer vision scientists to develop accurate clinical and research applications that are specific to sport. While work remains to be done for broad application, markerless motion capture technology is currently on a positive trajectory and the data from this analysis provide an efficient roadmap toward widespread adoption.

Introduction To determine whether individuals with a history of anterior cruciate ligament reconstruction (ACLR) exhibit altered neuromotor function compared to healthy controls. It was hypothesized that the ACLR group would have slower postural responses compared to healthy individuals of similar age. Materials and Methods Sixteen adults with a unilateral ACLR and 16 matched healthy controls participated. General assessments of neuromotor function were gathered and included measures of reaction time (both seated and postural conditions), walking ability, balance, ankle ROM, proprioception, knee joint laxity, patellar tendon reflex latency, and quadriceps strength. Data were analyzed using mixed generalized linear models with between‐subject (ie, controls, ACLR) and within‐subject factors (ie, affected, unaffected limb). Results Individuals with an ACLR exhibited a significant slowing of their postural reaction times compared to the control individuals. The ACLR group was slower under both the simple (ACLR: 484 ± 6.17 ms, control: 399 ± 1.95 ms) and choice reaction time conditions (ACLR: 550 ± 43 ms, control: 445 ± 43 ms). No other group differences were found in any of the other measures. Conclusion Overall, ACLR individuals had a reduced ability to respond quickly under more challenging postural conditions (ie, stepping response). This finding would indicate that the impact of an ACLR is not purely mechanical and restricted to the joint. Rather, injury and reconstruction of the ACL impact neural mechanisms, altering individuals' ability to respond under challenging balance tasks.

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Autism spectrum disorder (ASD) is a complex diagnosis characterized primarily by persistent deficits in social communication/interaction and repetitive behavior patterns, interests, and/or activities. ASD is also characterized by various physiological and/or behavioral features that span sensory, neurological, and neuromotor function. Although problems with lower body coordination and control have been noted, little prior research has examined lower extremity strength and proprioception, a process requiring integration of sensorimotor information to locate body/limbs in space. We designed this study to compare lower limb proprioception and strength in adolescents with ASD and neurotypical controls. Adolescents diagnosed with ASD (n = 17) and matched controls (n = 17) performed ankle plantarflexion/dorsiflexion bilateral proprioception and strength tests on an isokinetic dynamometer. We assessed position-based proprioception using three targeted positions (5 and 20-degrees plantarflexion and 10-degrees dorsiflexion) and speed-based proprioception using two targeted speeds (60 and 120-degrees/second). We assessed strength at 60-degrees/second. Participants with ASD performed 1.3-times more poorly during plantarflexion position and 2-times more poorly during the speed-based proprioception tests compared to controls. Participants with ASD also exhibited a 40% reduction in plantarflexion strength compared to controls. These findings provide insight into mechanisms that underly the reduced coordination, aberrant gait mechanics, and coordination problems often seen in individuals with ASD, and the identification of these mechanisms now permits better targeting of rehabilitative goals in treatment programs.