The purpose of this study was to determine the role of antiphase trunk motion during quiet stance while maintaining constant visual and support surface conditions. Eyes-open quiet stance trials were performed on a firm support surface while wearing a rigid hip-knee orthotic brace that reduced antiphase trunk motion. Amplitude spectral density, coherence, and cophase were compared for hip-locked, hip-unlocked, and no-brace conditions. Amplitude spectral density calculations showed that trunk and leg sway velocities, and ankle torque (AT) decreased when antiphase trunk sway was allowed. Coherence and cophase estimates identified in-phase trunk-legs sway below 1 Hz and antiphase at higher frequencies. Legs-AT cophase calculations showed that the legs lagged the application of AT at all frequencies, while trunk-AT cophase showed the trunk lagged AT below 1 Hz and led AT at higher frequencies. The results demonstrate that antiphase trunk sway helps reduce sway velocity and AT. Furthermore, the trunk-leading cophase relationship with AT showed that antiphase trunk motion occurred before AT was applied. This implies that antiphase trunk motion facilitates changes in sway direction and helps regulate sway velocity. The results have significant implications for predicting postural control deficiencies due to injury, disease, and aging.

BackgroundAfter reconstruction surgery for anterior cruciate ligament injury (ACLR), approximately 20% of individuals experience re-injury following rehabilitation suggesting elevated injury risk compared to individuals without prior surgery. We evaluate limb loading asymmetry, a risk factor, during sit-to-stand (STS) to gain insight regarding two plausible explanations for the higher risk of injury. First, surgery may introduce new risks, or second, injury susceptibility may increase for pre-existing risk levels. The purpose of this study was to compare loading asymmetries during STS between post-rehabilitation ACLR participants and healthy controls.MethodsFifty-six participants (28 ACLR/28 controls) performed five STS trials. In each trial, peak vertical ground reaction force (vGRF), knee and hip extension moments, and knee and hip power were determined. Two-way repeated-measures ANOVAs examined group, limb, and interaction effects. Effect sizes were determined using Cohen’s d.FindingsDespite the lack of hip moment or vGRF asymmetries, ACLR participants demonstrated surgical limb underloading during STS, greater interlimb knee asymmetry, and greater bilateral peak knee power and hip power than controls.Interpretation Post-rehabilitation ACLR participants demonstrated surgical knee underloading during STS compared to controls despite no observed vGRF or hip moment asymmetry. This suggests that vGRF and hip moment dynamics may mask compensatory knee strategies among ACLR participants. Greater bilateral knee and hip power suggest a velocity-dependent compensatory strategy that mitigates surgical knee underloading during STS. Finally, because it can be observed immediately post-surgery to beyond the rehabilitation period, STS is a useful clinical assessment tool for observing hard-to-detect persistent knee loading asymmetries.

Background: Patients who have undergone Anterior Cruciate Ligament Reconstruction (ACLR) have a 6-24% chance of either re-tearing or having subsequent knee surgery. To date there have been no practical validated risk prediction models that can be easily implemented into clinical workflow for re-injury risk. Micro-Doppler radar (MDR) provides a promising solution. Objective: The purpose of this study was to investigate the predictive ability of MDR to identify persons with a previous ACLR relative to an age and sex matched healthy control. Methods: ACLR patients (n=81) and controls (n=100) performed drop box jump, sit to stand (STS), and walking trials as MDR signatures were collected. A 1D Convolutional Neural Network was developed to evaluate each activity individually followed by the development of a fusion model validation using all three activities. Results: The STS model individually achieved the highest overall accuracy of 82.3%, with a sensitivity of 71.6% and specificity of 91.0%. The fusion model using all activities achieved a peak overall accuracy to detect ACLR of 86.2%, 80.3% sensitivity, and 91% specificity. Conclusions: Currently, there is no clinically validated, efficient approach to objectively evaluate human motion at the point of care. When coupled with machine learning, MDR accurately differentiates ACLR from control groups by identifying complex biomechanical asymmetries, with classification performance comparable to or exceeding that of motion capture. Future research is needed to determine if MDR can be used in conjunction with risk prediction modeling. Key points: Micro-Doppler radar provides a promising new solution to identify important human motion asymmetries in clinical settings. Here we evaluated a group of patients who have a history of Anterior Cruciate Ligament reconstruction versus a control group. Simple movements performed in the presence of the micro-Doppler radar system were used to identify the 2 groups with accuracy comparable or superior to motion capture systems.