Journal of Applied Biomechanics最新文献

Instrumented gait analysis has traditionally been isolated to laboratory, marker-based optoelectronic motion capture systems, which limits clinical uptake. Markerless motion capture (MMC) systems driven by trained machine learning algorithms offer high-throughput solutions for translational clinical opportunities. The aim of this study was to examine the day-to-day repeatability of discrete knee kinematic gait metrics in a healthy population using an MMC system uniquely installed in a hospital hallway. Twenty healthy adults (13 females, 7 males) participated in 3 overground hallway gait sessions, on average 11 days apart, using a novel MMC system setup. Intraclass correlation coefficients, standard errors of measurement, and minimal detectable changes were examined for each gait outcome. Results indicated good-to-excellent repeatability, with most (7/8) outcomes having intraclass correlation coefficient values over .86. Standard error of measurement values for all kinematic outcomes were less than 2.0°, and minimal detectable change values were less than 4.7°. Our novel setup of a hospital hallway MMC system produced highly repeatable gait kinematic metrics in a population of healthy adults. Repeatability errors from this study can be used as a healthy reference for future applications of this system.

Lower-extremity exoskeletons (EXOs) may be able to assist with performance and injury risk reduction for military-relevant activities, like walking with loads. However, the effects of EXOs on local dynamic stability (LDS), a measure of motor control, have not been established. Eleven active duty Army Soldiers (9 males, aged 22 [4] y) completed a familiarization session, followed by 2 testing sessions where they did (EXO) or did not (NoEXO) wear a pneumatic powered, knee-actuated EXO. Inertial measurement units were attached bilaterally to the shank and posterior pelvis. Participants completed a 2-mile ruck march on a treadmill at a self-selected pace (1.34 [0.10] m/s), carrying a load equal to 30% of body weight and an additional 9.07 kg for the EXO during that session. LDS was calculated using gyroscope data for 100 strides at the 0.25- (Start) and 2-mile (End) marks of the march. For the right shank, LDS was found to be significantly lower for EXO versus NoEXO (mean difference = 0.28, P < .01, partial η2 = .75). A similar effect was found for the left shank, and while not significant, the effect size was large (P = .07, partial η2 = .29). Finally, LDS was higher at the pelvis in the EXO versus NoEXO, and with a large effect size, although the results were not significant (P = .07, partial η2 = .29). Our results suggest that lower-extremity EXOs reduce distal LDS, which may point to the need for habituation periods for new users of EXOs.

Inertial Measurement Units (IMUs) enable accurate estimation of anatomical joint angles but require a sensor-to-segment calibration. Literature has presented several algorithms that address this gap; however, adequately comparing calibration performance is not trivial. This study compares 3 calibration methods: N-pose calibration (NP), functional calibration (FC), and manual alignment (MA) to estimate 3D wrist joint angles during single-plane and multiplane tasks. Thirteen healthy participants were instrumented with IMUs and optical markers to compute the range of motion error (ε), root mean squared error, and offset between the joint angles from the optical reference and each IMU calibration (NP, FC, and MA) as dependent variables. We then performed 3-way repeated-measures analyses of variance on each dependent variable to evaluate interactions between calibrations, tasks, and joint axes. NP showed the worst root mean squared error (8.34° [7.41°]) performance in the calibration main effect (η2G = .095) and calibration × tasks interaction (η2G = .121). In an exploratory analysis, FC performed best (main effect root mean squared error = 6.52° [4.47°]) in the offset calibration × axes interaction in single-plane (η2G = .160) tasks. Therefore, we recommend FC to optimally perform wrist calibration and against NP. These findings are viable in aiding the development of portable IMU-based clinical motion-tracking devices.

A novel shear force control task has previously been shown to elucidate different neuromuscular control strategies among individuals after anterior cruciate ligament injury, individuals following anterior cruciate ligament reconstruction, uninjured collegiate athletes, and uninjured recreational athletes. However, the reliability of the methodology has not been evaluated. The purpose of this study was to determine the reliability of this methodology in a population with no lower-extremity injury. Thirteen individuals (7 men, 24.7 [4.8] y, body mass index: 23.9 [3.6] kg/m2) completed a standing force control task in the medial/lateral and anterior/posterior directions for each leg on 2 separate occasions (1 wk apart). Intraclass correlation coefficient with 95% confidence intervals, standard error of measurement, and minimum detectable change were calculated to assess the reliability of largest Lyapunov exponent values. Intraclass correlation coefficient values across all measures were good to excellent (r = .78-.92). Reliability was highest in the medial/lateral direction at the right lower extremity (r = .92; 95% confidence interval, .75-.98, P = .025). The shear force control task exhibited good to excellent reliability across measures, suggesting that it can reliably measure force control variability. This methodology may provide insight into neuromuscular control strategies following injury.

This study investigated differences in muscle synergies in the trunk and lower limbs during single-leg hops at 30% (SLH30) and 100% (SLH100) of maximum distance to understand the neuromechanical mechanisms underlying longer hop distances. Unilateral surface EMG data were collected from 16 muscle groups in the trunk and lower limbs during both SLH30 and SLH100 in 10 healthy males. Nonnegative matrix factorization was used to extract muscle synergies. The number of muscle synergies in SLH100 was significantly higher than in SLH30 (P = .0078, effect size = 1.28), with median values of 4.0 (3.0-5.0) for SLH30 and 5.0 (4.0-6.0) for SLH100. We identified 4 shared muscle synergies between SLH30 and SLH100, signifying a foundational neuromuscular control strategy. In addition, muscle synergies specific to SLH100 demonstrated the involvement of abdominal muscles and hip and ankle extensor muscles, highlighting their contributions to achieving longer hopping distances. Interventions aimed at enhancing SLH performance for return to sport may benefit from incorporating exercises targeting these synergy patterns. However, it should be noted that SLH100 synergies primarily involved nonknee muscles, warranting caution when using SLH as an indicator of knee function as improvements in hop distance may not directly reflect knee-specific recovery.

This study quantified vertical ground reaction forces (vGRFs) and lower limb stiffness for both sexes walking and running with body-borne load over 2 surfaces. Nine males and 9 females had lower limb biomechanics quantified during a walk (1.3 m/s) and run (4.5 m/s) with (15 kg) and without (0 kg) body-borne load over a firm and soft foam surface. vGRF measures, and leg and lower limb joint stiffness were submitted to a linear mixed model. Loaded walking increased very GRF and stiffness measure (all: P < .016). Loaded running increased every GRF measure and knee stiffness (all: P < .033). The foam surface increased peak vGRF (P = .002, P = .010) and knee stiffness (P < .001, P = .004) during the walk and run, and leg (P < .001) and ankle (P = .025) stiffness during the run. Males walked with greater peak vGRF (P = .012), and stiffer hip and ankle (P = .026; P = .012), but ran with a stiffer knee on the foam (P = .041) and stiffer hip on the firm (P = .005) surface than females. Loaded walking and running may elevate injury risk by increasing vertical GRFs and lower limb stiffness. Injury risk may also increase for locomotion over a foam surface, especially for males.

Artificial neural networks (ANNs) are becoming a regular tool to support biomechanical methods, while physics-based models are widespread to understand the mechanics of body in motion. Thus, this study aimed to demonstrate the accuracy of recurrent ANN models compared with a physics-based approach in the task of predicting ground reaction forces and net lower limb joint moments during running. An inertial motion capture system and a force plate were used to collect running biomechanics data for training the ANN. Kinematic data from optical motion capture systems, sourced from publicly available databases, were used to evaluate the prediction performance and accuracy of the ANN. The linear and angular momentum theorems were applied to compute ground reaction forces and joint moments in the physics-based approach. The main finding indicates that the recurrent ANN tends to outperform the physics-based approach significantly (P < .05) at similar and higher running velocities for which the ANN was trained, specifically in the anteroposterior, vertical, and mediolateral ground reaction forces, as well as for the knee and ankle flexion moments, and hip abduction and rotation moments. Furthermore, this study demonstrates that the trained recurrent ANN can be used to predict running kinetic data from kinematics obtained with different experimental techniques and sources.