Journal of biomechanics最新文献

Cervicocephalic proprioception (CCP) is an important assessment item for people with a range of clinical conditions, where reduced CCP is associated with neck pain and imbalance. Reliability has been established for a range of positional and movements tests, but there is limited data regarding sense of force, particularly across three planes of movement. The current test–retest study assessed reliability when evaluating sense of force in healthy adults (8 males, 6 females, mean age 31.50 years [SD 10.14]) over two sessions, 4–7 days apart. A force matching protocol was used to evaluate reliability of absolute error (AE), constant error (CE), and variable error (VE) for 10 % and 25 % maximal voluntary contraction (MVC) target forces for flexion, extension, lateral flexion, and rotation. Participants were strapped to a chair to limit trunk movement and data was captured using a compressive force transducer fixed to an adjustable wall mount. Six trials were performed for each contraction-type, totaling 72 submaximal MVCs per session. ICC estimates for AE (0.15–0.77), CE (0.01–0.85), and VE (0.00–0.83) were varied and confidence intervals were mostly wide. Considering lower limits of confidence intervals, CE had best reliability values generally, but more specifically the most reliable contraction type and movement was 25 % MVC flexion (ICC 0.85, confidence interval 0.54–0.95). This study found that reliability for sense of force testing was dependent upon contraction, type of error, and target force utilized. Further reliability analysis should be performed when applying this test to measure validity outcomes in clinical populations.

The objective of this study was to evaluate the reliability of Modified Vector Coding in assessing the coordination and coordination variability of the lower limbs and pelvis during running and to determine the Minimal Detectable Change (MDC). Twenty-five healthy runners participated in a biomechanical analysis of treadmill running using a motion capture system. Modified vector coding was applied to assess the three-dimensional coordination among various pelvis and lower limb segmental couplings. Reliability was assessed using the Intraclass Correlation Coefficient (ICC), Standard Error of Measurement (SEM), MDC, and Bland-Altman analysis to ascertain measurement consistency, agreement, and the smallest clinically meaningful change that exceeds measurement error. The test–retest reliability for 33 of 42 segmental couplings analyzed was good to excellent, with ICC values ranging from 0.613 to 0.928 (p <0.05), which substantiates the robustness of modified vector coding in running biomechanics. However, nine couplings, particularly femur-tibia in the sagittal plane during midstance and foot in the frontal plane-tibia in the transverse plane during late stance, exhibited poor to moderate reliability. These findings underscore the need for cautious interpretation due to significant proportional bias (p <0.05). SEM and MDC provided insights into the precision and minimal clinically significant changes for each coupling. The findings confirm the reliability of modified vector coding for biomechanical analysis in running, with most couplings demonstrating consistent high reliability. Nevertheless, specific couplings should be interpreted with caution due to potential measurement errors. The application of MDC highlights the precision of modified vector coding in biomechanical analyses and emphasizes the importance of careful interpretation to improve clinical and research outcomes in running-related injuries.

Maintaining dynamic balance during transitional movements like sit-to-walk (STW) can be challenging for older adults. Age-related neuromuscular decline can alter movement in STW, such as rising with greater trunk flexion, narrowing the feet, or using arms to push off. Initial foot and arm position can affect subsequent movement biomechanics, with different ground reaction forces (GRFs) that stabilize and advance the body center of mass (COM). The purpose of this study was to quantify whole-body biomechanics and trunk control of STW transitions. Fifteen younger adults (18–35 years) and fifteen older adults (50–79 years) performed STW from four initial foot positions and two arm positions. Three-dimensional (3D) GRFs, 3D body COM displacement, and integrated electromyography values from the lumbar paraspinals and gluteus medius were evaluated. Younger adults generated greater mediolateral GRF ranges while rising, whereas older adults generated greater mediolateral GRF ranges when stepping forward suggesting different strategies to laterally control the body COM. Initial foot position affected the STW movement, with narrow foot positions having smaller body COM displacement than wide foot positions, associated with smaller medial GRFs to move the body COM toward the stance limb. Rising with arm support required less lumbar paraspinal excitation, which was further reduced when with a posteriorly offset foot. Gluteus medius activity was greater for older adults compared to younger adults in STW. Completing STW with arm support can reduce the muscle activity required to stabilize the torso when rising, which likely has implications for balance control and low back loading.

Recent reports have suggested that there may be a relationship between footstrike pattern and overuse injury incidence and type. With the recent increase in wearable sensors, it is important to identify paradigms where the footstrike pattern can be detected in real-time from minimal data. Machine learning was used to classify tibial acceleration data into three distinct footstrike patterns: rearfoot, midfoot, or forefoot. Tibial accelerometry data were collected during treadmill running from 58 participants who each ran with rearfoot, midfoot, and forefoot strike patterns. These data were used as inputs into an artificial neural network classifier. Models were created by using three distinct acceleration data sets, using the first 100%, 75%, and 40% of stance phase. All models were able to predict the footstrike pattern with up to 89.9% average accuracy. The highest error was associated with the identification of the midfoot versus forefoot strike pattern. This technique required no pre-selection of features or filtering of the data and may be easily incorporated into a wearable device to aid with real-time footstrike pattern detection.

The Achilles tendon enthesis (ATE) anchors the Achilles tendon into the calcaneus through fibrocartilaginous tissue. The latter is enriched in type II collagen and proteoglycans (PGs), both of which give the enthesis its capacity to withstand compressive stress. Because unloading and reloading induce remodeling of the ATE fibrocartilage (Camy et al., 2022), chronic changes in the mechanical load could modify the mechanical response under compressive stress. Therefore, we investigated the ATE fatigue behavior in mice, under cyclic compressive loading, after 14 days of hindlimb suspension and 6 days of reloading. In addition, we performed a qualitative histological study of PGs in ATE fibrocartilage. The mechanical behavior of ATE was impaired in unloaded mice. A significant loss of 27 % in Δd (difference between the maximum and minimum displacements) was observed at the end of the test. In addition, the hysteresis area decreased by approximately 27 % and the stiffness increased by over 45 %. The increased stiffness and loss of viscosity were thrice and almost twice those of the control, respectively. In the reloaded entheses, where the loss of Δd was not significant, we found a significant 28 % decrease in the hysteresis area and a 26 % increase in stiffness, both of which were higher regarding the control condition. These load-dependent changes in the mechanical response seem partly related to changes in PGs in the uncalficied part of the ATE. These findings highlight the importance of managing compressive loading on ATE when performing prophylactic and rehabilitation exercises.

This study aimed to evaluate clinical utility of 2D-markerless motion analysis (2DMMA) from a single camera during a reaching-sideways-task in individuals with dyskinetic cerebral palsy (DCP) by determining (1) concurrent validity by correlating 2DMMA against marker-based 3D-motion analysis (3DMA) and (2) construct validity by assessing differences in 2DMMA features between DCP and typically developing (TD) peers.

2DMMA key points were tracked from frontal videos of a single camera by DeepLabCut and accuracy was assessed against human labelling. Shoulder, elbow and wrist angles were calculated from 2DMMA and 3DMA (as gold standard) and correlated to assess concurrent validity. Additionally, execution time and variability features such as mean point-wise standard deviation of the angular trajectories (i.e. shoulder elevation, elbow and wrist flexion/extension) and wrist trajectory deviation by mean overshoot and convex hull were calculated from key points. 2DMMA features were compared between the DCP group and TD peers to assess construct validity.

Fifty-one individuals (30 DCP;21 TD; age:5–24 years) participated. An accuracy of approximately 1.5 cm was reached for key point tracking. While significant correlations were found for wrist (ρ = 0.810;p < 0.001) and elbow angles (ρ = 0.483;p < 0.001), 2DMMA shoulder angles were not correlated (ρ = 0.247;p = 0.102) to 3DMA. Wrist and elbow angles, execution time and variability features all differed between groups (Effect sizes 0.35–0.81;p < 0.05).

Videos of a reaching-sideways-task processed by 2DMMA to assess upper extremity movements in DCP showed promising validity. The method is especially valuable to assess movement variability.

Recent computational and experimental studies of intracranial aneurysms have revealed potential mechanisms of aneurysm bruits and murmurs, driven by flow instabilities rather than by stable pulsatile flow. Some of these studies have been conducted under the assumption of constant flow rate (steady flow); however the validity of this assumption has not been evaluated for high-frequency flow instability, or vibrations from fluid-structure interaction (FSI) simulations. We evaluated the time-averaged wall shear stress, flow instability and vibration amplitude of steady flow simulations, performed at both cycle-averaged and peak-systolic flow rates, and compared these to recent pulsatile FSI simulations. Wall shear stress fields of pulsatile flow (time-averaged and peak values) were well-approximated by the respective steady-flow FSI simulations, and the spatial distribution and frequency content of flow instability and vibrations were reasonably approximated by the steady flow simulations at peak-systolic flow rates. However, the level of flow instability and vibration was generally over-predicted by the steady flow simulations at peak-systolic flow rates as flow remained unstable for longer than in the pulsatile simulation, while no flow instability was detected for steady flow at cycle-averaged flow rates. Additionally, the amplitude of flow instability and vibration fluctuated considerably in the steady flow simulations, while the pulsatile simulations exhibited consistent vibration amplitudes (less than 10 % variation at peak systole between cycles). Finally, steady flow simulations at peak-systolic conditions required 2-3x more compute time than the pulsatile simulations for the same time duration. Therefore, we recommend using pulsatile flow simulations when investigating vibrations and flow instabilities.

Accurately estimating in vivo tendon load non-invasively remains a major challenge in biomechanics, which might be overcome by shear-wave tensiometry. Shear-wave tensiometry measures the speed of mechanically induced tendon shear waves by skin-mounted accelerometers. To gauge the feasibility and accuracy of this novel technique, we obtained patellar tendon shear wave speeds via shear-wave tensiometry during sustained or ramp voluntary contractions of the knee extensors in two experiments (n = 8 in both). In experiment one, participants produced a constant knee extension torque of ∼ 50 Nm at five different knee joint angles (i.e. variable tendon load), which resulted in estimated patellar tendon forces between 1005 ± 6N and 1182 ± 16 N. However, wave speed squared did not correlate with estimated tendon force within participants (r_rm(31) = -0.19, p = 0.278). In experiment two, averaged correlation coefficients between normalized wave speed squared and torque of maximal and submaximal voluntary contractions across participants ranged between r = 0.43 and r = 0.94, while the time-varying correlation between these normalized signals ranged from r = -0.99 to r = 1.00. Further, the mean absolute errors (MAEs) between normalized wave speed squared and normalized torque across participants ranged between 0.03 and 0.54, which were larger than the MAEs between normalized torque and normalized summed EMG amplitude from the superficial quadriceps muscles (0.03–0.54 versus 0.06–0.26, respectively). In conclusion, there was no simple relation between shear wave speed squared and patellar tendon load, which severely limits the feasibility of shear-wave tensiometry for accurately estimating in vivo tendon load at the knee joint.

The human foot is a complex structure comprising 26 bones, whose coordinated movements facilitate proper deformation of the foot, ensuring stable and efficient locomotion. Despite their critical role, the kinematics of foot bones during movement remain largely unexplored, primarily due to the absence of non-invasive methods for measuring foot bone kinematics. This study addresses this gap by proposing a neural network model for estimating foot bone movements using surface markers. To establish a mapping between the positions and orientations of the foot bones and 41 skin markers attached on the human foot, computed tomography scans of the foot with the markers were obtained with eleven healthy adults and thirteen cadaver specimens in different foot postures. The neural network architecture comprises four layers, with input and output layers containing the 41 marker positions and the positions and orientations of the nine foot bones, respectively. The mean errors between estimated and true foot bone position and orientation were 0.5 mm and 0.6 degrees, respectively, indicating that the neural network can provide 3D kinematics of the foot bones with sufficient accuracy in a non-invasive manner, thereby contributing to a better understanding of foot function and the pathogenetic mechanisms underlying foot disorders.

A three-partition distributed force platform (3P-DFP) is proposed to measure the plantar pressure distribution, and foot support characteristics are evaluated with the obtained pressure distribution information. Twenty-seven young adults and 40 elderly adults were randomly recruited to perform the test in three phases: double-leg stance with eyes closed, double-leg stance with eyes opened, and single-leg stance with eyes opened. The evaluation parameters of foot support surface characteristics and support point characteristics were calculated based on the average position information of the center of pressure in the lateral of sole, media of sole, posterior of heel, and entire pelma. The results showed that the support width of the foot sole was significantly greater in the elderly group (p <0.01), but the overall support area showed a decreasing trend. The CoP excursion in the media-lateral direction was significantly higher in the elderly group from double-leg stance to single-leg stance (p <0.01). The CoP trajectory in the elderly group tends to shift in a medial direction with increasing age or postural difficulty.