We aimed to determine a method for prescribing a standing prosthetic leg length (ProsL) that results in an equivalent running biological leg length (BioL) for athletes with unilateral (UTTA) and ...bilateral transtibial amputations (BTTA). We measured standing leg length of ten non-amputee (NA) athletes, ten athletes with UTTA, and five athletes with BTTA. All athletes performed treadmill running trials from 3 m/s to their maximum speed. We calculated standing and running BioL and ProsL lengths and assessed the running-to-standing leg length ratio (L
) at three instances during ground contact: touchdown, mid-stance, and take-off. Athletes with UTTA had 2.4 cm longer standing ProsL than BioL length (p = 0.030), but their ProsL length were up to 3.3 cm shorter at touchdown and 4.1 cm shorter at mid-stance than BioL, at speed 3-11.5 m/s. At touchdown, mid-stance, and take-off, athletes with BTTA had 0.01-0.05 lower L
at 3 m/s (p < 0.001) and 0.03-0.07 lower L
at 10 m/s (p < 0.001) in their ProsL compared to the BioL of NA athletes. During running, ProsL were consistently shorter than BioL. To achieve equivalent running leg lengths at touchdown and take-off, athletes with UTTA should set their running-specific prosthesis height so that their standing ProsL length is 2.8-4.5% longer than their BioL length, and athletes with BTTA should set their running-specific prosthesis height so that their standing ProsL lengths are at least 2.1-3.9% longer than their presumed BioL length. Setting ProsL length to match presumed biological dimensions during standing results in shorter legs during running.
Inertial measurement units (IMUs) are popular tools for estimating biomechanical variables such as peak vertical ground reaction force (GRFv) and foot-ground contact time (tc), often by using ...multiple sensors or predictive models. Despite their growing use, little is known about the effects of varying low-pass filter cutoff frequency, which can affect the magnitude of force-related dependent variables, the accuracy of IMU-derived metrics, or if simpler methods for such estimations exist. The purpose of this study was to investigate the effects of varying low-pass filter cutoff frequency on the correlation of IMU-derived peak GRFv and tc to gold-standard lab-based measurements. Thirty National Collegiate Athletics Association Division 1 cross country runners ran on an instrumented treadmill at a range of speeds while outfitted with a sacral-mounted IMU. A simple method for estimating peak GRFv from the IMU was implemented by multiplying the IMU’s vertical acceleration by the runner’s body mass. Data from the IMU were low-pass filtered with 5, 10, and 30 Hz cutoffs. Pearson correlation coefficients were used to determine how well the IMU-derived estimates matched gold-standard biomechanical estimations. Correlations ranged from very weak to moderate for peak GRFv and tc. For peak GRFv, the 10 Hz low-pass filter cutoff performed best (r = 0.638), while for tc the 5 Hz cut-off performed best (r = 0.656). These results suggest that IMU-derived estimates of force and contact time are influenced by the low-pass filter cutoff frequency. Further investigations are needed to determine the optimal low-pass filter cutoff frequency or a different method to accurately estimate force and contact time is suggested.
Stress fractures are injuries caused by repetitive loading during activities such as running. The application of advanced analytical methods such as machine learning to data from multiple wearable ...sensors has allowed for predictions of biomechanical variables associated with running-related injuries like stress fractures. However, it is unclear if data from a single wearable sensor can accurately estimate variables that characterize external loading during running such as peak vertical ground reaction force (vGRF), vertical impulse, and ground contact time. Predicting these biomechanical variables with a single wearable sensor could allow researchers, clinicians, and coaches to longitudinally monitor biomechanical running-related injury risk factors without expensive force-measuring equipment.
We quantified the accuracy of applying quantile regression forest (QRF) and linear regression (LR) models to sacral-mounted accelerometer data to predict peak vGRF, vertical impulse, and ground contact time across a range of running speeds.
Thirty-seven collegiate cross country runners (24 females, 13 males) ran on a force-measuring treadmill at 3.8-5.4 m/s while wearing an accelerometer clipped posteriorly to the waistband of their running shorts. We cross-validated QRF and LR models by training them on acceleration data, running speed, step frequency, and body mass as predictor variables. Trained models were then used to predict peak vGRF, vertical impulse, and contact time. We compared predicted values to those calculated from a force-measuring treadmill on a subset of data (
= 9) withheld during model training. We quantified prediction accuracy by calculating the root mean square error (RMSE) and mean absolute percentage error (MAPE).
The QRF model predicted peak vGRF with a RMSE of 0.150 body weights (BW) and MAPE of 4.27 ± 2.85%, predicted vertical impulse with a RMSE of 0.004 BW*s and MAPE of 0.80 ± 0.91%, and predicted contact time with a RMSE of 0.011 s and MAPE of 4.68 ± 3.00%. The LR model predicted peak vGRF with a RMSE of 0.139 BW and MAPE of 4.04 ± 2.57%, predicted vertical impulse with a RMSE of 0.002 BW*s and MAPE of 0.50 ± 0.42%, and predicted contact time with a RMSE of 0.008 s and MAPE of 3.50 ± 2.27%. There were no statistically significant differences between QRF and LR model prediction MAPE for peak vGRF (
= 0.549) or vertical impulse (
= 0.073), but the LR model's MAPE for contact time was significantly lower than the QRF model's MAPE (
= 0.0497).
Our findings indicate that the QRF and LR models can accurately predict peak vGRF, vertical impulse, and contact time (MAPE < 5%) from a single sacral-mounted accelerometer across a range of running speeds. These findings may be beneficial for researchers, clinicians, or coaches seeking to monitor running-related injury risk factors without force-measuring equipment.
The metabolic cost of walking is due to muscle force generated to support body weight (BW), external work performed to redirect and accelerate the center of mass (CoM), and internal work performed to ...swing the limbs and maintain balance. We hypothesized that BW support would incur a greater and lower percentage of Net Metabolic Power (NMP) for uphill and downhill slopes, respectively, compared to level-ground walking. Additionally, we hypothesized that mass redirection would incur a greater and lower percentage of NMP for uphill and downhill slopes, respectively compared to level-ground walking. 10 subjects walked at 1.25 m/s on 0°, ±3°, and ±6° slopes with reduced/added weight and added mass while we measured metabolic rates. We calculated NMP per Newton of reduced BW at each slope and found that BW support required 58% and 64% of the NMP to walk at +3° and +6°, respectively, both greater than the 15% required for level-ground walking (p < 0.025). We calculated NMP per kg of added mass at each slope and found that mass redirection required 19% and 23% of the NMP to walk at +3° and +6°, respectively, both lower than the 35% required for level-ground walking (p < 0.025). We found no significant differences in the percentage of NMP for BW support or mass redirection during downhill compared to level ground walking (p > 0.05). Our findings elucidate that the percentage of NMP attributed to BW support and mass redirection is different for sloped compared to level-ground walking. These results inform biomimetic assistive device designs aimed at reducing metabolic cost.
Upright standing involves small displacements of the center of mass about the base of support. These displacements are often quantified by measuring various kinematic features of the ...center-of-pressure trajectory. The plantar flexors have often been identified as the key muscles for the control of these displacements; however, studies have suggested that the hip abductor and adductors may also be important. The purpose of our study was to determine the association between the force capabilities of selected leg muscles and sway-area rate across four balance conditions in young (25 ± 4 years; 12/19 women) and older adults (71 ± 5 years; 5/19 women). Due to the marked overlap in sway-area rate between the two age groups, the data were collapsed, and individuals were assigned to groups of low- and high-sway area rates based on a k-medoid cluster analysis. The number of participants assigned to each group varied across balance conditions and a subset of older adults was always included in the low-sway group for each balance condition. The most consistent explanatory variable for the variance in sway-area rate was force control of the hip abductors and ankle dorsiflexors as indicated by the magnitude of the normalized force fluctuations (force steadiness) during a submaximal isometric contraction. The explanatory power of the regression models varied across conditions, thereby identifying specific balance conditions that should be examined further in future studies of postural control.
Humans change joint quasi-stiffness (
) and leg stiffness (k
) when running at different speeds on level ground and during uphill and downhill running. These mechanical properties can inform device ...designs for running such as footwear, exoskeletons and prostheses. We measured kinetics and kinematics from 17 runners (10 M; 7 F) at three speeds on 0°, ±2°, ±4° and ±6° slopes. We calculated ankle and knee
, the quotient of change in joint moment and angular displacement, and theoretical leg stiffness (k
) based on the joint external moment arms and
. Runners increased
at faster speeds (
< 0.01). Runners increased and decreased the ankle and knee contributions to k
, respectively, by 2.89% per 1° steeper uphill slope (
< 0.01) during the first half of stance. Runners decreased and increased ankle and knee joint contributions to k
, respectively, by 3.68% during the first half and 0.86% during the second half of stance per 1° steeper downhill slope (
< 0.01). Thus, biomimetic devices require stiffer
for faster speeds, and greater ankle contributions and greater knee contributions to k
during the first half of stance for steeper uphill and downhill slopes, respectively.
To improve locomotor performance, coaches and clinicians encourage individuals with unilateral physical impairments to minimize biomechanical asymmetries. Yet, it is unknown if biomechanical ...asymmetries
per se
, affect metabolic energy expenditure in individuals with or without unilateral impairments during running. Thus, inter-leg biomechanical asymmetries may or may not influence distance-running performance. Purpose: We sought to determine whether running with asymmetric step times affects metabolic rate in unimpaired individuals. Methods: Ten unimpaired individuals were instructed to run on a force-measuring treadmill at 2.8 m/s and contact the ground simultaneously to the beat of an audible metronome. The metronome either played at time intervals equal to the respective participant’s preferred step times (0% asymmetry), or at time intervals that elicited asymmetric step times between legs (7, 14, and 21% step time asymmetry); stride time remained constant across all trials. We measured ground reaction forces and metabolic rates during each trial. Results: Every 10% increase in step time and stance average vertical ground reaction force asymmetry increased net metabolic power by 3.5%. Every 10% increase in ground contact time asymmetry increased net metabolic power by 7.8%. More asymmetric peak braking and peak propulsive ground reaction forces, leg stiffness, as well as positive and negative external mechanical work, but not peak vertical ground reaction force, increased net metabolic power during running. Step time asymmetry increases the net metabolic power of unimpaired individuals during running. Therefore, unimpaired individuals likely optimize distance-running performance by using symmetric step times and overall symmetric biomechanics.
This study examined the effects of speed and leg prostheses on mediolateral (ML) foot placement and its variability in sprinters with and without transtibial amputations. We hypothesized that ML foot ...placement variability would: 1. increase with running speed up to maximum speed and 2. be symmetrical between the legs of non-amputee sprinters but asymmetrically greater for the affected leg of sprinters with a unilateral transtibial amputation. We measured the midline of the body (kinematic data) and center of pressure (kinetic data) in the ML direction while 12 non-amputee sprinters and 7 Paralympic sprinters with transtibial amputations (6 unilateral, 1 bilateral) ran across a range of speeds up to maximum speed on a high-speed force measuring treadmill. We quantified ML foot placement relative to the body's midline and its variability. We interpret our results with respect to a hypothesized relation between ML foot placement variability and lateral balance. We infer that greater ML foot placement variability indicates greater challenges with maintaining lateral balance. In non-amputee sprinters, ML foot placement variability for each leg increased substantially and symmetrically across speed. In sprinters with a unilateral amputation, ML foot placement variability for the affected and unaffected leg also increased substantially, but was asymmetric across speeds. In general, ML foot placement variability for sprinters with a unilateral amputation was within the range observed in non-amputee sprinters. For the sprinter with bilateral amputations, both affected legs exhibited the greatest increase in ML foot placement variability with speed. Overall, we find that maintaining lateral balance becomes increasingly challenging at faster speeds up to maximum speed but was equally challenging for sprinters with and without a unilateral transtibial amputation. Finally, when compared to all other sprinters in our subject pool, maintaining lateral balance appears to be the most challenging for the Paralympic sprinter with bilateral transtibial amputations.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
When humans hop with a passive-elastic exoskeleton with springs in parallel with both legs, net metabolic power (P
) decreases compared with normal hopping (NH). Furthermore, humans retain ...near-constant total vertical stiffness (
) when hopping with such an exoskeleton. To determine how spring stiffness profile affects P
and biomechanics, 10 subjects hopped on both legs normally and with three full-leg exoskeletons that each used a different spring stiffness profile at 2.4, 2.6, 2.8, and 3.0 Hz. Each subject hopped with an exoskeleton that had a degressive spring stiffness (DG
), where stiffness, the slope of force vs. displacement, is initially high but decreases with greater displacement, linear spring stiffness (LN
), where stiffness is constant, or progressive spring stiffness (PG
), where stiffness is initially low but increases with greater displacement. Compared with NH, use of the DG
, LN
, and PG
numerically resulted in 13-24% lower, 4-12% lower, and 0-8% higher P
, respectively, at 2.4-3.0 Hz. Hopping with the DG
reduced P
compared with NH at 2.4-2.6 Hz (
≤ 0.0457) and reduced P
compared with the PG
at 2.4-2.8 Hz (
< 0.001).
while hopping with each exoskeleton was not different compared with NH, suggesting that humans adjust leg stiffness to maintain overall stiffness regardless of the spring stiffness profile in an exoskeleton. Furthermore, the DG
provided the greatest elastic energy return, followed by LN
and PG
(
≤ 0.001). Future full-leg, passive-elastic exoskeleton designs for hopping, and presumably running, should use a DG
rather than an LN
or a PG
to minimize metabolic demand.
When humans hop at 2.4-3.0 Hz normally and with an exoskeleton with different spring stiffness profiles in parallel to the legs, net metabolic power is lowest when hopping with an exoskeleton with degressive spring stiffness. Total vertical stiffness is constant when using an exoskeleton with linear or nonlinear spring stiffness compared with normal hopping. In-parallel spring stiffness influences net metabolic power and biomechanics and should be considered when designing passive-elastic exoskeletons for hopping and running.
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