The relationship between relative aerobic load,energy cost,and speed of walking in individuals post-stroke

BackgroundIndividuals post-stroke walk slower than their able-bodied peers, which limits participation. This might be attributed to neurological impairments, but could also be caused by a mismatch between aerobic capacity and aerobic load of walking leading to an unsustainable relative aerobic load at most economic speed and preference for a lower walking speed.Research questionWhat is the impact of aerobic capacity and aerobic load of walking on walking ability post-stroke?MethodsForty individuals post-stroke (more impaired N = 21; preferred walking speed (PWS)<0.8 m/s, less impaired N = 19), and 15 able-bodied individuals performed five, 5-minute treadmill walking trials at 70 %, 85 %, 100 %, 115 % and 130 % PWS. Energy expenditure (mlO₂/kg/min) and energy cost (mlO₂/kg/m) were derived from oxygen uptake (${\dot{\text{V}}\text{O}}_2$). Relative load was defined as energy expenditure divided by peak aerobic capacity (%${\dot{\text{V}}\text{O}}_2$peak) and by ${\dot{\text{V}}\text{O}}_2$ at ventilatory threshold (%${\dot{\text{V}}\text{O}}_2$-VT). Relative load and energy cost at PWS were compared with one-way ANOVA’s. The effect of speed on these parameters was modeled with Generalized Estimating Equations.ResultsBoth more and less impaired individuals post-stroke showed lower PWS than able-bodied controls (0.44 [0.19−0.76] and 1.04 [0.81−1.43] vs 1.36 [0.89−1.53] m/s) and higher relative load at PWS (50.2 ± 14.4 and 51.7 ± 16.8 vs 36.2 ± 7.6 %${\dot{\text{V}}\text{O}}_2$peak and 101.9 ± 20.5 and 97.0 ± 27.3 vs 64.9 ± 13.8 %${\dot{\text{V}}\text{O}}_2$-VT). Energy cost at PWS of more impaired (0.30 [.19–1.03] mlO₂/kg/m) was higher than less-impaired (0.19[0.10−0.24] mlO₂/kg/m) and able-bodied (0.15 [0.13−0.18] mlO₂/kg/m). For post-stroke individuals, increasing walking speed above PWS decreased energy cost, but resulted in a relative load above endurance threshold.SignificanceIndividuals post-stroke seem to reduce walking speed to prevent unsustainably high relative aerobic loads at the expense of reduced economy. When aiming to improve walking ability post-stroke, it is important to consider training aerobic capacity.     

Lower body kinematics estimation from wearable sensors for walking and running: A deep learning approach

Background:Inertial measurement units (IMUs) are promising tools for collecting human movement data. Model-based filtering approaches (e.g. Extended Kalman Filter) have been proposed to estimate joint angles from IMUs data but little is known about the potential of data-driven approaches.Research question:Can deep learning models accurately predict lower limb joint angles from IMU data during gait?Methods:Lower-limb kinematic data were simultaneously measured with a marker-based motion capture system and running leggings with 5 integrated IMUs measuring acceleration and angular velocity at the pelvis, thighs and tibias. Data acquisition was performed on 27 participants (26.5 (3.9) years, 1.75 (0.07) m, 68.3 (10.0) kg) while walking at 4 and 6 km/h and running at 8, 10, 12 and 14 km/h on a treadmill. The model input consists of raw IMU data, while the output estimates the joint angles of the lower body. The model was trained with a nested $k$-fold cross-validation and tested considering a user-independent approach. Mean error (ME), mean absolute error (MAE) and Pearson correlation coefficient ($r$) were computed between the ground truth and predicted joint angles.Results:MAE for the DOFs ranged from 2.2(0.9) to 5.1(2.7)$°$ with an average of 3.6(2.1)$°$. $r$ ranged from 0.67(0.23) to 0.99(0.01) with moderate correlation ($0.4\le r<0.7$) was found for the hip right rotation and lumbar extension, strong correlation ($0.7\le r<0.9$) was found for the hip left rotation and ankle right/left inversion while all other DOFs showed very strong correlation ($r\ge 0.9$).Significance:The proposed model can reliably predict joint kinematics for walking, running and gait transitions without specific knowledge about the body characteristics of the wearer, or the position and orientation of the IMU relative to the attached segment. These results have been validated with treadmill gait, and have not yet been confirmed for gait in other settings.     

Both a single sacral marker and the whole-body center of mass accurately estimate peak vertical ground reaction force in running

BackgroundWhile running, the human body absorbs repetitive shocks with every step. These shocks can be quantified by the peak vertical ground reaction force ($F_{v,\text{m}\text{a}\text{x}}$). To measure so, using a force plate is the gold standard method (GSM), but not always at hand. In this case, a motion capture system might be an alternative if it accurately estimates $F_{v,\text{m}\text{a}\text{x}}$.Research questionThe purpose of this study was to estimate $F_{v,\text{m}\text{a}\text{x}}$ based on motion capture data and validate the obtained estimates with force plate-based measures.MethodsOne hundred and fifteen runners participated at this study and ran at 9, 11, and 13 km/h. Force data (1000 Hz) and whole-body kinematics (200 Hz) were acquired with an instrumented treadmill and an optoelectronic system, respectively. The vertical ground reaction force was reconstructed from either the whole-body center of mass (COM-M) or sacral marker (SACR-M) accelerations, calculated as the second derivative of their respective positions, and further low-pass filtered using several cutoff frequencies (2−20 Hz) and a fourth-order Butterworth filter.ResultsThe most accurate estimations of $F_{v,\text{m}\text{a}\text{x}}$ were obtained using 5 and 4 Hz cutoff frequencies for the filtering of COM and sacral marker accelerations, respectively. GSM, COM-M, and SACR-M were not significantly different at 11 km/h but were at 9 and 13 km/h. The comparison between GSM and COM-M or SACR-M for each speed depicted root mean square error (RMSE) smaller or equal to 0.17BW (≤6.5 %) and no systematic bias at 11 km/h but small systematic biases at 9 and 13 km/h (≤0.09 BW). COM-M gave systematic biases three times smaller than SACR-M and two times smaller RMSE.SignificanceThe findings of this study support the use of either COM-M or SACR-M using data filtered at 5 and 4 Hz, respectively, to estimate $F_{v,\text{m}\text{a}\text{x}}$ during level treadmill runs at endurance speeds.     

Reliability of walking and stair climbing kinematics in a young obese population using a standard kinematic and the CGM2 model

Background:Recently, the successor of the Conventional Gait Model, the CGM2 was introduced. Even though achievable reliability of gait kinematics is a well-assessed topic in gait analysis for several models, information about reliability in difficult study samples with high amount of subcutaneous fat is scarce and to date, not available for the CGM2. Therefore, this study evaluated the test–retest reliability of the CGM2 model for difficult data with high amount of soft tissue artifacts.Research question:What is the test–retest reliability of the CGM2 during level walking and stair climbing in a young obese population? Is there a clinically relevant difference in reliability between a standard direct kinematic model and the CGM2?Methods:A retrospective test–retest dataset from eight male and two female volunteers was used. It comprised standard 3D gait analysis data of three walking conditions: level walking, stair ascent and descent. To quantify test–retest reliability the Standard Error of Measurement (SEM) was calculated for each kinematic waveform for a direct kinematic model (Cleveland clinic marker set) and the CGM2.Results:Both models showed an acceptable level of test–retest reliability in all three walking conditions. However, SEM ranged between two and five degrees (${}^{\circ }$) for both models and, thus, needs consideration during interpretation. The choice of model did not affect reliability considerably. Differences in SEM between stair climbing and level walking were small and not clinically relevant ($<$1°).Significance:Results showed an acceptable level of reliability and only small differences between the models. It is noteworthy, that the SEM was increased during the first half of swing in all walking conditions. This might be attributed to increased variability resulting for example from inaccurate knee and ankle axis definitions or increased variability in the gait pattern and needs to be considered during data interpretation.     

Analysis of sloped gait: How many steps are needed to reach steady-state walking speed after gait initiation?

BackgroundGait initiation in level walking is suggested to take three steps before reaching steady-state walking speed. In sloped gait, it is not clear if the general recommendation of level gait can be used.Research questionThe aim of this study was to investigate (1) if steady-state walking speed is reached within four steps in sloped gait, and (2) to what extent the number of initial steps cause differences in step length, cadence and ground reaction force (GRF).MethodsFourteen healthy participants walked on an instrumented ramp at inclinations of 0°, ±6°, ±12°, and ±18°, covering slight (clinical application) to steep (hiking and mountaineering) slopes. The starting position on the ramp was adjusted to collect each of the first to fourth step using a 12 infrared-camera motion capture system and two force plates. For each slope condition steady-state walking speed was determined using the ratio of the braking and propulsion impulse (ratio p_ap; $\frac{p_{braking}}{p_{propulsion}}$) and the resultant Centre of Mass (CoM) speed (vel_CoM). Statistical differences between steps were calculated by using a Friedman ANOVA and pairwise post-hoc Wilcoxon tests.ResultsIn all inclinations, ≥90 % (uphill) and ≥95 % (downhill) of steady-state speed regarding ratio p_ap and maximum vel_CoM was reached with the 3rd step. In the level and uphill condition the 4th step showed a slight decrease in vel_CoM. In uphill and downhill condition, the acceleration was mainly generated due to the increase in cadence with significant increases between the 1st and 2nd step as well as between the 2nd and 3rd step. A significant increase in step length was only observed in the uphill conditions.SignificanceSteady-state walking speed was reached with the 3rd step and thus, walkways which allow for two initial steps seem to be appropriate for uphill and downhill gait analysis for inclinations up to ±18°.