Propulsion is a fundamental component of walking, and our lab is working to develop methods to train propulsion in individuals with neuromotor impairment.
So far, our group has pursued two methods for training propulsion during walking. The first method is based on belt accelerations. Our method uses a split belt treadmill and trains propulsion by accelerating the treadmill belt of the trailing limb during push off. Belt accelerations require subjects to produce greater propulsive force to maintain their position on the treadmill and increase trailing limb angle through increased velocity of the accelerated limb. In our first study with this protocol, we established that exposure to belt accelerations induced after effects in propulsion mechanics, as shown by increased self-selected speed in a training group, which was not observed in a velocity-control group.
Treadmill belt accelerations during intervention as a function of gait cycle
Within participant change in gait speed from baseline walking, broken down by group (HA: High accelerations (7 m/s^2), LA: Low accelerations (2 m/s^2), VC: Velocity Control). Top Left: Group average change in GS across the experimental protocol, resampled in time. Shaded areas depict standard error. Estimated change in gait speed is reported for acceleration groups based on duration of applied accelerations. Top Right: Mean and standard error of group average change in GS in experimental phases of interest ( BL: baseline, E. TR: Early Training, L TR: Late Training, E. PT: early post-training, L. PT: late post-training). Asterisks denote significant change from baseline from post-hoc Tukey HSD analysis. Bottom: Histogram of responder analysis of after effects measured in the Early (right) and Late (left) post-training session.
Our second method is based on the application of pulses of joint torque to the hip using a robotic exoskeleton. We first conducted a biomechanical analysis to establish how humans modulate their joint torque under a factorial combination of gait speed and stride length, two parameters that are crucial for propulsion mechanics. We conducted an experiment with healthy control subjects instructed to walk on a treadmill at various speeds and asked to modulate stride length via visual feedback. Sagittal plane joint torques were extracted via an inverse dynamics analysis of instrumented treadmill and motion capture data.
We utilized a torque pulse approximation analysis to determine optimal timing and amplitude of torque pulses that approximate the difference in joint torque profiles resulting from different stride length conditions measured at different values of walking speed. Our group analysis generated a set of 16 pulse torque assistance profiles that were experimentally tested using our ALEX exoskeleton, with 2 active d.o.f., actuating about the hip and knee joints. In our first experiment, healthy control participants were exposed to the 16 joint torque profiles in single strides. In a second experiment, healthy control participants were exposed to a selected subset of 8 joint torque profiles in repeated strides. The effects and after-effects on hip extension and normalized propulsive impulse were assessed.
We observed that on the group level, participants showed adaptation to late flexion torque and early extension torque and learning effects with late extension torque and early flexion torque – in which all after-effects were positive. For the measure of normalized propulsive impulse we observed adaptation effects in which early pulse application for both late flexion torque conditions exhibited positive modulation and the early flexion torque condition exhibited significant positive modulation in late after-effects.
Stride-by-stride evolution of metrics of propulsion mechanics (hip extension – HE – and propulsive impulse – PI) from the experiment were processed and used to compare five state-space models that describe neuromotor adaptation to pulse torque application during walking. These models included single-state space (single-state), two-state space (two-state), two-state fast and slow (fast&slow), use-dependent and error-based learning (UDL), and modified use-dependent (mod. UDL) models. We evaluated the goodness-of-fit of each model to describe responses during and after exposure to robotic training using coefficient of determination (R2) and Akaike information criterion (AIC). Our findings indicate that the modified UDL model proposed in our lab showed the best goodness-of-fit compared to other motor adaptation models in terms of AIC values in 15 out of 16 conditions for describing group-level responses. Additionally, for participant-specific level responses, both the modified UDL and the complete two-state model have significantly better goodness-of-fit compared to the other models. (G. Kim and F. Sergi pre-print)
In our lab, we are able to measure propulsive force while walking on a treadmill using 6 degree-of-freedom force plates. These force plates are expensive and not always found in robotics labs, leaving some labs unable to measure propulsive force. We recently completed an experiment that compared nine models of kinematic and kinetic metrics of propulsion to estimate propulsive force. We found that kinematic measurements best predicted maximum anterior ground reaction force (AGRF) and propulsive impulse (PI). When using two factors to predict these outcomes, the maximum AGRF and PI normalized by bodyweight were best predicted by stride length and vertical ground reaction force (VGRF) at the time of peak AGRF. Maximum VGRF was a factor for both maximum AGRF and PI. Our models best predicted maximum AGRF using maximum VGRF and stride length (R2 = 0.92), as well as stride length alone (R2 = 0.91). (H. N. Cohen, M. Vasquez, F. Sergi pre-print)
Publications on this topic
H. N. Cohen, M. Vasquez, F. Sergi, “Estimating Propulsion Kinetics in Absence of a Direct Measurement of the Anterior Component of Ground Reaction Force”, 2024, doi: 10.1101/2024.02.19.581016, pre-print.
R. L. McGrath, F. Sergi, “Robot-Aided Training of Propulsion: Effects of Torque Pulses Applied to the Hip and Knee Joint Under User-Driven Treadmill Control”, 2023, pre-print. doi: 10.36227/techrxiv.23596251.v1
R. L. McGrath, F. Sergi, “Using Repetitive Control to Enhance Force Control During Human-Robot Interaction in Quasi-Periodic Tasks”, IEEE Transactions on Medical Robotics and Bionics, vol. 5, no. 1, pp. 79-87, Feb. 2023, doi: 10.1109/TMRB.2023.3237766, available online, pre-print.
R. L. McGrath, B. Bodt, F. Sergi, “Robot-aided Training of Propulsion During Walking Effects of Torque Pulses Applied to the Hip and Knee Joints During Stance”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 12, Nov. 2020, doi: 10.1109/TNSRE.2020.3039962, pre-print, available online.
A. Farrens, M. Lilley, F. Sergi, “Training Propulsion via Acceleration of the Trailing Limb”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 12, Oct. 2020, doi: 10.1109/TNSRE.2020.3032094, pre-print, available online.
A. J. Farrens, R. Marbaker, M. Liley, F. Sergi, “Training propulsion during walking: adaptation to accelerations of the trailing limb”, 16th International Conference on Rehabilitation Robotics, 2019, pre-print, available online.
R. L. McGrath, F. Sergi, “Single stride exposure to pulsed torque assistance provided by a robotic exoskeleton at the hip and knee joints”, 16th International Conference on Rehabilitation Robotics, 2019, pre-print, available online.
R. L. McGrath, M. L. Ziegler, M. Pires-Fernandes, B. A. Knarr, J. S. Higginson, and F. Sergi, “The effect of stride length on lower extremity joint kinetics at various gait speeds,” PLoS One, vol. 14, no. 2, p. e0200862, Feb. 2019, doi: 10.1371/journal.pone.0200862, pre-print, available online.
R. L. McGrath, M. Pires-Fernandes, B. Knarr, J. S. Higginson, F. Sergi, “Toward goal-oriented robotic gait training: the effect of gait speed and stride length on lower extremity joint torques”, IEEE/RAS-EMBS International Conference on Rehabilitation Robotics, London, UK, August 2017, pre-print, available online.
