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1. Using robot-assisted stiffness perturbations to evoke aftereffects useful to post-stroke gait rehabilitation

   https://doi.org/10.3389/frobt.2022.1073746  

   Chambers, Vaughn; Artemiadis, Panagiotis (January 2023, Frontiers in Robotics and AI)

   Stroke is a major global issue, affecting millions every year. When a stroke occurs, survivors are often left with physical disabilities or difficulties, frequently marked by abnormal gait. Post-stroke gait normally presents as one of or a combination of unilaterally shortened step length, decreased dorsiflexion during swing phase, and decreased walking speed. These factors lead to an increased chance of falling and an overall decrease in quality of life due to a reduced ability to locomote quickly and safely under one’s own power. Many current rehabilitation techniques fail to show lasting results that suggest the potential for producing permanent changes. As technology has advanced, robot-assisted rehabilitation appears to have a distinct advantage, as the precision and repeatability of such an intervention are not matched by conventional human-administered therapy. The possible role in gait rehabilitation of the Variable Stiffness Treadmill (VST), a unique, robotic treadmill, is further investigated in this paper. The VST is a split-belt treadmill that can reduce the vertical stiffness of one of the belts, while the other belt remains rigid. In this work, we show that the repeated unilateral stiffness perturbations created by this device elicit an aftereffect of increased step length that is seen for over 575 gait cycles with healthy subjects after a single 10-min intervention. These long aftereffects are currently unmatched in the literature according to our knowledge. This step length increase is accompanied by kinematics and muscle activity aftereffects that help explain functional changes and have their own independent value when considering the characteristics of post-stroke gait. These results suggest that repeated unilateral stiffness perturbations could possibly be a useful form of post-stroke gait rehabilitation. 

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2. Contributions of spatial and temporal control of step length symmetry in the transfer of locomotor adaptation from a motorized to a non-motorized split-belt treadmill

   https://doi.org/10.1098/rsos.202084  

   Gregory, Daniel L.; Sup, Frank C.; Choi, Julia T. (February 2021, Royal Society Open Science)

   null (Ed.)

   Walking requires control of where and when to step for stable interlimb coordination. Motorized split-belt treadmills which constrain each leg to move at different speeds lead to adaptive changes to limb coordination that result in after-effects (e.g. gait asymmetry) on return to normal treadmill walking. These after-effects indicate an underlying neural adaptation. Here, we assessed the transfer of motorized split-belt treadmill adaptations with a custom non-motorized split-belt treadmill where each belt can be self-propelled at different speeds. Transfer was indicated by the presence of after-effects in step length, foot placement and step timing differences. Ten healthy participants adapted on a motorized split-belt treadmill (2 : 1 speed ratio) and were then assessed for after-effects during subsequent non-motorized treadmill and motorized tied-belt treadmill walking. We found that after-effects in step length difference during transfer to non-motorized split-belt walking were primarily associated with step time differences. Conversely, residual after-effects during motorized tied-belt walking following transfer were associated with foot placement differences. Our data demonstrate decoupling of adapted spatial and temporal locomotor control during transfer to a novel context, suggesting that foot placement and step timing control can be independently modulated during walking. 

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3. Facilitation of motor adaptation using multiple gait rehabilitation interventions

   https://doi.org/10.3389/fresc.2024.1238139  

   Hoque, Adila; Kim, Seok Hun; Reed, Kyle B (October 2024, Frontiers in Rehabilitation Sciences)

   Escorpizo, Reuben (Ed.)

   The rate of adjustment in a movement, driven by feedback error, is referred to as the adaptation rate, and the rate of recovery of a newly adapted movement to its unperturbed condition is called the de-adaptation rate. The rates of adaptation and de-adaptation are dependent on the training mechanism and intrinsic factors such as the participant's sensorimotor abilities. This study investigated the facilitation of the motor adaptation and de-adaptation processes for spatiotemporal features of an asymmetric gait pattern by sequentially applying split-belt treadmill (SBT) and asymmetric rhythmic auditory cueing (ARAC). Methods: Two sessions tested the individual gait characteristics of SBT and ARAC, and the remaining four sessions consisted of applying the two interventions sequentially during training. The adjustment process to the second intervention is referred to as “re-adaptation” and is driven by feedback error associated with the second intervention. Results: Ten healthy individuals participated in the randomized six-session trial. Spatiotemporal asymmetries during the adaptation and post-adaptation (when intervention is removed) stages were fitted into a two-component exponential model that reflects the explicit and implicit adaptation processes. A double component was shown to fit better than a single-component model. The decay constants of the model were indicative of the corresponding timescales and compared between trials. Results revealed that the explicit (fast) component of adaptation to ARAC was reduced for step length and step time when applied after SBT. Contrarily, the explicit component of adaptation to SBT was increased when it was applied after ARAC for step length. Additionally, the implicit (slow) component of adaptation to SBT was inhibited when applied incongruently after ARAC for step time. These outcomes show that the role of working motor memory as a translational tool between different gait interventions is dependent on (i) the adaptation mechanisms associated with the interventions, (ii) the targeted motor outcome of the interventions; the effects of factors (i) and (ii) are specific to the explicit and implicit components of the adaptation processes; these effects are unique to spatial and temporal gait characteristics. 

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4. Self-selected step length asymmetry is not explained by energy cost minimization in individuals with chronic stroke

   https://doi.org/10.1186/s12984-020-00733-y  

   Nguyen, Thu M.; Jackson, Rachel W.; Aucie, Yashar; de Kam, Digna; Collins, Steven H.; Torres-Oviedo, Gelsy (December 2020, Journal of NeuroEngineering and Rehabilitation)

   null (Ed.)

   Abstract Background Asymmetric gait post-stroke is associated with decreased mobility, yet individuals with chronic stroke often self-select an asymmetric gait despite being capable of walking more symmetrically. The purpose of this study was to test whether self-selected asymmetry could be explained by energy cost minimization. We hypothesized that short-term deviations from self-selected asymmetry would result in increased metabolic energy consumption, despite being associated with long-term rehabilitation benefits. Other studies have found no difference in metabolic rate across different levels of enforced asymmetry among individuals with chronic stroke, but used methods that left some uncertainty to be resolved. Methods In this study, ten individuals with chronic stroke walked on a treadmill at participant-specific speeds while voluntarily altering step length asymmetry. We included only participants with clinically relevant self-selected asymmetry who were able to significantly alter asymmetry using visual biofeedback. Conditions included targeting zero asymmetry, self-selected asymmetry, and double the self-selected asymmetry. Participants were trained with the biofeedback system in one session, and data were collected in three subsequent sessions with repeated measures. Self-selected asymmetry was consistent across sessions. A similar protocol was conducted among unimpaired participants. Results Participants with chronic stroke substantially altered step length asymmetry using biofeedback, but this did not affect metabolic rate (ANOVA, p  = 0.68). In unimpaired participants, self-selected step length asymmetry was close to zero and corresponded to the lowest metabolic energy cost (ANOVA, p  = 6e-4). While the symmetry of unimpaired gait may be the result of energy cost minimization, self-selected step length asymmetry in individuals with chronic stroke cannot be explained by a similar least-effort drive. Conclusions Interventions that encourage changes in step length asymmetry by manipulating metabolic energy consumption may be effective because these therapies would not have to overcome a metabolic penalty for altering asymmetry. 

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5. Wearable Sensor-Based Step Length Estimation During Overground Locomotion Using a Deep Convolutional Neural Network

   https://doi.org/10.1109/EMBC46164.2021.9630060  

   Jin, Heejoo; Kang, Inseung; Choi, Gayeon; Molinaro, Dean D.; Young, Aaron J. (November 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   Step length is a critical gait parameter that allows a quantitative assessment of gait asymmetry. Gait asymmetry can lead to many potential health threats such as joint degeneration, difficult balance control, and gait inefficiency. Therefore, accurate step length estimation is essential to understand gait asymmetry and provide appropriate clinical interventions or gait training programs. The conventional method for step length measurement relies on using foot-mounted inertial measurement units (IMUs). However, this may not be suitable for real-world applications due to sensor signal drift and the potential obtrusiveness of using distal sensors. To overcome this challenge, we propose a deep convolutional neural network-based step length estimation using only proximal wearable sensors (hip goniometer, trunk IMU, and thigh IMU) capable of generalizing to various walking speeds. To evaluate this approach, we utilized treadmill data collected from sixteen able-bodied subjects at different walking speeds. We tested our optimized model on the overground walking data. Our CNN model estimated the step length with an average mean absolute error of 2.89 ± 0.89 cm across all subjects and walking speeds. Since wearable sensors and CNN models are easily deployable in real-time, our study findings can provide personalized real-time step length monitoring in wearable assistive devices and gait training programs. 

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