BACKGROUND:Researchers and clinicians often use gait speed to classify hemiparetic gait dysfunction because it offers clinical predictive capacity. However, gait speed fails to distinguish unique biomechanical characteristics that differentiate aspects of gait dysfunction. RESEARCH QUESTION:Here we describe a novel classification of hemiparetic gait dysfunction based on biomechanical traits of pelvic excursion. We hypothesize that individuals with greater deviation of pelvic excursion, relative to controls, demonstrate greater impairment in key gait characteristics. METHODS:We compared 41 participants (61.0 ± 11.2yrs) with chronic post-stroke hemiparesis to 21 non-disabled controls (55.8 ± 9.0yrs). Participants walked on an instrumented split-belt treadmill at self-selected walking speed. Pelvic excursion was quantified as the peak-to-peak magnitude of pelvic motion in three orthogonal planes (i.e., tilt, rotation, and obliquity). Raw values of pelvic excursion were compared against the distribution of control data to establish deviation scores which were assigned bilaterally for the three planes producing six values per individual. Deviation scores were then summed to produce a composite pelvic deviation score. Based on composite scores, participants were allocated to one of three categories of hemiparetic gait dysfunction with progressively increasing pelvic excursion deviation relative to controls: Type I (n = 15) - minimal pelvic excursion deviation; Type II (n = 20) - moderate pelvic excursion deviation; and Type III (n = 6) - marked pelvic excursion deviation. We assessed resulting groups for asymmetry in key gait parameters including: kinematics, joint powers temporally linked to the stance-to-swing transition, and timing of lower extremity muscle activity. RESULTS:All groups post-stroke walked at similar self-selected speeds; however, classification based on pelvic excursion deviation revealed progressive asymmetry in gait kinematics, kinetics and temporal patterns of muscle activity. SIGNIFICANCE:The progressive asymmetry revealed in multiple gait characteristics suggests exaggerated pelvic motion contributes to gait dysfunction post-stroke.

Kinematic and spatiotemporal gait parameters are known to scale with gait speed, though inter-joint coordination during swing remains consistent, at least across comfortable speeds. The purpose of this study was to determine whether coordination patterns serving limb clearance and shortening change across a range of gait speeds. We assessed 17 healthy adults walking overground at their self-selected speed and multiple, progressively slower speeds. We collected lower extremity kinematics with 3D motion analysis and quantified joint influence, or relative joint contributions, to limb clearance and shortening. We investigated changes in coordination using linear mixed models to determine magnitude and timing differences of joint influence across walking speeds. Joint influences serving limb clearance (hip, knee, and ankle) reduced considerably with slower walking speeds. Similarly, knee and ankle influences on limb shortening reduced with slower walking speeds. Temporally, joint influences on limb clearance varied across walking speeds. Notably, the temporal order of peak hip and knee influences reversed below typical self-selected walking speeds. For limb shortening, the timing of knee and ankle influences occurred later in the gait cycle as walking speed decreased. While relative joint contributions serve limb clearance and shortening scale with walking speeds, our results demonstrate that temporal coordination of limb clearance is altered in healthy individuals as walking speed falls below the range of typical self-selected walking speeds.

Three-dimensional (3D) kinematic analysis of gait holds potential as a digital biomarker to identify neuropathologies, monitor disease progression, and provide a high-resolution outcome measure to monitor neurorehabilitation efficacy by characterizing the mechanisms underlying gait impairments. There is a need for 3D motion capture technologies accessible to community, clinical, and rehabilitation settings. Image-based markerless motion capture (MLMC) using neural network-based deep learning algorithms shows promise as an accessible technology in these settings. In this study, we assessed the feasibility of implementing 3D MLMC technology outside the traditional laboratory environment to evaluate its potential as a tool for outcomes assessment in neurorehabilitation. A sample population of 166 individuals aged 9-87 years (mean 43.7, S.D. 20.4) of varied health history were evaluated at six different locations in the community over a 3-month period. Participants walked overground at self-selected (SS) and fastest comfortable (FC) speeds. Feasibility measures considered the expansion, implementation, and practicality of this MLMC system. A subset of the sample population (46 individuals) walked over a pressure-sensitive walkway (PSW) concurrently with MLMC to assess agreement of the spatiotemporal gait parameters measured between the two systems. Twelve spatiotemporal parameters were compared using mean differences, Bland-Altman analysis, and intraclass correlation coefficients for agreement (ICC_2,1) and consistency (ICC_3,1). All measures showed good to excellent agreement between MLMC and the PSW system with cadence, speed, step length, step time, stride length, and stride time showing strong similarity. Furthermore, this information can inform the development of rehabilitation strategies targeting gait dysfunction. These first experiments provide evidence for feasibility of using MLMC in community and clinical practice environments to acquire robust 3D kinematic data from a diverse population. This foundational work enables future investigation with MLMC especially its use as a digital biomarker of disease progression and rehabilitation outcome.

Stroke survivors often exhibit gait dysfunction which compromises self-efficacy and quality of life. Muscle Synergy Analysis (MSA), derived from electromyography (EMG), has been argued as a method to quantify the complexity of descending motor commands and serve as a direct correlate of neural function. However, controversy remains regarding this interpretation, specifically attribution of MSA as a neuromarker. Here we sought to determine the relationship between MSA and accepted neurophysiological parameters of motor efficacy in healthy controls, high (HFH), and low (LFH) functioning stroke survivors. Surface EMG was collected from twenty-four participants while walking at their self-selected speed. Concurrently, transcranial magnetic stimulation (TMS) was administered, during walking, to elicit motor evoked potentials (MEPs) in the plantarflexor muscles during the pre-swing phase of gait. MSA was able to differentiate control and LFH individuals. Conversely, motor neurophysiological parameters, including soleus MEP area, revealed that MEP latency differentiated control and HFH individuals. Significant correlations were revealed between MSA and motor neurophysiological parameters adding evidence to our understanding of MSA as a correlate of neural function and highlighting the utility of combining MSA with other relevant outcomes to aid interpretation of this analysis technique.

Introduction. Restoration of upper extremity (UE) functional use remains a challenge for individuals following stroke. Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive modality that modulates cortical excitability and is being explored as a means to potentially ameliorate these deficits. The purpose of this study was to evaluate, in the presence of chronic stroke, the effects of low-frequency rTMS to the contralesional hemisphere as an adjuvant to functional task practice (FTP), to improve UE functional ability. Methods. Twenty-two individuals with chronic stroke and subsequent moderate UE deficits were randomized to receive 16 sessions (4 times/week for 4 weeks) of either real-rTMS or sham-rTMS followed by 1-hour of paretic UE FTP. Results. No differences in UE outcomes were revealed between the real-rTMS and sham-rTMS intervention groups. After adjusting for baseline differences, no differences were revealed in contralesional cortical excitability postintervention. In a secondary analysis, data pooled across both groups revealed small, but statistically significant, improvements in UE behavioral measures. Conclusions. rTMS did not augment changes in UE motor ability in this population of individuals with chronic stroke. The chronicity of our participant cohort and their degree of UE motor impairment may have contributed to inability to produce marked effects using rTMS.

Background

Difficulty advancing the paretic limb during the swing phase of gait is a prominent manifestation of walking dysfunction following stroke. This clinically observable sign, frequently referred to as 'foot drop', ostensibly results from dorsiflexor weakness.

Objective

Here we investigated the extent to which hip, knee, and ankle motions contribute to impaired paretic limb advancement. We hypothesized that neither: 1) minimal toe clearance and maximal limb shortening during swing nor, 2) the pattern of multiple joint contributions to toe clearance and limb shortening would differ between post-stroke and non-disabled control groups.

Methods

We studied 16 individuals post-stroke during overground walking at self-selected speed and nine non-disabled controls who walked at matched speeds using 3D motion analysis.

Results

No differences were detected with respect to the ankle dorsiflexion contribution to toe clearance post-stroke. Rather, hip flexion had a greater relative influence, while the knee flexion influence on producing toe clearance was reduced.

Conclusions

Similarity in the ankle dorsiflexion, but differences in the hip and knee, contributions to toe clearance between groups argues strongly against dorsiflexion dysfunction as the fundamental impairment of limb advancement post-stroke. Marked reversal in the roles of hip and knee flexion indicates disruption of inter-joint coordination, which most likely results from impairment of the dynamic contribution to knee flexion by the gastrocnemius muscle in preparation for swing. These findings suggest the need to reconsider the notion of foot drop in persons post-stroke. Redirecting the focus of rehabilitation and restoration of hemiparetic walking dysfunction appropriately, towards contributory neuromechanical impairments, will improve outcomes and reduce disability.

Purpose: To examine the between-day absolute reliability of gait parameters acquired with Theia3D markerless motion capture for use in biomechanical and clinical settings. Methods: Twenty-one (7 M,14 F) participants aged between 18 and 73 years were recruited in community locations to perform two walking tasks: self-selected and fastest-comfortable walking speed. Participants walked along a designated walkway on two separate days.Joint angle kinematics for the hip, knee, and ankle, for all planes of motion, and spatiotemporal parameters were extracted to determine absolute reliability between-days. For kinematics, absolute reliability was examined using: full curve analysis [root mean square difference (RMSD)] and discrete point analysis at defined gait events using standard error of measurement (SEM). The absolute reliability of spatiotemporal parameters was also examined using SEM and SEM%. Results: Markerless motion capture produced low measurement error for kinematic full curve analysis with RMSDs ranging between 0.96° and 3.71° across all joints and planes for both walking tasks. Similarly, discrete point analysis within the gait cycle produced SEM values ranging between 0.91° and 3.25° for both sagittal and frontal plane angles of the hip, knee, and ankle. The highest measurement errors were observed in the transverse plane, with SEM >5° for ankle and knee range of motion. For the majority of spatiotemporal parameters, markerless motion capture produced low SEM values and SEM% below 10%. Conclusion: Markerless motion capture using Theia3D offers reliable gait analysis suitable for biomechanical and clinical use.

Muscle synergy analysis (MSA) is a mathematical technique that reduces the dimensionality of electromyographic (EMG) data. Used increasingly in biomechanics research, MSA requires methodological choices at each stage of the analysis. Differences in methodological steps affect the overall outcome, making it difficult to compare results across studies. We applied MSA to EMG data collected from individuals post-stroke identified as either responders (RES) or non-responders (nRES) on the basis of a critical post-treatment increase in walking speed. Importantly, no clinical or functional indicators identified differences between the cohort of RES and nRES at baseline. For this exploratory study, we selected the five highest RES and five lowest nRES available from a larger sample. Our goal was to assess how the methodological choices made before, during, and after MSA affect the ability to differentiate two groups with intrinsic physiologic differences based on MSA results. We investigated 30 variations in MSA methodology to determine which choices allowed differentiation of RES from nRES at baseline. Trial-to-trial variability in time-independent synergy vectors (SVs) and time-varying neural commands (NCs) were measured as a function of: (1) number of synergies computed; (2) EMG normalization method before MSA; (3) whether SVs were held constant across trials or allowed to vary during MSA; and (4) synergy analysis output normalization method after MSA. MSA methodology had a strong effect on our ability to differentiate RES from nRES at baseline. Across all 10 individuals and MSA variations, two synergies were needed to reach an average of 90% variance accounted for (VAF). Based on effect sizes, differences in SV and NC variability between groups were greatest using two synergies with SVs that varied from trial-to-trial. Differences in SV variability were clearest using unit magnitude per trial EMG normalization, while NC variability was less sensitive to EMG normalization method. No outcomes were greatly impacted by output normalization method. MSA variability for some, but not all, methods successfully differentiated intrinsic physiological differences inaccessible to traditional clinical or biomechanical assessments. Our results were sensitive to methodological choices, highlighting the need for disclosure of all aspects of MSA methodology in future studies.