Abstract

BackgroundStroke survivors often have lower extremity sensorimotor impairments, resulting in an inability to sufficiently recruit muscle activity at appropriate times in a gait cycle. Currently there is a lack of a standardized method that allows comparison of muscle activation in hemiparetic gait post-stroke to a normative profile.MethodsWe developed a new tool to quantify altered muscle activation patterns (AMAP). AMAP accounts for spatiotemporal asymmetries in stroke gait by evaluating the deviations of muscle activation specific to each gait sub-phase. It also recognizes the characteristic variability within the healthy population. The inter-individual variability of normal electromyography (EMG) patterns within some sub-phases of the gait cycle is larger compared to others, therefore AMAP penalizes more for deviations in a gait sub-phase with a constant profile (absolute active or inactive) vs variable profile. EMG data were collected during treadmill walking, from eight leg muscles of 34 stroke survivors at self-selected speeds and 20 healthy controls at four different speeds. Stroke survivors’ AMAP scores, for timing and amplitude variations, were computed in comparison to healthy controls walking at speeds matched to the stroke survivors’ self-selected speeds.ResultsAltered EMG patterns in the stroke population quantified using AMAP agree with the previously reported EMG alterations in stroke gait that were identified using qualitative methods. We defined scores ranging between ±2.57 as “normal”. Only 9% of healthy controls were outside “normal” window for timing and amplitude. Percentages of stroke subjects outside the “normal” window for each muscle were, Soleus = 79%; 73%, Medial Gastrocnemius = 62%; 79%, Tibialis Anterior = 62%; 59%, and Gluteus Medius = 48%; 51% for amplitude and timing component respectively, alterations were relatively smaller for the other four muscles. Paretic-propulsion was negatively correlated to AMAP scores for the timing component of Soleus. Stroke survivors’ self-selected walking speed was negatively correlated with AMAP scores for amplitude and timing of Soleus but only amplitude of Medial gastrocnemius (p < 0.05).ConclusionsOur results validate the ability of AMAP to identify alterations in the EMG patterns within the stroke population and its potential to be used to identify the gait phases that may require more attention when developing an optimal gait training paradigm.Trial registrationClinicalTrials.govNCT00712179, Registered July 3rd 2008

Highlights

  • Stroke survivors often have lower extremity sensorimotor impairments, resulting in an inability to sufficiently recruit muscle activity at appropriate times in a gait cycle

  • Current techniques do not account for altered gait mechanics; robustly identify the onset or termination of electromyographic (EMG) activity [1, 2]; or acknowledge normal gait variability [3]

  • We propose to use the absolute values of the altered muscle activation patterns (AMAP) scores for this purpose and average the scores of all six regions of the gait cycle, thereby obtaining two values for each muscle to assess the deviations in the muscle timing profile or amplitude expressed as: Total

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Summary

Introduction

Stroke survivors often have lower extremity sensorimotor impairments, resulting in an inability to sufficiently recruit muscle activity at appropriate times in a gait cycle. Quantitative understanding of walking in the post-stroke population includes assessment of muscle activity patterns in addition to biomechanical variables and clinical measures, existing techniques to identify abnormal muscle activity have significant limitations. One significant limitation of all these tools is that they do not account for the asymmetries i.e. phase-specific differences between healthy and post-stroke walking patterns (we have further discussed the methodology and limitations of these tools in comparison to the proposed tool in the discussion section under AMAP and existing tools for quantitative assessment of muscle activity). A third limitation is the large inter-individual variability within the healthy population that is not accounted for by current methods, which may result in over estimation of EMG pattern abnormalities while evaluating deviations from an averaged normal profile. We seek to develop a quantitative measure that can recognize the characteristic variability within the healthy population while quantifying deviations in the amplitude and timing of stroke survivors’ EMG profiles accounting for asymmetric hemiparetic gait

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