Electromyography Assessment During Gait in a Robotic Exoskeleton for Acute Stroke

I got absolutely nothing out of this that would've helped at any time for my gait rehab.
https://www.frontiersin.org/articles/10.3389/fneur.2018.00630/full?

Ghaith J. Androwis^1,2, Rakesh Pilkar^1,3, Arvind Ramanujam¹ and Karen J. Nolan^1,2,3*

• ¹Human Performance and Engineering Research, Kessler Foundation, West Orange, NJ, United States
• ²Children's Specialized Hospital, Mountainside, NJ, United States
• ³Department of Physical Medicine and Rehabilitation, Rutgers–New Jersey Medical School, Newark, NJ, United States

Background: Robotic exoskeleton (RE) based gait training involves repetitive task-oriented movements and weight shifts to promote functional recovery. To effectively understand the neuromuscular alterations occurring due to hemiplegia as well as due to the utilization of RE in acute stroke, there is a need for electromyography (EMG) techniques that not only quantify the intensity of muscle activations but also quantify and compare activation timings in different gait training environments.

Purpose: To examine the applicability of a novel EMG analysis technique, Burst Duration Similarity Index (BDSI) during a single session of inpatient gait training in RE and during traditional overground gait training for individuals with acute stroke.

Methods: Surface EMG was collected bilaterally with and without the RE device for five participants with acute stroke during the normalized gait cycle to measure lower limb muscle activations. EMG outcomes included integrated EMG (iEMG) calculated from the root-mean-square profiles, and a novel measure, BDSI derived from activation timing comparisons.

Results: EMG data demonstrated volitional although varied levels of muscle activations on the affected and unaffected limbs, during gait with and without the RE. During the stance phase mean iEMG of the soleus (p = 0.019) and rectus femoris (RF) (p = 0.017) on the affected side significantly decreased with RE, as compared to without the RE. The differences in mean BDSI scores on the affected side with RE were significantly higher than without RE for the vastus lateralis (VL) (p = 0.010) and RF (p = 0.019).

Conclusions: A traditional amplitude analysis (iEMG) and a novel timing analysis (BDSI) techniques were presented to assess the neuromuscular adaptations resulting in lower extremities muscles during RE assisted hemiplegic gait post acute stroke. The RE gait training environment allowed participants with hemiplegia post acute stroke to preserve their volitional neuromuscular activations during gait iEMG and BDSI analyses showed that the neuromuscular changes occurring in the RE environment were characterized by correctly timed amplitude and temporal adaptations. As a result of these adaptations, VL and RF on the affected side closely matched the activation patterns of healthy gait. Preliminary EMG data suggests that the RE provides an effective gait training environment for in acute stroke rehabilitation.

Introduction

Recovery of function post stroke is based on neural adaptation, and progressive task specific repetitive training based on the principles of neuroplasticity (1, 2). While major advances have been made in early intervention for the treatment of patients post stroke, the majority of survivors have residual mobility challenges and hemiplegia (3, 4). Hemiplegia typically manifests in pronounced asymmetrical deficits and is one of the most common disabling impairments resulting from stroke (5). Asymmetrical gait can be associated with muscle weakness, leading to inefficient ambulation, balance control challenges and risk of musculoskeletal injury to the non-paretic limb (6, 7). Task-oriented, high-repetition movements can improve muscular strength, motor control and movement coordination in patients post stroke (2). The task-specific training pertains to the training driven to achieve a functional task such as walking rather than focusing on minimizing an impairment (8, 9). In acute phase, traditional gait rehabilitation administered by a physical therapist is strenuous, inconsistent (in terms of movements generated) and less intense (in terms of number of steps). Integrating robotic exoskeleton (RE) technology into standard of care programs during the critical acute phase when the injured nervous system is highly plastic could maximize repetitive practice (9, 10), improve functional outcome measurements and provide quality gait training (10, 11). Programmable RE technology can also be used to advance progression during treatment and under the guidance of a physical therapist can emulate some features of manual assistance in a consistent and reproducible manner (2). The RE based training involves repetitive task-oriented (gait) movements and weight shifts to promote functional recovery. RE gait training may lead to changes in muscle activation as it provides task-specific movements to the lower limbs, increased step dosing and may provide a more symmetrical gait pattern (12).

An additional challenge in acute stroke is that many patients have a difficult time producing volitional movements that can be practiced repeatedly especially during the acute stage. In order to recover from physiological and functional lower extremity deficits, the task-related activities should include contributions from appropriate muscle groups during practice of these movements (13). Using an RE during gait rehabilitation in the acute phase may allow volitional muscle activation and improved phasic coordination (activation timing) during walking. However, the accuracy of these muscle contributions should be tracked. Surface electromyography (EMG) is one of the most effective, non-invasive tools which provides easy access to underlying neuromuscular processes that cause muscles to generate force, produce movement and achieve any functional task (14). During gait, EMG data reveals characteristic patterns of activation associated with each involved muscle in terms of onset timings, burst durations and levels of activations (15). These characteristic patterns significantly differ between healthy and pathological gait and this information can be used to assess the levels of improvement in muscle function, motor control, and neuromuscular adaptations post rehabilitation interventions. Bilateral EMG recordings of lower extremities can be further utilized to compare changes on the paretic side with respect to non-paretic side to assess inter-limb synchronization post RE intervention in individuals with stroke related hemiplegia.

To effectively understand the alterations occurring due to the RE, there is a need for EMG techniques that not only quantify the intensity of muscle activations but also quantify and compare activation timings for a single muscle during different gait training environments (e.g., overground or RE assisted). Although EMG amplitude is one of the most common variables reported in the literature (14, 16, 17), it does not distinctively provide temporal information (on–off timings). Particularly, in a cyclic activity such as gait, it is not only important for lower extremity muscles to produce activations but also activate them at the accurate time, especially for individuals with neurological disability such as acute stroke (16). In the post-stroke gait rehabilitation setting, the need to assess temporal information is even more apparent as muscle activation timing may be altered due to, (1) hemiplegia secondary to stroke and (2) the presence of a RE. The temporal features extracted from EMG data can allow the assessment of accuracy of participant's volitional contributions during training but also assess the modifications that the RE guided gait training may have. Several techniques have been used to extract the temporal information of muscle activations; however, their applicability in the domain of RE based gait training in acute stroke is limited.

The purpose of this investigation was to examine the applicability of a novel EMG analysis technique, Burst Duration Similarity Index (BDSI) during a single session of inpatient gait training in a RE and during traditional over ground gait training for individuals with acute stroke. EMG outcomes included standard measures of integrated EMG (iEMG) calculated from the root-mean-square (RMS) profiles, and a novel measure, BDSI (18) which quantifies the similarity between the two muscle activations by measuring co-excitation (common active regions) and co-inhibition (common inactive regions) during gait. Using iEMG and BDSI EMG analyses techniques, we hypothesized that the RE gait training environment will preserve the volitional neuromuscular activations in acute stroke. Volitional neuromuscular activations represent the residual post stroke muscle function during walking in the lower limbs. Our secondary hypothesis is that the RE gait training environment will change the activation timing of lower extremity muscles, measured by applying the BDSI technique, to match established normative healthy gait muscle activation timing patterns (15).