Electromyography Assessment During Gait in a Robotic Exoskeleton for Acute Stroke

Abstract

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.

A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

“…Stroke etiology was reported in 92 cases: 72 of which were ischemic in origin, 18 were hemorrhagic, and two were ischemic/hemorrhagic. Six studies did not provide information related to stroke type (Ping et al, 2013 ; He et al, 2014 ; Androwis et al, 2018 ; Sloot et al, 2018 ). The time from stroke to study participation was reported for 98 patients, with the majority ( N = 57) recruited in the chronic phase of stroke.…”

Clinical research of lower extremity exoskeleton robot In post-stroke hemiplegic patients

“…The area under the sEMG envelopes were calculated by numerical integration, which is called integral electromyogram (IEMG). IEMG is positively related with muscle strength and activity, which is a practical index for muscle function assessment [19,40] . Fig.5 B compares the IEMG of measured bilateral muscles between circumstances with or without exoskeleton assistance.…”

“…Through the analysis of the above clinical researches, it is found that the rehabilitation effect of exoskeleton robot varies in different stages of stroke and different joints of limbs [13][14][15][16] , the key to improving its clinical effect lies in the mechanism design and software control [17][18][19][20] , and there is no mature research paradigm of gait training assisted by exoskeleton robot for stroke patients [21][22][23] .…”

Surface Electromyography Applied to Gait Analysis: How to Improve Its Impact in Clinics?

“…• to support diagnosis (2,23) • to design complex surgical interventions [e.g., multilevel surgery of hemiplegic children (24,25)] • to design personalized rehabilitation protocols and objectively prove their effectiveness (e.g., outcome evaluation of a proprioceptive training in MS patients), including new rehabilitation trends exploiting exoskeletons, e.g., in acute stroke patients (26), neurorehabilitation with Functional Electrical Stimulation (FES) (27), and any other system providing biofeedback based on myoelectric control (28)(29)(30)(31) • to support clinical decision (e.g., appropriate candidate selection for botulin toxin injection and choice of the target muscles (32), evidence-based choice of the type of joint prosthesis to implant (15) • for therapy evaluation (e.g., to assess the effects of levodopa, or Deep Brain Stimulation on the muscle activation and muscle synergies of PD patients) (33)(34)(35) • for the production of quantitative reports to optimize patient's follow-up or to conduct longitudinal studies (16) • to evaluate muscle fatigue (e.g., in ergonomics and sports) (36-39) • to support forensic medicine with objective outcomes (e.g., to help medical insurance companies estimating a patient's risk, establishing adequate insurance compensations, unmasking simulators and avoiding frauds) (40,41).…”