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.

Keywords: electromyography; hemiplegic gait; rehabilitation; robotic exoskeleton; stroke.

Figure 1

(A) Frontal view of the EksoGT, (B) Oblique view of EksoGT, (C) One representative participant in the commercially available RE device (EksoGT, Ekso Bionics, Inc. Richmond, CA, USA) during gait analysis and EMG data collection. (Media consent provided by the participant for publication).

Figure 2

Left RF EMG onset detection using TKEO – 8 standard deviation threshold method for a 58 year old representative participant (A) with and (B) without RE.

Figure 3

EMG activation of (A) the affected limb and (B) the unaffected limb for all participants (n = 5) during gait with and without RE. Shaded rectangular areas with red horizontal lines indicate when a muscle is active based on normative healthy adult gait, Perry et al. (15).

Figure 4

Mean iEMG of the TA, SOL, GA, BF, VL, and RF with and without the RE during the affected (A) stance and (B) swing phase of gait and the unaffected (C) stance and (D) swing phase of gait. *Within limb significant differences during stance and swing across gait training environments (p ≤ 0.05). ^a−jThe inter-limb comparisons during the stance and swing phases within the same gait training environment (with or without RE). a. TA affected vs. unaffected without RE during stance (p ≤ 0.05), b. GA affected vs. unaffected without RE during stance (p ≤ 0.05), c. BF affected vs. unaffected with RE during stance (p ≤ 0.05), d. BF affected vs. unaffected without RE during stance (p ≤ 0.05), e. RF affected vs. unaffected with RE during stance (p ≤ 0.05), f. SOL affected vs. unaffected with RE during swing (p ≤ 0.05), g. SOL affected vs. unaffected without RE during swing (p ≤ 0.05), h. GA affected vs. unaffected without RE during swing (p ≤ 0.05), i. BF affected vs. unaffected with RE during swing (p ≤ 0.05), j. BF affected vs. unaffected without RE during swing (p ≤ 0.05).

Figure 5

Mean BDSI calculated using Equation (2), by comparing (A) affected side with and without RE, (B) unaffected side with and without RE, (C) affected side compared to normative healthy gait muscle activation timing (15), with and without RE and (D) unaffected side compared to healthy gait with and without RE. The titles (for A,B) and the legends (for C,D) show the exact arguments used in the Equation (1) for computing BDSI. ^*p ≤ 0.05.

Figure 6

RMS EMG profiles for (A) SOL, (B) RF, and (C) VL of the affected side in the RE gait training environment for each study participant (P1 to P5). The shaded rectangular areas represent the periods of healthy muscle activations. Amplitude adaptations (shown as a green rectangle) represent the events where excessive muscle activation was reduced in the RE environment. Temporal adaptations (shown as a red rectangle) represent the events where the muscle activation was correctly time-shifted to the correct phase of the gait cycle in the RE environment.