Eight stroke survivors and eight healthy subjects matched by age, height, and gender were recruited in this study (Table 1). The inclusion criteria for the stroke survivors included (i) the occurrence of a first stroke with unilateral hemiparesis lesions confirmed by magnetic resonance imaging or computed tomography; (ii) an interval of at least 3 months post-stroke; (iii) the ability to step across an obstacle height of 30% leg length; and (iv) the ability to sign an informed consent form. This study was approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University. All procedures were conducted according to the Declaration of Helsinki and all subjects provided written consent before the experiments. The motor function of stroke survivors was evaluated by an experienced physiotherapist based on the Berg Balance Scale (BBS) and Fugl-Meyer assessment (FMA) for lower extremities.

Circular silver–silver chloride (Ag–AgCl) electrodes with a diameter of 5 mm and inter-electrode distance of 20 mm were bilaterally attached to the bellies of the rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and medial gastrocnemius (MG) of the lower limbs of the subjects. The muscle groups showed obvious changes when stepping over obstacles (20). Eight pre-amplified wireless transmission modules (DTS Noraxon, Scottsdale, AZ, USA) at a gain of 4,000 were linked with the electrodes to record EMG signals at a sample frequency of 1 kHz.

A total of 35 spherical 15-mm infrared-reflective markers were fastened to the subject’s whole body according to the Vicon Plug-In Gait marker placement. A 6-camera 3D motion analysis system (Vicon Motion Systems, Oxford, UK) recorded the marker positions at a sample frequency of 100 Hz. Two force plates (464 mm × 508 mm × 83 mm, AMTI, Watertown, MA, USA) at a sample frequency of 1 kHz were placed in the middle of the path with the obstacle between them. All the EMG, kinematic, and kinetic signals were recorded simultaneously and processed by the Vicon Nexus operating system.

Anthropometric characteristics were measured before the gait analysis (height, leg length, and bodyweight). Leg length was measured with scaled tape from the anterior superior iliac spine to the lateral malleolus and used to calculate the obstacle height of each individual. The electrodes and spherical markers were then attached to the corresponding locations on the subject. Before the electrode placement, the area around the muscles were shaved and cleaned with alcohol, and surgical tape was used as appropriate around the electrode and amplifier to obtain better EMG signals.

When the preparation was finished and the subjects had enough rest, the subjects were instructed to walk at a self-selected speed with bare feet along an 8-m walkway with a height-adjustable obstacle placed midway. The leading limb was defined as the first limb to cross the obstacle. The stroke subjects were instructed to use their affected leg as the leading limb of the obstacle crossing, and the healthy controls were instructed to use their dominant leg. The obstacle was set to three height conditions (10, 20, and 30% leg length). The three height conditions were performed in random order, and three trials of each height condition were recorded. Trials in which the subjects touched the obstacle were ignored and excluded from analysis. Prior to the trials, subjects visually and manually inspected the obstacle, and then practiced two to three trials according to the therapist’s instructions. Subjects were reminded to perform the task within their limits of safety and stop if they felt at risk. A therapist accompanied the subject and walked to the side to provide protection and assistance if required.

After all the trials were finished, the subject was asked to rest for a few minutes and then instructed to lay supine with the tested limb placed at 90° hip and knee flexion, and the other limb resting in neutral to perform maximum voluntary contraction (MVC) tests (21). Another experienced therapist held a hand-held dynamometer (MicroFET3, Hoggan Inc., UT, USA; with the precision of 0.4 N and range from 13 to 1,330 N) stably as a resistance at the corresponding position of the measured joint (22), and the subject used tested muscle group to push maximally against the hand-held dynamometer for about 5 s. To measure the MVC of the TA and MG, the hand-held dynamometer was placed proximally to metatarsophalangeal joints on dorsal and plantar surface of foot. To measure the MVC of the RF and BF, the hand-held dynamometer was placed proximally to ankle on anterior and posterior surface of leg (23). The subject performed 2–3 submaximum voluntary contractions before the MVC test began to become familiar with the test procedure. MVC test included knee flexion and extension, dorsiflexion, and plantar flexion, and the MVC was recorded three times for each muscle. During the MVC procedure, subjects were verbally encouraged to ensure maximal recruitment.

For all MVC and gait trials, raw EMG signals were collected at 1 kHz, band-pass filtered through a fourth-order Butterworth filter with a bandwidth of 10–350 Hz, full-wave rectified, and low-pass filtered through a second-order Butterworth filter with a cut off frequency of 6 Hz. The root mean square was calculated during a particular phase of the gait. The filtered signals from the gait trials were then normalized to the MVC for each muscle to determine the relative activation levels. The calculation of the cocontraction index (CI) required two more steps with a linear envelope: (1) subtraction of the average resting EMG activity and (2) normalization to the maximum value of EMG activation in each muscle during the MVC tests (24). The CI value is given by CI=1T∫TAij(t)dt where A_ij(t) is the overlapping activity of EMG linear envelopes after subtraction and normalization for the agonist and antagonist muscles i and j, T is the length of the signal period. The CI value varies from 0 to 1. Zero means there is no overlapping of the two EMG envelopes, and 1 means the two muscles are fully activated to 100% MVC during the trial.

The MPF was calculated using the band-pass filtered signals (through a fourth-order Butterworth filter with a bandwidth of 10–350 Hz) for each time window (the stance, swing, or entire gait cycle). The MPF value is given by MPF=∫0∞fP(f)df∫0∞P(f)df where P(f) indicates the power intensity curve and f indicates the frequency. The kinematic data were filtered by a 20-Hz low-pass Butterworth filter. We considered the toe-off time to be when the toe marker was 2 mm off the ground and the heel-strike time as when the force platform received enough signals to make the measurement reliable (25). The gait cycle was then divided into a swing phase and stance phase for a single lower limb.

Descriptive statistics (mean values and SD) were calculated for all dependent variables. The normalized EMG activation, CI, and MPF values were subjected to a repeated-measures two-way (group: stroke and control × obstacle height: 10, 20, and 30% of leg length) analysis of variance (ANOVA). The ANOVA results were adjusted using a Bonferroni post hoc test. If there was an interaction between the two effects, then one-way ANOVA was separately performed for the group effect and height effect. Pearson product–moment correlations were used to examine the relationships between the calculated variables and clinical scales. The level of significance was set at an alpha level of 0.05. All statistical analyses were done using SPSS 19 statistical software.

When crossing the obstacles, the two groups encountered the same mechanical challenge (obstacle heights equal to the same percentage of leg length), but the stroke survivors showed greater overall muscle activation levels than the healthy controls in both the leading limb and the trailing limb (Table 2). Postural stability and the ability to maintain balance were impaired after stroke. Kirker et al. demonstrated that while a normal pattern of hemiparetic muscle activation was found in stepping, these muscles remained badly impaired in response to a perturbation and compensated by increased activity of the non-paretic muscles (11). Obstacle crossing challenges the stroke survivors to the limits of their capacity, and they are required to activate a greater level of their neuromuscular capacity to walk and safely step across obstacles. This may cause more serious postural instability (6). Similar results were also found in children with cerebral palsy. The RF and MG activation have been reported to increase in the swing phase of gait, which is considered an abnormal activation pattern that results from muscle weakness caused by cerebral palsy (26). According to Hahn et al., the relatively higher activation level might lead to muscular fatigue and place stroke survivors at higher risk of falls (10).

We found that the activation level of TA increased with increasing obstacle height in the swing phase of both leading and trailing limbs in stroke survivors. The TA muscle is the primary ankle dorsiflexor and is activated from the initial swing to let the ankle joint turn into a neutral position. However, Antonopoulos et al. found no significant height effect of the activation of the TA muscle for young adults during obstacle crossing (27). The increased activation level of TA in stroke may related to spasticity. Because of the excessive coactivation of the antagonist (MG), the increased activation level of TA was necessary to ensure a safe clearance during obstacle crossing.

Although cocontraction is inefficient for joint movement, it might be important for providing joint stability, especially in tasks like obstacle crossing (14). When the leading limb steps over an obstacle, the cocontraction of the TA and MG is greater in the stroke survivors compared with the healthy controls and also increased with increasing obstacle height (Figure 2C). The dorsiflexor (TA) strength is weakened after stroke, and the cocontraction of the antagonist (MG) might reduce the dorsiflexion range but increase the stability during the swing phase to ensure safe crossing (28). Also, the cocontraction of thigh muscles (BF and RF) is greater in the post-obstacle stance phase of the leading limb of the stroke survivors compared with the healthy controls, which is helpful to maintain balance by controlling the knee position during loading (29) (Figure 2B).

The greater cocontraction of the two pairs of lower limb muscles with increasing height also indicated muscle weakness on the paretic side of stroke survivors, who needed greater cocontraction to maintain balance when crossing obstacles of higher heights. Muscle cocontraction is also related to postural stability and dynamic strength in osteoarthritis (30), cerebral palsy (31), and Parkinson’s disease (32). The increased muscle cocontraction is a metabolically costly process (33), but it may help preserve some mobility in those with weakness (34).

When the trailing limb steps over the obstacle, the cocontraction of the TA and MG also showed a height effect, but there was no significant difference between groups during the swing phase (Figure 2H). This indicates that the cocontraction might be an adaptation strategy for both stroke survivors and healthy controls to increase postural stability. Both of the two pairs of lower limb muscles showed greater cocontraction in stroke survivors during the trailing limb stance phase (Figures 2E,G), which could be attributed to the need for greater stability and motor adaptation to the weakness of the affected leading limb to support body weight (35, 36). With motor recovery, the muscle strength could be increased and enable a more efficient strategy with decreased muscle cocontraction. Hu et al. investigated the motor function recovery process in cases of chronic stroke and found that CI values decreased during the recovery process as motor function improved (24). Assessing muscle cocontraction helps us to understand the coordination mechanism in stroke survivors and the adaptation strategy they use to ensure safe crossing.

The overall power spectrum analysis of all subjects indicated that stroke survivors had reduced MPF of the surface EMG in the leading limb compared with healthy controls (Figure 3). The reduction in MPF or MF on the paretic side has been report previously (16, 17). The decrease in MPF values of the paretic muscles could be due to the loss of muscle fibers and impairments in the motor unit following stroke. The firing rate has been demonstrated to be lower on the paretic side of stroke survivors compared both with the non-paretic side of stroke survivors and with the healthy controls during contraction (37, 38), which might also cause the reduced MPF values.

Alternations in the EMG spectrum have also been reported in children with cerebral palsy. Wakeling et al. and Gestel et al. found increased MPF value in such children compared with asymptomatic controls during gait and related it to the muscle dysfunction (26, 39). The assessment of the EMG spectrum could be used as an evaluation tool for functional muscle strength. No significant difference of MPF was found in the trailing limb between stroke survivors and healthy controls. This might be due to our instruction to the stroke survivors to use their affected side to cross the obstacle first and to use the unaffected limb as the trailing limb. This might have taken less effort to cross the obstacle and resulted in the lack of a significant between-group difference.

Obstacle crossing is a complex task, and the different heights place different demands on the subjects. Both groups showed a decreasing trend in MPF value with increasing obstacle height in the leading limb except for the MPF of the TA (Figure 3). Gabriel and Kamen also reported a significant decrease in the spectra with the force in the biceps brachii (40). With increased muscle activation level, there might be an increased degree of motor unit synchronization for the stroke survivors with the increasing obstacle height, which was likely to lead to reduction in the MPF values and fatigue (41, 42).

One interesting finding in our study is that there were significant correlations between calculated muscle activation levels of BF and TA in the leading limb and FMA scores measured by the therapists during swing phase at 10% leg length height (Figure 4) in stroke survivors. No significant correlation was found between muscle activation levels and FMA scores during 20 or 30% leg length height obstacle crossing in stroke survivors. Performance of stroke survivors was more disturbed during challenging task such as for obstacle crossing of higher height (43), and they couldn’t take good control of themselves which led to abnormal patterns and large variations within group when step across the obstacle. The activation level of the BF increased while that of the TA decreased with increasing obstacle height. Proximal control is more efficient than distal control in the lower limbs for stroke survivors to ensure safe obstacle crossing according to kinematic analysis (7). Our research showed through EMG analysis that stroke survivors with high FMA scores had greater BF activation, which controlled the hip joint and knee joint to elevate the toe. At the same time, the TA (which is related to ankle dorsiflexion) was abnormally activated to a larger degree in the face of fall risk among stroke survivors with lower FMA scores. Similar to Li et al., we found no significant correlation between the MPF values and clinical scales or between CI and clinical scales (16). Nevertheless, the significant correlation between the muscle activation levels and FMA scores means that it is reliable to use EMG signals to analyze the muscles of stroke survivors.

This study has several limitations. The recruited stroke survivors in this study had good function, so the results might not be useful for moderate or severe stroke survivors. We instructed the stroke survivors to use their hemiparetic side to take the first step over the obstacle and to use the unaffected side as the supporting limb, which is safer for them. We neglected situations using the other limb as the leading limb, which might lead to a minor difference in the results. Moreover, significant differences between stroke survivors and healthy controls should also be interpreted with caution considering the relatively small sample size. We plan to recruit more stroke survivors of different functional levels and to instruct them to use both limbs as the leading limb in future work to obtain a more comprehensive understanding of the neuromuscular changes.

In this study, stroke survivors were recruited to step across obstacles of three different heights and compared with healthy controls to investigate motor control mechanisms that could not be reflected during level walking. Although the stroke survivors could safely step across the obstacles, they demonstrated abnormal motor control patterns, such as greater overall muscle activation level and larger cocontraction of the agonist and antagonist muscles. These might result in muscle fatigue, which would lead to a high risk of tripping and higher energy cost. The reduction in the MPF values of the paretic side of stroke survivors could be related to impairments of the motor unit or other complex neuromuscular alterations. The decreasing trend of the MPF values when crossing higher heights might due to greater motor unit synchronization, which could also lead to fatigue. The significant correlations between muscle activation levels and clinical scales provided a reliable method of analyzing the muscle functions of stroke survivors. These findings could help therapists to understand the neuromuscular changes following stroke and work out specific methods for rehabilitation of the lower limb muscles.