This study investigated the capability of individuals with chronic stroke to attain faster walking speeds than their self-selected overground maximum walking speed, when using a treadmill or when pushed overground using a robotic device (“push mode”). The results showed that these individuals were capable of walking significantly faster in treadmill and “push mode” walking compared with their overground self-selected maximum walking speed. Moreover, in many cases they were able to match the top speed limit of the robotic device (2.0 m/sec), indicating that they may have been capable of moving at an even higher velocity. In terms of spatiotemporal parameters, as participants walked under progressively faster conditions, they were able to increase both average step length and average cadence until a maximum average step length was reached and then significant increases in cadence were needed to match speeds. We discuss each of these key results below.
Both conditions, treadmill and “push mode” walking, permitted participants to attain significantly faster walking speeds as compared to their self-selected maximum walking speed overground. This result reaffirms that individuals post-stroke are able to, at least, match their overground walking speeds when walking on a treadmill. In the case of this experiment, when walking in the robotic device; the results are consistent with those that were demonstrated by studies aimed to determine differences between overground and treadmill walking at matched speeds [16, 33–39]. It is important to note that in those studies, as in this experiment, walking speeds on the treadmill were determined by the researcher. However, studies where post-stroke individuals self-select their walking speeds on the treadmill revealed that individuals choose speeds that are significantly slower than those self-selected overground [9, 40]. Our current study showed that individuals were not only able to match their overground walking speeds, but also had the capability to go beyond those self-selected walking speeds overground.
The ability of individuals to attain faster walking speeds during treadmill and “push mode” walking might be due to common features between the two devices. For instance, both devices provided rhythmically constant external cues at fixed speeds allowing for a more stable and continuous walking pattern than during a self-selected walking speeds. On the treadmill, several experiments in people post-stroke have demonstrated a more symmetrical kinematic gait pattern and decrease step-to-step variability compared to overground walking [33, 34]. These more symmetrical walking patterns might be associated with the capability of individuals to reach faster walking speeds. In the robotic device, the only evidence available indicates that no kinematic alterations are observed with functional activities [41, 42]. In this way, the device’s predetermined speed requires individuals to choose only those appropriate walking strategies that serve to avoid falling. Our results showed that participants had longer step lengths and slower cadences during overground walking compared to the other two conditions (treadmill and “push mode”) of matched walking speeds. These results have been extensively demonstrated for the treadmill versus overground walking [9, 12, 33, 35].
In addition, participants wore a safety harness during both walking conditions. We propose that, by wearing a safety harness, participants experienced decreased levels of fall-related anxiety (decrease fear or concern of falling) which allowed individuals to appraise their own walking capacities at more challenging and faster speeds. In older individuals, studies suggest that anxiety related to falls is eliminated or reduced by wearing a harness . Also, spatial and temporal parameters are affected not only by physiological constraints, but also by psychological demands [44–46]. Stability-challenging tasks (i.e. walking on elevated or narrow walkways) while in conditions that alter anxiety (i.e. dual tasks or dimmed light) induced decrease walking speeds, shorter steps, decrease cadence, and increase time in double limb support. These studies support a multidimensional model of fear of falling, where fear and anxiety result from an individual’s appraisal of his or her own abilities to maintain balance in combination with other contributors such as falls history . Even though this model has been based on data and experiments performed in older individuals it could be extended to other populations, such as stroke survivors. In the context of this experiment we propose that, by wearing a safety harness, participants experienced decreased levels of fall-related anxiety which allowed individuals to appraise their own walking capacities at more challenging and faster speeds. However, it is important to note that four participants were unwilling to increase their walking speeds in order to avoid experiencing a loss of balance. These participants explicitly stated that they were fearful, apprehensive, or anxious about walking at faster speeds, despite repeated demonstrations that the device was capable of catching them and preventing harm during the experiment. In the case of this group of participants, we think that fear of falling was not reduced, regardless of the use of a harness. In the multidimensional model of fear of falling mentioned above, fear of falling results from an individual’s appraisal of his or her own abilities to maintain balance . This appraisal has three components: physiological, behavioral, and cognitive; all of which could be affected after a stroke. For example, the physiological component is appraised when changes to locomotor control after stroke can negatively impact balance and gait performance. Also, previous falls could lead to the development of the “post-fall” syndrome (i.e. avoidance of activity due to excessive fear of falling) or negatively affecting the cognitive component [47–49]. In our current experiment, none of these components were empirically tested, but our experimental set-up was designed to test the limits of the neurophysiological system. In other words, we asked individuals to increase their walking speed until a loss of balance or a fall was experienced which we interpreted as the fastest speed that the neuromechanical system could accommodate. Thus, we expected individuals to experience some level of fear or concern of falling due to the physiological and cognitive loads imposed to individuals when exposed to this environment in a single session. In summary, the group of participants that were unwilling to attain faster walking speeds represented a subgroup of the post-stroke population in which fear of falling causes an avoidance of participation, whereas in the group of participants that were able to attain faster walking speed, fear of falling was reduced by wearing a harness and demonstration of the ability of the systems to catch people and avoid harm. Also, we propose that the individuals who achieved faster walking speeds were being asked to react to predetermined, stable, and continuous speeds while in provided with a safe environment, in contrast to situations where they are asked to self-select both walking speed and pattern without necessarily providing increased safety assurances.
Participants reached significantly faster speeds during treadmill walking than overground walking, but those speeds were not as fast as those attained in the “push mode” of the robotic device. This result might be influenced by the different biomechanical constraints imposed by each walking environment. Previous investigations that compared treadmill and overground walking have found that, regardless of age, healthy individuals showed minimal changes in spatiotemporal and kinematic gait parameters, but variables such as patterns of muscle activation differ slightly [35–37, 50]. In stroke survivors, the evidence suggests even more differences in kinematic and kinetic parameters in treadmill versus overground walking [33, 34, 38, 39]. These studies have shown that during treadmill walking individuals present immediate changes in joint angles, muscle activity, and spatiotemporal parameters that result in more consistent and symmetrical walking patterns. These changes have been attributed mostly to the constraints imposed by the treadmill. For example, when the non-paretic leg is in stance phase, and the treadmill belt moves backwards, the paretic leg is forced to perform a timely swing to maintain the center of mass inside the margins of stability to avoid falling . In this way, not only mechanical changes are observed but also alterations in muscle activity that result in more appropriate walking behaviors . Also, the changes observed in the spatiotemporal parameters seem to be influenced by speed, as demonstrated by several studies [15, 16, 33]. Our results agree with previous investigations that the mechanical constraints of the treadmill induce mechanical and neuromuscular changes that allow individuals to acquire walking patterns that adjust to faster speeds.
Participants reached their greatest walking speeds during “push mode” and in many cases were able to match the top speed limit of the robotic device (2.0 m/sec), which indicates that they may have been able to walk at even greater speeds. We propose that these results are due to a combination of factors, such as a decreased fear of falling, and biomechanical constraints imposed by the robotic device, among other factors. The first two factors mentioned (fear of falling and speed control) are common to treadmill walking and explained above. On the other hand, the constraints imposed by the robotic device are different from those during treadmill walking. During “push mode” walking, the robotic device moves along with the individual providing a safe overground walking environment, but also allows for control of walking speed. Thus, when the device was set to move at a specified speed, individuals were required to advance their lower extremities, under their own volition and in response to a forward push provided by the device, in order to keep up with the device’s velocity and avoid falling. In other words, the robotic device “pushed” individuals to walk at specified speeds by controlling the velocity and position of the individual’s center of mass. Contrary to the treadmill belt, where the margins of stability are controlled by moving the feet, this pelvic mechanism controls the velocity of the individual’s center of mass. Additionally, the robotic device’s design allowed participants to walk overground which provided congruent sensorimotor information regarding body progression and displacement; contrary to treadmill walking where individuals experience conflicting visual, proprioceptive and vestibular information. Also, the robotic device did not provide assistance with stability since it was developed to allow full degrees of freedom about the hip, pelvis, and trunk so as to challenge individuals to maintain balance . In detail, this robotic device consists of a torso and a pelvis harness attached to a mobile robotic base. The pelvic harness has six degrees of freedom that allow individuals to move in all directions while walking. Both harnesses have a transparent Safety Zone in which the individual can move without any assistance or hindrance from the device. At the boundary of this range the trunk support implements a compliant constraint which catches the patient when he or she loses balance . The combination of these characteristics result in an environment that is safe for attaining fast walking speeds but also similar enough to normal overground walking that the individual is not required to use a different gait strategy than what he or she normally uses for ambulation.
Also, in the mini-experiment with non-impaired subjects reported here, during the “push mode” walking individuals generated less force than at equivalent speeds while in the robotic device without being “pushed”. This reflects decreased force generation to develop forward velocity, allowing subjects to achieve a given velocity with lesser effort. This important finding relates to the determinants of walking speed in post-stroke individuals previously reported. Researchers found a positive correlation between muscle strength and walking speed, both at comfortable and maximum walking speeds [3, 4, 51–62]. In terms of comfortable walking speed, most studies agreed that the strongest predictor, of walking speed after stroke, was hip and ankle power generation on the paretic leg during the pre- and initial swing phases of the gait cycle [4, 51, 55, 56, 59]. In terms of maximum walking speed, the strongest predictors included decreased strength in the hip flexors [4, 51, 55, 57] and ankle plantar flexors [3, 4, 51, 54, 55, 57, 59, 61]. Moreover, in the post-stroke population individuals who present a lower functional level, both ankle and hip power generation did not increase with increased voluntary walking speed as they do in controls [4, 56]. If the decreased force generation observed in healthy individuals, in this experiment, also occurred in the post-stroke individuals that we studied then we can infer that the robotic device’s “push mode” provided assistance while walking. In other words, the robotic device provided the necessary force to achieve those speeds; thus compensating for individuals’ impairments. This indicated that individuals with impaired power generation would be able to achieve greater walking speeds in the “push mode”. The specific mechanisms by which this assistance occurred in this experiment are unclear, but will be addressed in future studies.
During both the treadmill and “push-mode” conditions, participants increased their average step length and average cadence until a plateau was reached by the average step length and subsequently significant increases in cadence were observed. These results are consistent with previous studies that showed changes in spatiotemporal parameters with increments in speed [9, 16, 33], implying that post-stroke individuals have the capacity to modify their current walking pattern in order to increase walking speed. Our results suggest that once individuals stopped increasing step length, increments in cadence were the only available modification to the current walking pattern. However, the changes in spatiotemporal parameters observed differ between researchers, as well as the definition of “fast speeds”. For example, during treadmill walking Bayat el al. found increased step length but not cadence , Tyrell et al.  found longer step lengths, and Brouwer et al.  a combination of different kinetic and kinematic factors to attain faster walking speeds [16, 33]. Our results showed that participants had longer step lengths and slower cadences during overground walking compared to the other two conditions (treadmill and “push mode”) at matched walking speeds; similar to previously published data [9, 33–35]. However, our results also showed that when these individuals were prompted to walk at faster speeds than their maximum overground walking, their step lengths increased to match those of maximum overground walking and subsequently, only increased cadence was observed. These results suggest that once the limit in step length is reached, increments in cadence are the only available modification to the current walking pattern. Therefore, we propose that at fast walking speeds step length became a limiting factor to achieve faster speeds during treadmill and “push mode” walking.
In this experiment, participants were able to walk 3 times faster in the “push mode” and 2.6 times faster during treadmill walking as compared to their overground self-selected comfortable walking pace. Other researchers have done similar comparisons, Tyrell et al.  reported walking speeds that were 1.6 times faster than the participants’ self-selected walking pace overground, and Brouwer et al.  and Bayat et al.  showed speeds 1.2 times faster. We understand that these changes should not be used as direct comparisons because of the inherent differences between each study. Yet, we use this as an indicator of a possible factor that yields the results of our study.
The interpretation of results for this study may be limited by several factors. First, the spatiotemporal parameters measured in this experiment, average step length and cadence, were calculated from the recorded time and steps that participants generated to complete the 5-meter distance. Therefore, the data in this experiment do not provide for individual limb differences or allow for further comparison between stance and swing phases for each leg. However, the average step lengths reported in this experiment are similar to those reported by previous studies [16, 33]. Future research that expands and compares the current results with more specific spatiotemporal measurements is needed.
Second, some participants stopped the experiment due to an increased concern or fear of falling. In order to reduce the incidence of this behavior, we provided practice with the safety harness and further verbal encouragement during all trials. For example, once participants expressed their concern they were encouraged to sit on the harness, on both the treadmill and the robotic device, to assure them that they were in a safe environment and reduce their anxiety. In the end, every individual decided if they wanted to continue with the testing procedures. Therefore, we propose that these participants did not reach their maximum walking speeds due to psychological constraints and not because of physical or physiological limitations. The result of psychological stressors on behaviors such as walking is recognized but not widely studied. Thus, it is important that this subgroup is studied and acknowledged during experiments that test individuals at maximum walking speed. We suggest that, in order to study fast walking speeds in this subgroup of post-stroke individuals, attempts at repeated exposure to simplified fast walking tasks where the cognitive load is reduced may result in greater acceptability to move at faster speeds.
Third, equipment constraints possibly limited the ability of participants to reach faster walking speeds. During treadmill walking at fast speeds the width of the treadmill belt was a limiting factor. Participants confined their base of support (step width) within the width of the treadmill in order to avoid tripping and possibly losing balance. For those subjects whose step width seemed to increase with faster walking speeds, this task was troublesome. On the other hand, during overground fast walking speeds in the “push mode”, the robotic device did not maintain a straight line. Consequently, this resulted in longer distances walked in the “push mode” than on the treadmill. These longer walking distances could be due to the individuals’ asymmetry in the walking pattern or asymmetry in force production, among other factors. Future research, evaluating and comparing these two walking environments are needed, as well as careful and detailed studies of the kinetic and kinematic changes at maximal walking speeds.