In this case study, we tested whether facilitating active participation of the patient while receiving robot-aided gait therapy would help to mitigate gait impairments after hemispheric stroke.
Our preliminary evaluation indicated that four weeks of active robotic training led to substantial improvements in several clinical and functional parameters and these improvements persisted during the follow-up evaluation. Importantly, the improvements were much larger than the measurement errors [37
] and were also substantially larger than the mean improvements seen after conventional Lokomat assisted gait therapy [6
] and manual therapist-assisted treadmill training [6
]. The positive clinical outcomes observed also reinforce the findings of a recent study that evaluated kinematic changes after robot-aided gait therapy using a similar approach [28
]. Although we are limited by the fact that we have studied only a single subject, the results suggest that these outcomes were clinically meaningful (see Table
) and that patient cooperative robot-aided gait training with target-tracking is a feasible and potentially
effective approach to minimizing post-stroke gait impairments.
In addition to improvements that were specific to training (like reduced tracking error and variability), our results also showed improvements in abilities that were not specifically trained. First, our subject showed an improvement in single-leg balance on the paretic leg, going from 1
s before training to 15
s after training. This improvement was also sustained during the follow-up exam. The increase in single-leg balance time may be important from the viewpoint of reducing the risk of falls [40
]. An interesting feature is that this improvement occurred despite the fact that the Lokomat restricts lateral and axial motion of the pelvis – an important feature for training balance and stability [42
]. Second, there was transfer to overground walking as reflected by improvements in walking speed, the TUG test and the 6-minute walking distance. These improvements suggest that robotic training can produce improvements that are not simply context-specific, but are generalizable to activities of daily living.
We also used several novel biomechanical and neuromuscular metrics to complement the typical clinical measures, which allowed us to objectively quantify the mechanisms underlying the neuromotor deficits and monitor changes with training. At the biomechanical level, we found that training not only improved the propulsive forces generated by both the paretic and non-paretic leg muscles, but also improved the symmetry of the antero-posterior GRFs between the paretic and non-paretic legs (Figure
). This finding suggests that positive outcomes observed after training were partly mediated by the improvements in motor output of both the paretic and non-paretic leg muscles and not simply due to functional compensation [43
]. We also note that the changes in peak antero-posterior GRF of the paretic leg were much larger than the minimal clinically important changes that have been reported for this variable [45
At the neuromuscular level, we found changes both in muscle coordination and motor cortical excitability during treadmill walking. In terms of muscle coordination, we found that the muscle modes obtained after training were closer to healthy controls, suggesting that part of the gait improvements were due to improvements in the timing of muscle activity. This was particularly evident in the timing of activation of the GM and TA muscles. In addition, the motor cortical excitability of the VM, MH, and GM muscles reduced substantially after training. The reduction in motor cortical excitability is probably due to improvements in lower-extremity muscle strength after training as previous studies indicate that strength training decreases motor cortical excitability [46
]. While we do not have muscle-specific strength measurements to support this hypothesis, our subject reported improvements in strength and muscle control with training. Moreover, changes in propulsive forces and Fugl-Meyer scores suggest that our subject may have realized meaningful strength gains after training.
What are the critical components of the current training paradigm that may have facilitated the positive outcomes observed? As described earlier, the prevalent hypothesis for the suboptimal outcomes in several robotic interventions is that the guidance provided by the robot results in a lack of active participation during training [11
]. Indeed, studies on motor learning show that excessive guidance may impair learning, even though it may augment performance temporarily [49
]. Here, we incorporated a control algorithm that reduced the amount of passive guidance/support provided by the Lokomat. This ensured that the subject could not slack and instead had to actively walk inside the Lokomat. However, in addition to reducing robot guidance, we also added a skill-learning component by using a target-tracking task that forced the participant to exaggerate hip and knee flexion during the swing phase. We anticipated that this tracking task would not only result in increases in muscle activity, but would also challenge the subject to reorganize the muscular coordination in order to produce the new gait pattern consistently. Further, we also ensured that the visual feedback during this tracking task was perceptually simple by using the position of the ankle (in x-y coordinates), thereby making it easy for participants to identify and correct their errors (e.g., “I am not lifting my foot high enough”). This perceptual simplicity has been shown to be important for coordinating multiple degrees of freedom [51
]. This feedback was also faded with practice in order to prevent the subject from being dependent on the visual feedback [53
]. This type of task-oriented gait therapy, which involves both active participation and skill learning, may be critical in promoting better functional recovery in stroke individuals.
We would like to point out some of the limitations of this study. First, the results reported are from a single subject and it is possible that our subject may not be representative of the typical stroke population at large. He had only moderate levels of lower-extremity impairment, good upper-extremity function, and was highly motivated. As a result, he was able to cope well with the training regimen and perform the target-tracking task without much difficulty. However, stroke subjects with higher levels of impairment may not have adequate knee/hip flexor strength to perform this task. Although the guidance force exerted by the robot can be tuned specifically to match the recovery state and functional capacity of each subject, it remains to be seen whether these improvements will be observed in patients with higher impairment levels. Second, some of the benefits observed in our subject could have been due to natural biological recovery, although this seems unlikely given the evidence that most of neurological and functional recovery plateaus after 3–4
months following stroke [55
]. Furthermore, it is possible that part of these improvements observed could have been solely due to the benefits of treadmill walking and not due to the robot-aided gait therapy. However, it is to be noted that the changes in functional outcomes observed in our subject is greater than those observed in any of the stroke subjects who participated in a recent pilot study that evaluated the effectiveness of manual therapist assisted treadmill training in stroke survivors [39
]. Finally, there is also the question of whether it is the visual feedback or the cooperative control that was responsible for the improvements. In this regard, a study from Kim et al. [30
] suggests that the combination of visual feedback and cooperative control is better for motor learning than either one alone.
In this case study, we report results from a novel robotic gait training approach that aimed to facilitate active participation and mitigate post-stroke gait impairments. Four weeks of patient-cooperative robot-aided walking with a target-tracking task resulted in clinically meaningful improvements in several of the measured locomotor outcomes. These improvements persisted during a follow-up evaluation that was performed at 6-weeks after the completion of training. The promising positive outcomes of this case study suggest that combining patient-cooperative robot-aided walking with a target-tracking task is a feasible approach to improve post-stroke walking function. Further research is necessary to identify whether this approach would be feasible in patients with varying levels of impairment and also to verify whether similar results can be obtained from a larger cohort of stroke population.