Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback

Abstract

Background Add-on robot-mediated therapy has proven to be more effective than conventional therapy alone in post-stroke gait rehabilitation. Such robot-mediated interventions routinely use also visual biofeedback tools. A better understanding of biofeedback content effects when used for robotic locomotor training may improve the rehabilitation process and outcomes. Methods This randomized cross-over pilot trial aimed to address the possible impact of different biofeedback contents on patients’ performance and experience during Lokomat training, by comparing a novel biofeedback based on online biological electromyographic information (EMGb) versus the commercial joint torque biofeedback (Rb) in sub-acute non ambulatory patients. 12 patients were randomized into two treatment groups, A and B, based on two different biofeedback training. For both groups, study protocol consisted of 12 Lokomat sessions, 6 for each biofeedback condition, 40 min each, 3 sessions per week of frequency. All patients performed Lokomat trainings as an add-on therapy to the conventional one that was the same for both groups and consisted of 40 min per day, 5 days per week. The primary outcome was the Modified Ashworth Spasticity Scale, and secondary outcomes included clinical, neurological, mechanical, and personal experience variables collected before and after each biofeedback training. Results Lokomat training significantly improved gait/daily living activity independence and trunk control, nevertheless, different effects due to biofeedback content were remarked. EMGb was more effective to reduce spasticity and improve muscle force at the ankle, knee and hip joints. Robot data suggest that Rb induces more adaptation to robotic movements than EMGb. Furthermore, Rb was perceived less demanding than EMGb, even though patient motivation was higher for EMGb. Robot was perceived to be effective, easy to use, reliable and safe: acceptability was rated as very high by all patients. Conclusions Specific effects can be related to biofeedback content: when muscular-based information is used, a more direct effect on lower limb spasticity and muscle activity is evidenced. In a similar manner, when biofeedback treatment is based on joint torque data, a higher patient compliance effect in terms of force exerted is achieved. Subjects who underwent EMGb seemed to be more motivated than those treated with Rb.

Haptic Adaptive Feedback to Promote Motor Learning With a Robotic Ankle Exoskeleton Integrated With a Video Game

“…Moreover, engagement with the training and entertainment are very important psychological aspects of games (Patton and Mussa-Ivaldi, 2004). In fact, visual feedback has been shown to improve robotic guidance therapy scenarios (Liu et al, 2006;Tamburella et al, 2019) and video games seem to be effective to improve motor function and health after stroke (Swanson and Whittinghill, 2015). Thus, different combinations of robotic guidance and video games have been proposed.…”

Wearable Biofeedback Improves Human-Robot Compliance during Ankle-Foot Exoskeleton-Assisted Gait Training: A Pre-Post Controlled Study in Healthy Participants

“…Some studies are reporting the promising use of biofeedback during post-stroke gait rehabilitation driven by exoskeletons. Most of them include gait training driven by hip and/or knee exoskeletons (EMG feedback in [ 9 ] and human-robot interaction torque feedback in [ 10 ]). EMG feedback is provided through a non-wearable display of colored stripes (one stripe per muscle) portioned into 16 stages within the gait cycle [ 9 ].…”

“…Most of them include gait training driven by hip and/or knee exoskeletons (EMG feedback in [ 9 ] and human-robot interaction torque feedback in [ 10 ]). EMG feedback is provided through a non-wearable display of colored stripes (one stripe per muscle) portioned into 16 stages within the gait cycle [ 9 ]. The stripes’ color represents over-activation, under-activation, or optimal muscle contraction according to a reference dataset [ 9 ].…”

“…EMG feedback is provided through a non-wearable display of colored stripes (one stripe per muscle) portioned into 16 stages within the gait cycle [ 9 ]. The stripes’ color represents over-activation, under-activation, or optimal muscle contraction according to a reference dataset [ 9 ]. Muscle contraction is measured through electromyographic sensors that require a time-consuming preparation.…”

“…Human-robot interaction torque is measured by force sensors embedded in the exoskeleton’s structure and it does not require any preparation. Moreover, human-robot interaction torque feedback was showed to be more effective to improve patient-exoskeleton compliance than EMG feedback [ 9 ].…”