Improving Ankle Muscle Recruitment via Plantar Pressure Biofeedback during Robot Resisted Gait Training in Cerebral Palsy

Predicting Steady-State Metabolic Power in Cerebral Palsy, Stroke, and the Elderly During Walking With and Without Assistive Devices

PURPOSE

Individuals with walking impairment, such as those with cerebral palsy, often face challenges in leading physically active lives due to the high energy cost of movement. Assistive devices like powered exoskeletons aim to alleviate this burden and improve mobility. Traditionally, optimizing the effectiveness of such devices has relied on time-consuming laboratory-based measurements of energy expenditure, which may not be feasible for some patient populations. To address this, our study aimed to enhance the state-of-the-art predictive model for estimating steady-state metabolic rate from 2-min walking trials to include individuals with and without walking disabilities and for a variety of terrains and wearable device conditions.

METHODS

Using over 200 walking trials collected from eight prior exoskeleton-related studies, we trained a simple linear machine learning model to predict metabolic power at steady state based on condition-specific factors, such as whether the trial was conducted on a treadmill (level or incline) or outdoors, as well as demographic information, such as the participant's weight or presence of walking impairment, and 2 minutes of metabolic data.

RESULTS

We demonstrated the ability to predict steady-state metabolic rate to within an accuracy of 4.71 ± 2.7% on average across all walking conditions and patient populations, including with assistive devices and on different terrains.

CONCLUSION

This work seeks to unlock the use of in-the-loop optimization of wearable assistive devices in individuals with limited walking capacity. A freely available MATLAB application allows other researchers to easily apply our model.

Journey to 1 Million Steps: A Retrospective Case Series Analyzing the Implementation of Robotic-Assisted Gait Training Into an Outpatient Pediatric Clinic

PURPOSE

To describe the implementation of an exoskeleton program in a rehabilitation setting using a Design Thinking framework.

METHODS

This is a retrospective case series of 3 randomly selected children who participated in skilled physical therapy using a pediatric exoskeleton that occurred on our journey to walking 1 000 000 steps in the exoskeleton devices. Participants ranged in age from 3 to 5 years, and all had neurologic disorders.

RESULTS

All participants improved toward achieving their therapy goals, tolerated the exoskeleton well, and had an increased number of steps taken over time.

CONCLUSION

The implementation of new technology into pediatric care and an established outpatient therapy clinic is described. The Design Thinking process applies to health care professionals and improves clinical care. Exoskeletons are effective tools for use in pediatric physical therapy.

Predicting Neuromuscular Engagement to Improve Gait Training with a Robotic Ankle Exoskeleton

The clinical efficacy of robotic rehabilitation interventions hinges on appropriate neuromuscular recruitment from the patient. The first purpose of this study was to evaluate the use of supervised machine learning techniques to predict neuromuscular recruitment of the ankle plantar flexors during walking with ankle exoskeleton resistance in individuals with cerebral palsy (CP). The second goal of this study was to utilize the predictive models of plantar flexor recruitment in the design of a personalized biofeedback framework intended to improve (i.e., increase) user engagement when walking with resistance. First, we developed and trained multilayer perceptrons (MLPs), a type of artificial neural network (ANN), utilizing features extracted exclusively from the exoskeleton's onboard sensors, and demonstrated 85-87% accuracy, on average, in predicting muscle recruitment from electromyography measurements. Next, our participants completed a gait training session while receiving audio-visual biofeedback of their personalized real-time planar flexor recruitment predictions from the online MLP. We found that adding biofeedback to resistance elevated plantar flexor recruitment by 24 16% compared to resistance alone. This study highlights the potential for online machine learning frameworks to improve the effectiveness and delivery of robotic rehabilitation systems in clinical populations.

Electromyographic biofeedback-driven gaming to alter calf muscle activation during gait in children with spastic cerebral palsy

BACKGROUND

Children with cerebral palsy often show deviating calf muscle activation patterns during gait, with excess activation during early stance and insufficient activation during push-off.

RESEARCH QUESTION

Can children with cerebral palsy improve their calf muscle activation patterns during gait using one session of biofeedback-driven gaming?

METHODS

Eighteen children (6-17 y) with spastic cerebral palsy received implicit game-based biofeedback on electromyographic activity of the calf muscle (soleus or gastrocnemius medialis) while walking on a treadmill during one session. Biofeedback alternately aimed to reduce early stance activity, increase push-off activity, and both combined. Early stance and push-off activity and the double-bump-index (early stance divided by push-off activity) were determined during baseline and walking with feedback. Changes were assessed at group level using repeated measures ANOVA with simple contrast or Friedman test with post-hoc Wilcoxon signed rank test, as well as individually using independent t-tests or Wilcoxon rank sum tests. Perceived competence and interest-enjoyment were assessed through a questionnaire.

RESULTS

Children successfully decreased their electromyographic activity during early stance feedback trials (relative decrease of 6.8 ± 12.2 %, P = 0.025), with a trend during the combined feedback trials (6.5 ± 13.9 %, P = 0.055), and increased their electromyographic activity during push-off feedback trials (8.1 ± 15.8 %, P = 0.038). Individual improvements were seen in twelve of eighteen participants. All children experienced high levels of interest-enjoyment (8.4/10) and perceived competence (8.1/10).

SIGNIFICANCE

This exploratory study suggests that children with cerebral palsy can achieve small within-session improvements of their calf muscle activation pattern when provided with implicit biofeedback-driven gaming in an enjoyable manner. Follow-up gait training studies can incorporate this method to assess retention and long-term functional benefits of electromyographic biofeedback-driven gaming.