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Locomotion requires careful coordination across the various joints and muscles of the body, which can be disrupted after neuromotor injuries such as stroke. People poststroke often have weakness in their paretic, or more impaired, ankle plantarflexors and a corresponding reliance on the hip joint to generate sufficient forward propulsion. The field of robotic rehabilitation has developed wearable systems that provide joint- and task-specific training for survivors of stroke, and in turn, increase use of the ankle muscles. However, capturing ankle use at the plantarflexor level remains a challenge with conventional tools given the unknown relative contributions of the dorsiflexor muscles. Moreover, variability across individuals complicates the interpretation of user response to these robotic interventions. In this work, we used standard biomechanical analysis as well as shear wave tensiometry in five people post-stroke to gain insight into user-specific ankle and hip adaptations in response to three levels of targeted plantarflexion exosuit resistance. We show that at a group and individual-level, evidence suggests a shift in biomechanical strategy from relying on the hip to using the ankle to modulate propulsion, with a subset of participants completely shifting to the ankle by the end of training. This work represents a step towards exploring more individualized methods for characterizing user response during adaptation to wearable robotic training interventions.
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Biomechanical and user comfort data when walking with an ankle exoskeleton
Biomechanical and user comfort data for the journal paper Using Biomechanical Signals and Gaussian Process Regression to Model Ankle Exoskeleton User Comfort. The data includes ground reaction forces, kinematic data (both marker positions and joint angles and velocity), kinetic data (joint moments and powers), metabolic cost, and comfort data for 13 subjects walking with a bilateral pair of ankle exoskeletons.
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- Award ID(s):
- 1930430
- PAR ID:
- 10636323
- Publisher / Repository:
- Penn State Data Commons
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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