Project description
Personalised solutions for balance-assistive devices
Balance impairment, a widespread issue linked to neurological diseases and ageing, hampers rehabilitation methods and assistive devices. While robotic assistive devices such the GyBAR, a gyroscopic backpack, can improve patient mobility, extensive training is often a prerequisite for reaping the full benefits. With the support of Marie Skłodowska-Curie Actions, the BalancingACT project unveils a gyroscopic backpack with proven success in enhancing standing and walking balance for both healthy and stroke-affected populations. BalancingACT seeks to break barriers by exploring innovative methods to co-adapt users and balance assistance, offering personalised solutions that directly target balance outcomes. The project aims to customise assistance through human-in-the-loop optimisation. These breakthroughs not only promise enhancements for the GyBAR but will also have a broader impact on diverse balance-assistive devices.
Objective
Balance impairment affects a large proportion of the global population, as a symptom of many neurological diseases and a consequence of advanced age. Methods to improve balance through rehabilitation or assistive devices are effective but are limited by the availability of physiotherapists or by the strength and agility of the patient. While robotic assistive devices could augment mobility, extensive training is often necessary to receive the full benefits. BalancingACT will tackle this bottleneck by investigating methods to facilitate co-adaptation of user and balance assistance, providing personalized assistance directly targeting balance outcomes. The GyBAR, a gyroscopic backpack, has improved standing and walking balance for both healthy and stroke populations, but could benefit from targeted user training.
BalancingACT will address three main aspects of human-robot co-adaptation. I will first examine existing datasets from healthy and patient populations to determine which balance metrics explain differences between populations, providing a measure to gauge and optimize human-robot co-adaptation. I will then probe methods to improve motor learning for a healthy population in a challenging task, i.e. walking along a narrow beam. Exploration, driven by the individual or by the device, is a vital component of early learning. I will conduct two experiments, one to understand how self-guided exploration affects learning and one using human-in-the-loop optimization, a method to customize assistance by directly estimating the user’s response to a variety of candidate controllers, to determine the benefits of device-led exploration. This algorithm has previously elicited positive learning effects in exoskeletons and can also provide insight into the third aspect of co-adaptation: adapting the device to the user. In the long term, these results can be used to not only improve outcomes for the GyBAR but can also be generalized to other balance assistive devices.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- medical and health sciencesclinical medicinephysiotherapy
- medical and health sciencesbasic medicineneurologystroke
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Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
3015 GD Rotterdam
Netherlands