Abstract
Current powered exoskeleton (exo) control algorithms for locomotion assistance and rehabilitation are based on assistive, resistive and error augmentation paradigms. Within the assistive controller’s family, assist-as-needed consists in applying a corrective force proportional to the error (actual limb position versus reference pattern). Our final goal is to implement a fully adaptable control mechanism to allow a full lower limb exo to dynamically adapt the gait pattern to each patient. We propose to use a modified version of tacit learning algorithm in combination with a variable stiffness actuator to explore the improvement of the adaptability in comparison to stiff actuators. The preliminary results show that using this concept on a compliant actuator it is possible to modulate a fixed trajectory to adapt to the position limits that are induced by user’s movement capabilities.
Original language | English (US) |
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Title of host publication | Biosystems and Biorobotics |
Publisher | Springer International Publishing |
Pages | 267-271 |
Number of pages | 5 |
DOIs | |
State | Published - 2017 |
Publication series
Name | Biosystems and Biorobotics |
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Volume | 16 |
ISSN (Print) | 2195-3562 |
ISSN (Electronic) | 2195-3570 |
Funding
Acknowledgments This study has been funded by grant from the European Commission, within the Seventh Framework Programme (IFP7-ICT-2013-10-611695: BioMot - Smart Wearable Robots with Bioinspired Sensory-Motor Skills).
ASJC Scopus subject areas
- Biomedical Engineering
- Mechanical Engineering
- Artificial Intelligence