User-Independent Intent Recognition on a Powered Transfemoral Prosthesis
Abstract
This research will develop intuitive and smart intent recognition systems for powered prostheses to predict user intent to optimally supply power to the gait cycle during locomotion tasks. Intelligent intent recognition systems are needed for these prostheses to be clinically deployable. The primary scope of this project first involves developing and preparing a powered prosthesis complete with control technologies for clinical testing with patients with transfemoral amputation. We will collect data during walking which includes various speeds, stairs and ramps. We will compare the clinical effectiveness of different intent recognition systems on lower limb amputees using a powered prosthesis. This research will result in clinically meaningful parameters including the success rate and speed of the amputees performing a circuit of locomotion activities including level walking, stairs and ramps. Biomechanics of movement using the controllers will be quantified and compared to passive prosthesis ambulation. Results to date include the development of a second iteration of a powered prosthesis that is more compact, lightweight, and easily adaptable to different users. Various machine learning analyses have been performed to estimate environmental variables from a user-independent perspective. Biomechanical analysis shows how the powered prosthesis can restore similar patterns of gait seen in able-bodied individuals compared to a passive devices. These are promising and strong outcome measures that will be further validated in further stage experiments as the project nears completion.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 2020
- Accession Number
- AD1094896
Entities
People
- Aaron Young
- Krishan Bhakta
Organizations
- Georgia Tech