A master thesis from Aalborg University
Exploring Shared Control to Improve Self-Sufficiency of Tetraplegics: Assistive Robotics using Multimodal Intent Prediction
Author(s)
Term
4. term
Publication year
2019
Submitted on
2019-06-03
Pages
121 pages
Abstract
Currently, tetraplegics have limited opportunities to perform activities of daily living (ADL) independently from caregivers. This project explores the possibility of introducing a shared control system based on computer vision and multimodal intent prediction. Following a review of previous work, we design a solution to improve simple interactions necessary for ADL. This system utilizes galvanic skin response and a novel intent prediction method based on previous user input. An evaluation with 24 able-bodied people was conducted to gather both subjective and objective data about the interactions and performance. Evaluation shows that aggressive arbitration can be a hindrance in certain measurements, but a tradeoff exists which requires more work and a longer, more comprehensive evaluation to define.
Currently, tetraplegics have limited opportunities to perform activities of daily living (ADL) independently from caregivers. This project explores the possibility of introducing a shared control system based on computer vision and multimodal intent prediction. Following a review of previous work, we design a solution to improve simple interactions necessary for ADL. This system utilizes galvanic skin response and a novel intent prediction method based on previous user input. An evaluation with 24 able-bodied people was conducted to gather both subjective and objective data about the interactions and performance. Evaluation shows that aggressive arbitration can be a hindrance in certain measurements, but a tradeoff exists which requires more work and a longer, more comprehensive evaluation to define.
Keywords
Documents
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