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A multimodal intention detection sensor suite for shared autonomy of upper-limb robotic prostheses
File | Description | Size | Format | |
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sensors-20-06097.pdf | Published version | 2.99 MB | Adobe PDF | View/Open |
Title: | A multimodal intention detection sensor suite for shared autonomy of upper-limb robotic prostheses |
Authors: | Gardner, M Mancero Castillo, C Wilson, S Farina, D Burdet, E Khoo, BC Atashzar, SF Vaidyanathan, R |
Item Type: | Journal Article |
Abstract: | Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human–machine interface (HMI). Grasp control remains one of the most challenging HMI tasks, demanding simultaneous, agile, and precise control of multiple degrees-of-freedom (DoFs) while following a specific timing pattern in the joint and human–robot task spaces. Most commercially available systems use either an indirect mode-switching configuration or a limited sequential control strategy, limiting activation to one DoF at a time. To address this challenge, we introduce a shared autonomy framework centred around a low-cost multi-modal sensor suite fusing: (a) mechanomyography (MMG) to estimate the intended muscle activation, (b) camera-based visual information for integrated autonomous object recognition, and (c) inertial measurement to enhance intention prediction based on the grasping trajectory. The complete system predicts user intent for grasp based on measured dynamical features during natural motions. A total of 84 motion features were extracted from the sensor suite, and tests were conducted on 10 able-bodied and 1 amputee participants for grasping common household objects with a robotic hand. Real-time grasp classification accuracy using visual and motion features obtained 100%, 82.5%, and 88.9% across all participants for detecting and executing grasping actions for a bottle, lid, and box, respectively. The proposed multimodal sensor suite is a novel approach for predicting different grasp strategies and automating task performance using a commercial upper-limb prosthetic device. The system also shows potential to improve the usability of modern neurorobotic systems due to the intuitive control design. |
Issue Date: | 27-Oct-2020 |
Date of Acceptance: | 23-Oct-2020 |
URI: | http://hdl.handle.net/10044/1/85026 |
DOI: | 10.3390/s20216097 |
ISSN: | 1424-8220 |
Publisher: | MDPI AG |
Journal / Book Title: | Sensors |
Volume: | 20 |
Issue: | 21 |
Copyright Statement: | ©2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering shared autonomy prosthetic technology mechanomyography HAND RECOGNITION STATE mechanomyography prosthetic technology shared autonomy Analytical Chemistry 0301 Analytical Chemistry 0805 Distributed Computing 0906 Electrical and Electronic Engineering 0502 Environmental Science and Management 0602 Ecology |
Publication Status: | Published |
Article Number: | ARTN 6097 |
Appears in Collections: | Mechanical Engineering Bioengineering Faculty of Engineering |
This item is licensed under a Creative Commons License