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Probabilistic real-time user posture tracking for personalized robot-assisted dressing

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Title: Probabilistic real-time user posture tracking for personalized robot-assisted dressing
Authors: Zhang, F
Cully, A
Demiris, Y
Item Type: Journal Article
Abstract: Robotic solutions to dressing assistance have the potential to provide tremendous support for elderly and disabled people. However, unexpected user movements may lead to dressing failures or even pose a risk to the user. Tracking such user movements with vision sensors is challenging due to severe visual occlusions created by the robot and clothes. In this paper, we propose a probabilistic tracking method using Bayesian networks in latent spaces, which fuses robot end-effector positions and force information to enable cameraless and real-time estimation of the user postures during dressing. The latent spaces are created before dressing by modeling the user movements with a Gaussian process latent variable model, taking the user’s movement limitations into account. We introduce a robot-assisted dressing system that combines our tracking method with hierarchical multitask control to minimize the force between the user and the robot. The experimental results demonstrate the robustness and accuracy of our tracking method. The proposed method enables the Baxter robot to provide personalized dressing assistance in putting on a sleeveless jacket for users with (simulated) upper-body impairments.
Issue Date: 1-Aug-2019
Date of Acceptance: 3-Mar-2019
URI: http://hdl.handle.net/10044/1/69196
DOI: 10.1109/tro.2019.2904461
ISSN: 1552-3098
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 873
End Page: 888
Journal / Book Title: IEEE Transactions on Robotics
Volume: 35
Issue: 4
Copyright Statement: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Royal Academy Of Engineering
Funder's Grant Number: CiET1718\46
Keywords: Science & Technology
Personalized dressing assistance
probabilistic real-time tracking
user modeling in latent spaces
Industrial Engineering & Automation
0801 Artificial Intelligence and Image Processing
0906 Electrical and Electronic Engineering
0913 Mechanical Engineering
Publication Status: Published
Online Publication Date: 2019-04-11
Appears in Collections:Computing
Electrical and Electronic Engineering
Faculty of Engineering