Probabilistic real-time user posture tracking for personalized robot-assisted dressing
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.
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The complex deformation of the cloth and its occlusion on the body makes human-posture tracking difficult.
“The complex deformation of the cloth and its occlusion on the body makes human-posture tracking difficult [31,304].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [304]
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Set-Based State Estimation With Probabilistic Consistency Guarantee Under Epistemic Uncertainty
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Rehabilitation robotics: History, applications, and recent advances
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Real-time motion tracking of cognitive Baxter robot based on differential inverse kinematics
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Bi-Manual Manipulation of Multi-Component Garments towards Robot-Assisted Dressing
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Real‐Time Dance Posture Tracking Method Based on Lightweight Network
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Garment Diffusion Models for Robot-Assisted Dressing
Kotsovolis, S., & Demiris, Y. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3518104 · Cited by 5 · see citing works
Self-citationA Robot System for Assisting Humans in Wearing Long-Sleeved Shirt
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Electric Wheelchair-Humanoid Robot Collaboration for Clothing Assistance of the Elderly
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Self-Wearing Adaptive Garments via Soft Robotic Unfurling
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A Pedestrian Detection and Tracking Method for Robot Equipped with Laser Radar
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Kinematic Structure Estimation of Arbitrary Articulated Rigid Objects for Event Cameras
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A Study on Mobile Phone Interface Interaction Design for the Elderly Based on Digital Technology
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Robotic manipulation via the assisted 3D point cloud of an object in the bin-picking application
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Comprehensive Support for Dressing Bottoms in Hemiplegic Individuals Using a Branching Arm Robot
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2019 Index IEEE Transactions on Robotics Vol. 35
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Real-time Multi-person Pose Tracking Method Using Deep Reinforcement Learning
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Motion Generation for Mobile Manipulators to Assist in Putting on a Jacket While Walking
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Autonomous Areial Surveillance System For Flood Rescue Operations
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An Image Uncertainty Calibration Network for Robotic Clothes Grasping
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Evaluating Digital Twin Visualisations for Safety and Trust in Robot-Assisted Dressing
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Principles of artificial intelligence in law
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User Preferences for Enhanced Robot Adaptation in Assistive Tasks
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- Authors
- Zhang, F., Cully, A., & Demiris, Y.
- Affiliations
- Imperial College London (GB)
- Venue
- IEEE Transactions on Robotics
- Published
- 2019-08
- Volume
- 35
- Issue
- 4
- Pages
- 873-888
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W2937476238
- Cited by
- 70
- References
- 67
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 10 · 2023: 12 · 2022: 18 · 2021: 8 · 2020: 7 · 2019: 1
- FWCI
- 4.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Robotics (publishedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- Royal Academy of Engineering
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- Bayesian networks, Gaussian process latent variable model, visual occlusion, force estimation, Baxter robot, real-time tracking
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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