In this section
@article{Zhang:2019:10.1109/tro.2019.2904461,
author = {Zhang, F and Cully, A and Demiris, Y},
doi = {10.1109/tro.2019.2904461},
journal = {IEEE Transactions on Robotics},
pages = {873--888},
title = {Probabilistic real-time user posture tracking for personalized robot-assisted dressing},
url = {http://dx.doi.org/10.1109/tro.2019.2904461},
volume = {35},
year = {2019}
}
TY - JOUR
AB - 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.
AU - Zhang,F
AU - Cully,A
AU - Demiris,Y
DO - 10.1109/tro.2019.2904461
EP - 888
PY - 2019///
SN - 1552-3098
SP - 873
TI - Probabilistic real-time user posture tracking for personalized robot-assisted dressing
T2 - IEEE Transactions on Robotics
UR - http://dx.doi.org/10.1109/tro.2019.2904461
UR - https://ieeexplore.ieee.org/document/8685136
UR - http://hdl.handle.net/10044/1/69196
VL - 35
ER -
