M
Marcia K. O'Malley
Researcher at Rice University
Publications - 252
Citations - 5765
Marcia K. O'Malley is an academic researcher from Rice University. The author has contributed to research in topics: Haptic technology & Exoskeleton. The author has an hindex of 36, co-authored 238 publications receiving 4662 citations. Previous affiliations of Marcia K. O'Malley include University of Texas Health Science Center at Houston & TIRR Memorial Hermann.
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Journal ArticleDOI
Design of a haptic arm exoskeleton for training and rehabilitation
TL;DR: In this paper, the authors present a detailed review of the requirements and constraints that are involved in the design of a high-quality haptic arm exoskeleton for training and rehabilitation in virtual environments.
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Design, Control and Performance of RiceWrist: A Force Feedback Wrist Exoskeleton for Rehabilitation and Training
TL;DR: The RiceWrist is intended to provide kinesthetic feedback during the training of motor skills or rehabilitation of reaching movements and exhibits low friction, zero-backlash and high manipulability, which are kinematic properties that characterize a high-quality impedance display device.
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A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human-Robot Interaction
TL;DR: This review provides a unifying view of human and robot sharing task execution in scenarios where collaboration and cooperation between the two entities are necessary, and where the physical coupling ofhuman and robot is a vital aspect.
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Shared Control in Haptic Systems for Performance Enhancement and Training
TL;DR: In this paper, a shared-control interaction paradigm for haptic interface systems is presented, where the haptic device contributes to execution of a dynamic target-hitting task via force commands from an automatic controller.
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Minimal Assist-as-Needed Controller for Upper Limb Robotic Rehabilitation
TL;DR: A minimal assist-as-needed (mAAN) controller for upper limb rehabilitation robots that employs sensorless force estimation to dynamically determine subject inputs without any underlying assumptions as to the nature of subject capabilities and computes a corresponding assistance torque with adjustable ultimate bounds on position error.