Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits

By Samuel K. Au, Max Berniker, Hugh M. Herr. Neural Networks, 2008

519 citations recorded by the source corpus.

Subfields: Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/280b0de1a1d8f3719b7369ef684df71d021f21e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. He (Helen) Huang
2. Levi J. Hargrove
3. Kevin B. Englehart
4. Zhi Dou
5. Fan Zhang
6. Daniel R. Rogers
7. Marcia Kilchenman O'Malley
8. Edoardo Battaglia

Citations in the corpus

• Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion (2011, background): “The desired impedance of powered knees also depends on the locomotion mode [4], [5], as the dynamics and kinematics of”
• Multiclass Real-Time Intent Recognition of a Powered Lower Limb Prosthesis (2009, unknown): “The authors use finite-state controllers in combination with torque, impedance, and position controllers for the control of a powered ankle prosthesis with a series elastic actuator in [19].”
• A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human-Robot Interaction (2018, background): “[32], to learn to distinguish between a few discrete operating states.”