Yannick Aoustin

Professeur des universités

Université de Nantes

France

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Yannick Aoustin is a Professeur des universités at Université de Nantes, France. His research areas include the real-time decomposition of intramuscular EMG signals, mechatronic design, and control systems for robotic applications. He has authored several articles focusing on experimental validations in robotics, gait analysis, and control performance improvements.

Articles (15)

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2025

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2025

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2025

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2024

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2023

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2023

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2022

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2022

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2021

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2021

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2020

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2020

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2020

Collaborators (1)

Andrey Polyakov

Université de Lille

FRANCE

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Yannick Aoustin

Research Interests

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Articles (15)

Real-Time Decomposition of Multi-Channel Intramuscular EMG Signals Recorded by Micro-Electrode Arrays in Humans

Validation of Cable-Driven Experimental Setup to Assess Movements Made with Elbow Joint Assistance

Design and Experimental Validation of a Controller for Bowden-Cable Actuators Subject to Friction Variation

Experimental Validation of the Essential Model for a Complete Walking Gait with the NAO Robot

A semi-implicit homogeneous discretized differentiator based on two projectors: experimental validation on a cable-driven parallel robot

A survey on the discrete-time differentiators in closed-loop control systems: Experiments on an electro-pneumatic system

Mechatronic design of dynamically decoupled manipulators based on the control performance improvement

Design and Optimization of a Planar Biped Leg Based on Direct Drive Linear Actuators

Feasible speeds for two optimal periodic walking gaits of a planar biped robot

Highly Accurate Real-Time Decomposition of Single Channel Intramuscular EMG

A new human-like walking for the humanoid robot Romeo

An Experimental Investigation of Discretized Homogeneous Differentiators: Pneumatic Actuator Case

Simulation of Motor Unit Action Potential Recordings From Intramuscular Multichannel Scanning Electrodes

On-Line Recursive Decomposition of Intramuscular EMG Signals Using GPU-Implemented Bayesian Filtering

Recursive Decomposition of Electromyographic Signals With a Varying Number of Active Sources: Bayesian Modeling and Filtering

Collaborators (1)

Andrey Polyakov

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