Emanuele Gruppioni — accepted AI-conference papers

2024

Long-Term Upper-Limb Prosthesis Myocontrol via High-Density sEMG and Incremental Learning

Dario Di Domenico, Nicoló Boccardo, Andrea Marinelli, Michele Canepa, Emanuele Gruppioni, Matteo Laffranchi +1

RA-L 2024

Noninvasive human-machine interfaces such as surface electromyography (sEMG) have long been employed for controlling robotic prostheses. However, classical controllers are limited to few degrees of freedom (DoF). More recently, machine learning methods have been proposed to learn personalized contro

Cited by 13 Source ↗code ↗Scholar ↗

2021

Hannes Prosthesis Control Based on Regression Machine Learning Algorithms

Dario Di Domenico, Andrea Marinelli, Nicoló Boccardo, Marianna Semprini, Lorenzo Lombardi, Michele Canepa +6

IROS 2021

The quality of life for upper limb amputees can be greatly improved by the adoption of poly-articulated myoelectric prostheses. Typically, in these applications, a pattern recognition algorithm is used to control the system by converting the recorded electromyographic activity (EMG) into complex mul

Cited by 16 Source ↗Scholar ↗

2020

Gait patterns generation based on basis functions interpolation for the TWIN lower-limb exoskeleton

Christian Vassallo, Samuele De Giuseppe, Chiara Piezzo, Stefano Maludrottu, Giulio Cerruti, Maria Laura D’Angelo +7

ICRA 2020poster

Since the uprising of new biomedical orthotic devices, exoskeletons have been put in the spotlight for their possible use in rehabilitation. Even if these products might share some commonalities among them in terms of overall structure, degrees of freedom and possible actions, they quite often diffe…

Cited by 26 Source ↗Scholar ↗