Dario Farina — accepted AI-conference papers

2025

MUniverse: A Simulation and Benchmarking Suite for Motor Unit Decomposition

Pranav Mamidanna, Thomas Klotz, Dimitrios Halatsis, Agnese Grison, Irene Mendez Guerra, Shihan Ma +4

NeurIPS 2025poster

Neural source separation enables the extraction of individual spike trains from complex electrophysiological recordings. When applied to electromyographic (EMG) signals, it provides a unique window into the motor output of the nervous system by isolating the spiking activity of motor units (MUs). MU…

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2024

Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios

Shihan Ma, Bo Hu, Tianyu Jia, Alexander Kenneth Clarke, Blanka Zicher, Arnault H. Caillet +2

NeurIPS 2024poster

The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroencephalography (EEG) and electromyography (EMG) recordings. However, current approaches for capturing high-level and contextual connectivity betwe…

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2024

Tackling Electrode Shift in Gesture Recognition with HD-EMG Electrode Subsets

João Pereira, Dimitrios Halatsis, Balint Hodossy, Dario Farina

ICASSP 2024accepted

sEMG pattern recognition algorithms have been explored extensively in decoding movement intent, yet are known to be vulnerable to changing recording conditions, exhibiting significant drops in performance across subjects, and even across sessions. Multi-channel surface EMG, also referred to as high-…

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2022

Hand Gesture Recognition Using Temporal Convolutions and Attention Mechanism

Elahe Rahimian, Soheil Zabihi, Amir Asif, Dario Farina, Seyed Farokh Atashzar, Arash Mohammadi

ICASSP 2022accepted

Advances in biosignal signal processing and machine learning, in particular Deep Neural Networks (DNNs), have paved the way for the development of innovative Human-Machine Interfaces for decoding the human intent and controlling artificial limbs. DNN models have shown promising results with respect…

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2020

Energetic Passivity Decoding of Human Hip Joint for Physical Human-Robot Interaction

Seyed Farokh Atashzar, Hsieng-Yung Huang, Fulvia Del Duca, Etienne Burdet, Dario Farina

RA-L 2020

The capacity of the biomechanics of human limbs to absorb energy during physical human-robot interaction (pHRI) can play an imperative role in controlling the performance of human-centered robotics systems. Using the concept of “excess of passivity,” we have recently designed passivity signature map

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2019

Online Finger Control Using High-Density EMG and Minimal Training Data for Robotic Applications

Michele Barsotti, Sigrid S. G. Dupan, Ivan Vujaklija, Strahinja Dosen, Antonio Frisoli, Dario Farina

RA-L 2019

A hand impairment can have a profound impact on the quality of life. This has motivated the development of dexterous prosthetic and orthotic devices. However, their control with neuromuscular interfacing remains challenging. Moreover, existing myocontrol interfaces typically require an extensive cal

Cited by 40 Source ↗Scholar ↗

2015

Simultaneous myoelectric control of a robot arm using muscle synergy-inspired inputs from high-density electrode grids

Mark Ison, Ivan Vujaklija, Bryan Whitsell, Dario Farina, Panagiotis Artemiadis

ICRA 2015poster

Myoelectric control has seen decades of research as a potential interface between human and machines. High-density surface electromyography (HDsEMG) non-invasively provides a rich set of signals representing underlying muscle contractions and, at a higher level, human motion intent. Many pattern rec…

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