Publications de Iason Batzianoulis

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Humans use their hands mainly for grasping and manipulating objects, performing simple and dexterous tasks. The loss of a hand may significantly affect one's working status and independence in daily life. A restoration of the grasping ability is important ...

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised le ...

Robotic assistance via motorized robotic arm manipulators can be of valuable assistance to individuals with upper-limb motor disabilities. Brain-computer interfaces (BCI) offer an intuitive means to control such assistive robotic manipulators. However, BCI ...

Objective. The limited functionality of hand prostheses remains one of the main reasons behind the lack of its wide adoption by amputees. Indeed, while commercial prostheses can perform a reasonable number of grasps, they are often inadequate for manipulat ...

During reach-to-grasp motions,the Electromyographic (EMG) activity of the arm varies depending on motion stage. The variability of the EMG signals results in low classification accuracy during the reaching phase, delaying the activation of the prosthesis. ...

Background: Active upper-limb prostheses are used to restore important hand functionalities, such as grasping. In conventional approaches, a pattern recognition system is trained over a number of static grasping gestures. However, training a classifier in ...

In a human robot interaction scenario, predicting the human motion intention is essential for avoiding inconvenient delays and for a smooth reactivity of the robotic system. In particular, when dealing with hand prosthetic devices, an early estimation of t ...

Cognitive information has been exploited in non-invasive Brain Computer Interface (BCI) scenarios to provide autonomous external agents with additional information. In this context, Error-related potentials (ErrPs), temporal deflections in electroencephalo ...

This paper proposes an EMG based learning approach for estimating the displacement along the 2-axes (abduction/adduction and flexion/extension) of the human wrist in real-time. The algorithm extracts features from the EMG electrodes on the upper and forear ...

We propose a novel controller for powered prosthetic arms, where fused EMG and gaze data predict the desired end-point for a full arm prosthesis, which could drive the forward motion of individual joints. We recorded EMG, gaze, and motion-tracking during p ...

Predicting the grasping function during reach-to-grasp motions is essential for controlling a prosthetic hand or a robotic assistive device. An early accurate prediction increases the usability and the comfort of a prosthetic device. This work proposes an ...

Iason Batzianoulis

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Improving the arm-hand coordination in neuroprosthetics control with prior information from muscle activity

A structured prediction approach for robot imitation learning

Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials

EMG-driven shared human-robot compliant control for in-hand object manipulation in hand prostheses

Reach-to-grasp motions: Towards a dynamic classification approach for upper-limp prosthesis

Decoding the Grasping Intention from Electromyography during Reaching Motions

EMG-Based Analysis of the Upper Limb Motion

Inferring subjective preferences on robot trajectories using EEG signals

EMG-based learning approach for estimating wrist motion

Intent Prediction Based on Biomechanical Coordination of EMG and Vision-Filtered Gaze for End-Point Control of an Arm Prosthesis

EMG-based decoding of grasp gestures in reaching-to-grasping motions