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Book ChapterDOI

Real-Time 3D Tracker in Robot-Based Neurorehabilitation

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TLDR
A computer vision-based robot-assisted system used in neurorehabilitation of post-stroke patients that allows the subjects to reach for and grasp objects in a defined workspace is described.
Abstract
The chapter describes a computer vision-based robot-assisted system used in neurorehabilitation of post-stroke patients that allows the subjects to reach for and grasp objects in a defined workspace. The proposed computer vision technique is used to model objects that have not been preprocessed in a real setting, track them in real time, and provide their actual pose to the robotic device in order to accomplish grasping tasks. The robotic device is composed of three integrated modules: (i) a 4-DOF arm exoskeleton that supports the patient's impaired arm when reaching for the objects; (ii) a 3-DOF actuated wrist exoskeleton for optimizing the hand pose in the grasping task; and (iii) a 2-DOF (flexion/extension) underactuated hand exoskeleton designed to be automatically adjusted for different grasping tasks based on contact forces. The conducted tests have demonstrated the robustness of the proposed approach, and its performance in the neurorehabilitation scenario through reaching and grasping task experiments.

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Citations
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Journal ArticleDOI

Multi-DoFs Exoskeleton-Based Bilateral Teleoperation with the Time-Domain Passivity Approach

TL;DR: The results have shown that the TDPA is suitable for a multi-DoFs asymmetrical setup composed by two isomorphic haptic interfaces characterized by different mechanical features, and could be used in several teleoperation scenarios like home-based tele-rehabilitation and rescue operations.
Book ChapterDOI

A Survey on Deep Learning in Electromyographic Signal Analysis

TL;DR: The bibliometric research shows four distinct clusters focused on different applications: Hand Gesture Classification; Speech and Emotion Classification; Sleep Stage Classification; Other Applications, and most of the papers related to DL and EMG signal processing concerns the hand gesture classification, and the convolutional neural network is the most used technique.
Proceedings ArticleDOI

An undercomplete autoencoder to extract muscle synergies for motor intention detection

TL;DR: The presented AE have shown promising results in muscle synergy extraction comparing its performance with the Non-Negative Matrix Factorization algorithm, i.e. the most used approach in literature.
Journal ArticleDOI

Artificial Cognitive Systems Applied in Executive Function Stimulation and Rehabilitation Programs: A Systematic Review

TL;DR: A systematic review of studies on cognitive training programs based on artificial cognitive systems and digital technologies and their effect on executive functions was presented in this paper , where the authors identified which populations have been studied, the characteristics of the implemented programs, the types of implemented cognitive systems, the evaluated executive functions, and the key findings of these studies.

Recent developments in computer vision based real-time monitoring in health and well-being

TL;DR: Current gesture recognition for realtime detection of human emotions and alertness, sign language translation, detection of safety critical incidents such as fall incident detection, functional vision aids for partially and fully blind persons, tele-surgery, computer vision based diagnostic health examination methods (endoscopy, photoplethysmography, and digital mammography), and computer visionbased aids within rehabilitation are presented.
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