Middle East Region: Turkey

Duygun Erol Barkana, Ismail Uzun,Devrim Tarakci,Ela Tarakci,Ayse Betul Oktay,Yusuf Sinan Akgul

Rehabilitation Robots for Neurorehabilitation in High-, Low-, and Middle-Income Countries（2024）

Yeditepe University | Istanbul Medipol University | Istanbul University-Cerrahpaşa | Yıldız Technical University | Gebze Technical University

Cited 0|Views7

Abstract

This chapter will first present health statistics for Turkey and describe conventional rehabilitation therapies used to rehabilitate patients with stroke. Next, we will present existing neurorehabilitation robotic centers that treat patients in Turkey and provide information about the upper/lower-extremity robots used in these centers. We will discuss the advantages and shortcomings of these robots and technological advances in neurorehabilitation. Then, we will describe the prototype upper/lower-extremity robots that are not yet used in the clinic but are still being developed in the universities to treat patients in Turkey. Next, we will discuss barriers to effective rehabilitation robotics and give an overview of the policies and legal and ethical issues around rehabilitation robotics in Turkey. Lastly, we will evaluate how rehabilitation has changed or is still changing during the COVID-19 pandemic, whether the need for rehabilitation robots will increase, and our opinions about the future of neurorehabilitation and what can be done further to use rehabilitation robots.

Translated text

Key words

Virtual Reality Rehabilitation,Rehabilitation Techniques,Rehabilitation

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper