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Use of automated system in upper extremity paralysis care proves beneficial for patients and medical staff

Automated systems for patient care


Rehabilitation robot training programs show promise in treating upper body paralysis.

Credit:  Osaka Metropolitan University

The increasing number of strokes and subsequent rehabilitation has highlighted the growing need for effective care strategies. Serious side effects, such as motor paralysis, can be challenging to mend, but the recent incorporation of robots into treatment has shown promise.

Automated robots repeatedly provide the proper movements necessary to recover motor function. However, to ensure appropriate care tailored to the degree of motor paralysis, knowledge of robots and rehabilitation is needed.

Osaka Metropolitan University Professor Takashi Takebayashi of the Graduate School of Rehabilitation Science led a team in collecting data from the actual use of Teijin Pharma Ltd.’s rehabilitation robot ReoGo-J.

The team looked into the rehabilitation programs that were selected by medical staff to match the degree of motor paralysis. By analyzing the data, the group developed the world’s first system that automatically recommends the optimal rehabilitation program. Based on a simple test to check the degree of motor paralysis in a patient’s hands, an appropriate treatment can be determined.

“By using this system, as long as medical professionals can carry out the test, even staff without experience with robots can provide appropriate robotic rehabilitation for motor paralysis,” stated Professor Takebayashi. “We hope this will lead to the further promotion of robot rehabilitation and a reduction in the burden on medical staff.”

The findings are published in Scientific Reports.

Funding

This work was supported by Teijin Pharma Ltd., Tokyo, Japan.

Paper information

Journal: Scientific Reports
Title: Automatic setting optimization for robotic upper-extremity rehabilitation in patients with stroke using ReoGo-J: a cross-sectional clinical trial
DOI: 10.1038/s41598-024-74672-2
Authors: Takashi Takebayashi, Yuki Uchiyama & Kazuhisa Domen
Published:  28 October 2024
URL: https://doi.org/10.1038/s41598-024-74672-2

Contact

Takashi Takebayashi

Graduate School of Rehabilitation Science
E-mail: takshi77[at]omu.ac.jp

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