Arm exoskeleton for rehabilitation following stroke by learning algorithm prediction
Stroke is a major cause of disability in worldwide and also one of the causes of death after coronary heart disease. Many devices had been designed for hand motor function rehabilitation that a stroke survivor can use for bilateral movement practice. This paper presents an arm motor function rehabil...
| الحاوية / القاعدة: | Procedia Computer Science |
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| المؤلف الرئيسي: | |
| التنسيق: | Conference paper |
| اللغة: | English |
| منشور في: |
Elsevier B.V.
2014
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| الوصول للمادة أونلاين: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925612427&doi=10.1016%2fj.procs.2014.11.074&partnerID=40&md5=364de0587c9306034acd1eb16317de24 |
| id |
Fikri M.A.; Abdullah S.C.; Ramli M.H.M. |
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| spelling |
Fikri M.A.; Abdullah S.C.; Ramli M.H.M. 2-s2.0-84925612427 Arm exoskeleton for rehabilitation following stroke by learning algorithm prediction 2014 Procedia Computer Science 42 C 10.1016/j.procs.2014.11.074 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925612427&doi=10.1016%2fj.procs.2014.11.074&partnerID=40&md5=364de0587c9306034acd1eb16317de24 Stroke is a major cause of disability in worldwide and also one of the causes of death after coronary heart disease. Many devices had been designed for hand motor function rehabilitation that a stroke survivor can use for bilateral movement practice. This paper presents an arm motor function rehabilitation device where it is designed to predict the position angle for the robotic arm. MATLAB software is used for real-time positioning that can be developed by SIMULINK block diagram and proof by the simulator in program code in order for devising to operate under the position demand. All the angular motions or feedback to the simulation mode from the attached optical encoders via the Data Acquisition Card (DAQ). The learning algorithm can directly determine the position of its joint and can therefore completely eliminate the need for any system modelling. The robotic arm shows a successful implementation of the learning algorithm in predicting the behavior for arm exoskeleton. © 2014 The Authors. Elsevier B.V. 18770509 English Conference paper All Open Access; Gold Open Access |
| author |
2-s2.0-84925612427 |
| spellingShingle |
2-s2.0-84925612427 Arm exoskeleton for rehabilitation following stroke by learning algorithm prediction |
| author_facet |
2-s2.0-84925612427 |
| author_sort |
2-s2.0-84925612427 |
| title |
Arm exoskeleton for rehabilitation following stroke by learning algorithm prediction |
| title_short |
Arm exoskeleton for rehabilitation following stroke by learning algorithm prediction |
| title_full |
Arm exoskeleton for rehabilitation following stroke by learning algorithm prediction |
| title_fullStr |
Arm exoskeleton for rehabilitation following stroke by learning algorithm prediction |
| title_full_unstemmed |
Arm exoskeleton for rehabilitation following stroke by learning algorithm prediction |
| title_sort |
Arm exoskeleton for rehabilitation following stroke by learning algorithm prediction |
| publishDate |
2014 |
| container_title |
Procedia Computer Science |
| container_volume |
42 |
| container_issue |
C |
| doi_str_mv |
10.1016/j.procs.2014.11.074 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925612427&doi=10.1016%2fj.procs.2014.11.074&partnerID=40&md5=364de0587c9306034acd1eb16317de24 |
| description |
Stroke is a major cause of disability in worldwide and also one of the causes of death after coronary heart disease. Many devices had been designed for hand motor function rehabilitation that a stroke survivor can use for bilateral movement practice. This paper presents an arm motor function rehabilitation device where it is designed to predict the position angle for the robotic arm. MATLAB software is used for real-time positioning that can be developed by SIMULINK block diagram and proof by the simulator in program code in order for devising to operate under the position demand. All the angular motions or feedback to the simulation mode from the attached optical encoders via the Data Acquisition Card (DAQ). The learning algorithm can directly determine the position of its joint and can therefore completely eliminate the need for any system modelling. The robotic arm shows a successful implementation of the learning algorithm in predicting the behavior for arm exoskeleton. © 2014 The Authors. |
| publisher |
Elsevier B.V. |
| issn |
18770509 |
| language |
English |
| format |
Conference paper |
| accesstype |
All Open Access; Gold Open Access |
| record_format |
scopus |
| collection |
Scopus |
| _version_ |
1828987883211456512 |