Title |
A Magnetic Resonance Compatible Soft Wearable Robotic Glove for Hand Rehabilitation and Brain Imaging
|
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Published in |
IEEE Transactions on Neural Systems and Rehabilitation Engineering, June 2017
|
DOI | 10.1109/tnsre.2016.2602941 |
Pubmed ID | |
Authors |
Hong Kai Yap, Nazir Kamaldin, Jeong Hoon Lim, Fatima A. Nasrallah, James Cho Hong Goh, Chen-Hua Yeow |
Abstract |
In this paper, we present the design, fabrication and evaluation of a soft wearable robotic glove, which can be used with functional Magnetic Resonance imaging (fMRI) during the hand rehabilitation and task specific training. The soft wearable robotic glove, called MR-Glove, consists of two major components: a) a set of soft pneumatic actuators and b) a glove. The soft pneumatic actuators, which are made of silicone elastomers, generate bending motion and actuate finger joints upon pressurization. The device is MR-compatible as it contains no ferromagnetic materials and operates pneumatically. Our results show that the device did not cause artifacts to fMRI images during hand rehabilitation and task-specific exercises. This study demonstrated the possibility of using fMRI and MR-compatible soft wearable robotic device to study brain activities and motor performances during hand rehabilitation, and to unravel the functional effects of rehabilitation robotics on brain stimulation. |
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Japan | 1 | 25% |
United States | 1 | 25% |
Singapore | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
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Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
Geographical breakdown
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Unknown | 183 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 37 | 20% |
Student > Master | 32 | 17% |
Researcher | 17 | 9% |
Student > Bachelor | 16 | 9% |
Student > Doctoral Student | 13 | 7% |
Other | 24 | 13% |
Unknown | 44 | 24% |
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Engineering | 87 | 48% |
Nursing and Health Professions | 9 | 5% |
Medicine and Dentistry | 7 | 4% |
Computer Science | 6 | 3% |
Neuroscience | 5 | 3% |
Other | 18 | 10% |
Unknown | 51 | 28% |