Azhar, H.

Barton, T.

Casey, A.

Sakel, M.

English

Canterbury Research and Theses Environment

BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients  Hannan Azhar1, Thomas Barton1, Ant Casey1 and Mohamed Sakel2 1Computing, Digital Forensics and Cybersecurity, Canterbury Christ Church University 2East Kent Hospitals University, NHS Foundation Trust, Canterbury, UK    Abstract Brain computer interface (BCI) controlled assistive robotic systems have been developed with increasing success with the aim to rehabilitation of patients after brain injury [1] to increase independence and quality of life. While such systems may use surgically implanted invasive sensors, non-invasive alternatives can be better suited due to ease of use, reduced cost, improvements in accuracy and reliability with the advancement of the technology and practicality of use. The consumer grade BCI devices often capable of integrating multiple types of signals, including Electroencephalogram (EEG) and Electromyogram (EMG) signals, as well as basic motion based signals such as gyroscopic data. This paper summarises the development of a BCI controlled robotic system using a non-invasive BCI headset “Muse” [2] and an open source robotic arm, U-Arm [2],  to  accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EMG and gyroscopic sensor readings to control the arm, which could perform a number of different tasks such as picking/placing objects or assist users in eating. Preliminary work was carried out to capture event driven EEG signals as alternative inputs to the controller. To avoid risks of injury while the device is being used in clinical settings, appropriate measures were incorporated into the software control of the arm. The project was a success in collaboration between the University and the East Kent Hospitals Neuro-Rehabilitation Unit, with the long term goal of testing the system in a clinical environment and up-scaling the system to larger robotic effectors. References:  1. L.E.H. van Dokkum, T. Ward, I. Laffont, Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke, Annals of Physical and Rehabilitation Medicine, Volume 58, Issue 1, Page 3, 2014. 2. Muse Headset, Available online: http://www.choosemuse.com/ [Retrieved August 2017] 3. U-Arm Robotic Arm, Available online: http://www.ufactory.cc/  [Retrieved August 2017]  Keywords: BCI, robotic arms, neurorehabilitation, assistive technology.     

BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients

https://repository.canterbury.ac.uk/download/6bd63ae18b08931d8655d8903b8f2e4191d0eb6c84fb35de9ed249508740adc9/388222/Abstract%20-BCi-HA.pdf

BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients

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