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

Journal article


Casey, A., Azhar, H., Grzes, M. and Sakel, M. 2019. BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients. Disability and Rehabilitation: Assistive Technology.
AuthorsCasey, A., Azhar, H., Grzes, M. and Sakel, M.
Abstract

Purpose: Brain–computer interface (BCI)-controlled assistive robotic systems have been developed with increasing success with the aim to rehabilitation of patients after brain injury 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 the 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 are often capable of integrating multiple types of signals, including Electroencephalogram (EEG) and Electromyogram (EMG) signals.

Materials and Methods: This paper summarizes the development of a portable and cost-efficient BCI-controlled assistive technology using a non-invasive BCI headset “OpenBCI” and an open source robotic arm, U-Arm, to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EEG and EMG sensor readings to control the arm. 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. A short survey was used following the system usability scale (SUS), to measure the usability of the technology to be trialed in clinical settings.

Results: From the experimental results, it was found that EMG is a very reliable method for assistive technology control, provided that the user specific EMG calibration is done. With the EEG, even though the results were promising, due to insufficient detection of the signal, the controller was not adequate to be used within a neurorehabilitation environment. The survey indicated that the usability of the system is not a barrier for moving the system into clinical trials.

Implication on rehabilitation
For the rehabilitation of patients suffering from neurological disabilities (particularly those suffering from varying degrees of paralysis), it is necessary to develop technology that bypasses the limitations of their condition. For example, if a patient is unable to walk due to the unresponsiveness in their motor neurons, technology can be developed that used an alternate input to move an exoskeleton, which enables the patient to walk again with the assistance of the exoskeleton.

This research focuses on neuro-rehabilitation within the framework of the NHS at the Kent and Canterbury Hospital in UK. The hospital currently does not have any system in place for self-driven rehabilitation and instead relies on traditional rehabilitation methods through assistance from physicians and exercise regimens to maintain muscle movement.

This paper summarises the development of a portable and cost-efficient BCI controlled assistive technology using a non-invasive BCI headset “OpenBCI” and an open source robotic arm, U-Arm, to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EEG and EMG sensor readings to control the arm, which could perform a number of different tasks such as picking/placing objects or assist users in eating.

KeywordsBCI; Disability; EMG; EEG; Assistive technology
Year2019
JournalDisability and Rehabilitation: Assistive Technology
PublisherTaylor and Francis Online
ISSN1748-3107
Digital Object Identifier (DOI)doi:10.1080/17483107.2019.1683239
Official URLhttp://doi.org/10.1080/17483107.2019.1683239
Publication dates
Online11 Nov 2019
Publication process dates
Accepted25 Sep 2019
Deposited24 Feb 2020
Permalink -

https://repository.canterbury.ac.uk/item/8qx04/bci-controlled-robotic-arm-as-assistance-to-the-rehabilitation-of-neurologically-disabled-patients

Accepted author manuscript

  • 5
    total views
  • 0
    total downloads
  • 3
    views this month
  • 0
    downloads this month

Related outputs

Comparisons of forensic tools to recover ephemeral data from iOS apps used for cyberbullying
Chamberlain, A. and Azhar, H. 2019. Comparisons of forensic tools to recover ephemeral data from iOS apps used for cyberbullying. in: CYBER 2019, The Fourth International Conference on Cyber-Technologies and Cyber-Systems IARIA. pp. 88-93
Recovery of forensic artefacts from a smart home IoT ecosystem
Azhar, H. and Bate, S. 2019. Recovery of forensic artefacts from a smart home IoT ecosystem. in: CYBER 2019, The Fourth International Conference on Cyber-Technologies and Cyber-Systems IARIA. pp. 94-99
A cost-effective BCI assisted technology framework for neurorehabilitation
Azhar, H., Casey, A. and Sakel, M. 2018. A cost-effective BCI assisted technology framework for neurorehabilitation.
Effects of students’ preferences in use of lighting and temperature on productivity in a university setting
Azhar, H., Islam, T. and Alfieri, M. 2019. Effects of students’ preferences in use of lighting and temperature on productivity in a university setting. in: Zheng, P., Callaghan, V., Crawford, D., Kymalainen, T. and Reyes-Munoz, A. (ed.) EAI International Conference on Technology, Innovation, Entrepreneurship and Education Springer.
Use of wearable technology to measure emotional responses amongst tennis players
Azhar, H., Nelson, T. and Casey, A. 2019. Use of wearable technology to measure emotional responses amongst tennis players. in: Zheng, P., Callaghan, V., Crawford, D., Kymalainen, T. and Reyes-Munoz, A. (ed.) EAI International Conference on Technology, Innovation, Entrepreneurship and Education Springer.
Drone forensic analysis using open source tools
Azhar, H., Barton, T. and Islam, T. 2018. Drone forensic analysis using open source tools. Journal of Digital Forensics, Security and Law. 13 (1), pp. 7-30.
An investigation on forensic opportunities to recover evidential data from mobile phones and personal computers
Naughton, P. and Azhar, H. 2017. An investigation on forensic opportunities to recover evidential data from mobile phones and personal computers.
BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients
Azhar, H., Barton, T., Casey, A. and Sakel, M. 2017. BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients. Research and Knowledge Exchange Conference 2017.
Open source forensics for a multi-platform drone system
Barton, T. and Azhar, H. 2018. Open source forensics for a multi-platform drone system. in: Matousek, P. and Schmiedecker, M. (ed.) 9th EAI International Conference on Digital Forensics & Cyber Crime Springer. pp. 83-96
Forensic analysis of popular UAV systems
Barton, T. and Azhar, H. 2017. Forensic analysis of popular UAV systems. Emerging Security Technologies (EST), 2017 Seventh International Conference on.
Evaluation of the MPS Predictive Policing Trial (redacted)
Bryant, R., Azhar, H., Blackburn, B. and Falade, M. 2015. Evaluation of the MPS Predictive Policing Trial (redacted).
A wearable brain-computer interface controlled robot
Azhar, H., Badicioiu, A. and Barton, T. 2016. A wearable brain-computer interface controlled robot.
Forensic analysis of the recovery of Wickr’s ephemeral data on Android platforms
Barton, T. and Azhar, H. 2016. Forensic analysis of the recovery of Wickr’s ephemeral data on Android platforms. in: Klemas, T. and Falk, R. (ed.) CYBER 2016 : The First International Conference on Cyber-Technologies and Cyber-Systems IARIA. pp. 35-40
Forensic analysis of secure ephemeral messaging applications on Android platforms
Azhar, H. and Barton, T. 2017. Forensic analysis of secure ephemeral messaging applications on Android platforms. in: Global Security, Safety and Sustainability - The Security Challenges of the Connected World: 11th International Conference, ICGS3 2017, London, UK, January 18-20, 2017, Proceedings Springer.
Forensic acquisitions of WhatsApp data on popular mobile platforms
Shortall, A. and Azhar, H. 2015. Forensic acquisitions of WhatsApp data on popular mobile platforms. in: Proceedings of the Sixth International Conference on Emerging Security Technologies IEEE Press. pp. 13-17
Usability and performance measure of a consumer-grade brain computer interface system for environmental control by neurological patients
Deravi, F., Ang, C., Azhar, H., Al-Wabil, A., Philips, M. and Sakel, M. 2015. Usability and performance measure of a consumer-grade brain computer interface system for environmental control by neurological patients. International Journal of Engineering and Technology Innovation (IJETI). 5 (3), pp. 165-177.
Criticality dispersion in swarms to optimize n-tuples
Azhar, H., Deravi, F. and Dimond, K. 2008. Criticality dispersion in swarms to optimize n-tuples. in: GECCO '08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation New York Association for Computing Machinery. pp. 1-8
Particle swarm intelligence to optimize the learning of n-tuples
Azhar, H., Deravi, F. and Dimond, K. 2008. Particle swarm intelligence to optimize the learning of n-tuples. Journal of Intelligent Systems. 17 (S), pp. 169-196.
Automatic identification of wildlife using local binary patterns
Azhar, H., Hoque, S. and Deravi, F. 2012. Automatic identification of wildlife using local binary patterns. in: IET Conference on Image Processing (IPR 2012) Institute of Engineering and Technology. pp. 5-11
Zoometrics - biometric identification of wildlife using natural body marks
Hoque, S., Azhar, H. and Deravi, F. 2011. Zoometrics - biometric identification of wildlife using natural body marks. International Journal of Bio-Science and Bio-Technology. 3 (3), pp. 45-53.