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Development of Electrooculogram-based Multi-Assistive Device Control System for Neuromuscular Disabled / Biswajeet Champaty

By: Champaty, Biswajeet.
Contributor(s): Pal, Kunal [Supervisor] | Thirugnanam A [Supervisor] | Department of Biotechnology and Medical Engineering.
Material type: materialTypeLabelBookPublisher: 2019Description: 142p.Subject(s): Biomedical Engineering | BiotechnologyOnline resources: Click here to access online Dissertation note: Thesis Ph.D/M.Tech (R) National Institute of Technology, Rourkela Summary: In recent years, Electrooculogram (EOG) signal has been studied by various researchers for developing human-computer interface (HCI) based assistive devices for the people with severe physical disabilities. This is attributed to the fact that the muscles responsible for the movement of eye are not significantly affected with the progression of the neuromuscular diseases. The current study delineates the development of an EOG based computer-aided multimodal control system and its application for the manipulation of a scaled-down wheelchair prototype, a computer aided communication support system and a robotic arm prototype. An EOG based HCI system was developed, comprising of three interlinked sub-systems, namely, EOG signal sensing, conditioning, and pre-processing sub-systems. The acquired EOG signals were processed and classified in a MATLAB based Graphical User Interface (GUI) to generate control signals. The GUI was capable to monitor the proper functionality of the system. The user details were registered and the system operability was monitored in the same GUI. The HCI system was initially used for controlling wireless wheelchair prototype. Secondly, a computer-aided communication support system was controlled so as to initiate communication protocols like telephonic call, sending SMS and email through HCI. The performance of the proposed assistive device was evaluated through volunteer activities. Finally a wireless controlled robotic arm movement was achieved. The execution time was reduced with a short training. The ability of the developed control system for controlling a multimodal system was successfully explored. Combination of control signals and signals from the Hall-effect (HE) sensor(s) were used for selective manipulation of the assistive devices. Due to multitasking and ease of use of the proposed device, the quality of life of the incapacitated individuals can be improved with greater independence.
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Thesis (Ph.D/M.Tech R) Thesis (Ph.D/M.Tech R) Thesis Section Reference Not for loan T883

Thesis Ph.D/M.Tech (R) National Institute of Technology, Rourkela

In recent years, Electrooculogram (EOG) signal has been studied by various researchers for developing human-computer interface (HCI) based assistive devices for the people with severe physical disabilities. This is attributed to the fact that the muscles responsible for the movement of eye are not significantly affected with the progression of the neuromuscular diseases. The current study delineates the development of an EOG based computer-aided multimodal control system and its application for the manipulation of a scaled-down wheelchair prototype, a computer aided communication support system and a robotic arm prototype. An EOG based HCI system was developed, comprising of three interlinked sub-systems, namely, EOG signal sensing, conditioning, and pre-processing sub-systems. The acquired EOG signals were processed and classified in a MATLAB based Graphical User Interface (GUI) to generate control signals. The GUI was capable to monitor the proper functionality of the system. The user details were registered and the system operability was monitored in the same GUI. The HCI system was initially used for controlling wireless wheelchair prototype. Secondly, a computer-aided communication support system was controlled so as to initiate communication protocols like telephonic call, sending SMS and email through HCI. The performance of the proposed assistive device was evaluated through volunteer activities. Finally a wireless controlled robotic arm movement was achieved. The execution time was reduced with a short training. The ability of the developed control system for controlling a multimodal system was successfully explored. Combination of control signals and signals from the Hall-effect (HE) sensor(s) were used for selective manipulation of the assistive devices. Due to multitasking and ease of use of the proposed device, the quality of life of the incapacitated individuals can be improved with greater independence.

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