Robot control systems using bio-potential signals

Robot control systems via human bio-potentials, such as electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG) signals offers unlimited opportunities to stakeholders. Research and development of the technology essential for estimating and identifying the usable biological...

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Main Authors: Sasaki, Minoru, Matsushita, Kojiro, Rusyidi, Muhammad Ilhamdi, Laksono, Pringgo Widyo, Muguro, Joseph, bin Suhaimi, Muhammad Syaiful Amri, Njeri, Waweru
Format: Conference Proceeding
Language:English
Subjects:
Control systems
Electroencephalography
Electromyography
Electrooculography
Eye movements
Inverse kinematics
Man-machine interfaces
Mapping
Muscles
People with disabilities
R&D
Research & development
Robot arms
Robot control
Robots
Signal processing
Workability
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creator Sasaki, Minoru
Matsushita, Kojiro
Rusyidi, Muhammad Ilhamdi
Laksono, Pringgo Widyo
Muguro, Joseph
bin Suhaimi, Muhammad Syaiful Amri
Njeri, Waweru
description Robot control systems via human bio-potentials, such as electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG) signals offers unlimited opportunities to stakeholders. Research and development of the technology essential for estimating and identifying the usable biological signals through sensors and signal processing techniques, as well as their conversion into control scheme has been carried out in the recent past. The need for bio-signal control is heightened by elderly and disabled people who through myriad of happenstances have lost control of the environment. To promote quality of life and self-reliance, biotechnology joined with Man-Machine interfaces are a promising undertaking. In this research, we utilize EOG, EMG and eye related information to control a robot in 3D environment. By mapping gaze motions to corresponding inverse kinematics, the operator can control a robot arm through his eye movements and facial muscles. The results prove the workability of the concept which on further improvement, would avail a dynamic 3D bio-signal control system.
doi_str_mv 10.1063/5.0000624
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source American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)
subjects Control systems
Electroencephalography
Electromyography
Electrooculography
Eye movements
Inverse kinematics
Man-machine interfaces
Mapping
Muscles
People with disabilities
R&D
Research & development
Robot arms
Robot control
Robots
Signal processing
Workability
title Robot control systems using bio-potential signals
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