Trained by demonstration humanoid robot controlled via a BCI system for telepresence

Saduanov, Batyrkhan; Alizadeh, Tohid; An, Jinung; Abibullaev, Berdakh

doi:10.1109/iww-bci.2018.8311508

Cited by 15 publications

(5 citation statements)

References 13 publications

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“…Overview: The application discussed in the previous section was simpler in terms of the actions performed, but provided a granularity of control that is sometimes not desired at the user level. This section discusses one such application in which a person is able to interact with the world using telepresence through a humanoid [ 68 ]. The control commands to be given to the humanoid in this case are high level, i.e., humanoid perform several subtasks that are grouped together and denoted as one high-level task (event, a few of such events can be seen in Figure 8 a).…”

Section: Bci-controlled Humanoid Applications Using Only Eegmentioning

confidence: 99%

“…Logistic regression was used for the classification of signals. It was used to train the function for predicting the output into the target or non-target events [ 68 ]. For the validation of the trained model, the subjects were asked to control the humanoids by brain signals.…”

Section: Bci-controlled Humanoid Applications Using Only Eegmentioning

confidence: 99%

“… ( a ) 4 × 4 Grid showing high-level commands and ( b ) abstract system pipeline for telepresence (adapted from: [ 68 ]). …”

Section: Figurementioning

confidence: 99%

See 2 more Smart Citations

Brain-Computer Interface-Based Humanoid Control: A Review

Chamola

Vineet

Nayyar

et al. 2020

Sensors

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A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications.

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Section: Bci-controlled Humanoid Applications Using Only Eegmentioning

confidence: 99%

Section: Bci-controlled Humanoid Applications Using Only Eegmentioning

confidence: 99%

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Brain-Computer Interface-Based Humanoid Control: A Review

Chamola

Vineet

Nayyar

et al. 2020

Sensors

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“…Implementation of action is a complicated process that requires various control, trajectory generation, timing, and other systems to be involved. Although ITR of BCI systems does not allow such full supervision of the robot, low-level control may be redundant in such a pipeline, since even human beings are not decoding body motions consciously at mechanical and dynamical levels [32]. Programming by demonstration is a convenient way of teaching robots intuitively by imitation or enforced demonstrations.…”

Section: Numerous Studies Have Demonstrated Successful Outcomes In Comentioning

confidence: 99%

Design and Optimization of a BCI-Driven Telepresence Robot Through Programming by Demonstration

et al. 2019

Self Cite

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Improving the life quality of people with severe motor paralysis has a significant impact on restoring their functional independence to perform activities of daily living (ADL). Telepresence is a subfield of the robotic-assisted route, where human plays the role of an operator, sending high-level instructions to an assistive robot while receiving sensory feedback. However, for severely motor-impaired people, conventional interaction modalities may not be suitable due to their complete paralysis. Thus, designing alternative ways of interaction such as Brain-Computer Interfaces (BCI) is essential for a telepresence capability. We propose a novel framework that integrates a BCI system and a humanoid robot to develop a brain-controlled telepresence system with multimodal control features. In particular, the low-level control is executed by Programming by Demonstration (PbD) models, and the higher-level cognitive commands are produced by a BCI system to perform vital ADLs. The presented system is based on real-time decoding of attentionmodulated neural responses elicited in the brain electroencephalographic signals and generating multiple control commands. As a result, the system allows a user to interact with a humanoid robot while receiving auditory and visual feedback from the robot's sensors. We validated our system across ten subjects in a realistic scenario. The experimental results show the feasibility of the approach in the design of a telepresence robot with high BCI decoding performances. INDEX TERMS Brain-Computer interfaces, telepresence, programming by demonstration, EEG, eventrelated potentials, humanoid robots.

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“…The accuracy of the system was 74.5%. In [ 10 ], Saduanov et al developed a framework for a P300-based BCI telepresence robot. The robot can execute 16 commands, including 7 present commands and 9 navigation commands.…”

Section: Introductionmentioning

confidence: 99%

A Human-Machine Interface Based on an EOG and a Gyroscope for Humanoid Robot Control and Its Application to Home Services

Wang

Pan

2022

Journal of Healthcare Engineering

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The human-machine interface (HMI) has been studied for robot teleoperation with the aim of empowering people who experience motor disabilities to increase their interaction with the physical environment. The challenge of an HMI for robot control is to rapidly, accurately, and sufficiently produce control commands. In this paper, an asynchronous HMI based on an electrooculogram (EOG) and a gyroscope is proposed using two self-paced and endogenous features, double blink and head rotation. By designing the multilevel graphical user interface (GUI), the user can rotate his head to move the cursor of the GUI and create a double blink to trigger the button in the interface. The proposed HMI is able to supply sufficient commands at the same time with high accuracy (ACC) and low response time (RT). In the trigger task of sixteen healthy subjects, the target was clicked from 20 options with ACC of 99.2% and RT 2.34 s. Furthermore, a continuous strategy that uses motion start and motion stop commands to create a certain robot motion is proposed to control a humanoid robot based on the HMI. It avoids the situation that combines some commands to achieve one motion or converts the certain motion to a command directly. In the home service experiment, all subjects operated a humanoid robot changing the state of a switch, grasping a key, and putting it into a box. The time ratio between HMI control and manual control was 1.22, and the number of commands ratio was 1.18. The results demonstrated that the continuous strategy and proposed HMI can improve performance in humanoid robot control.

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Trained by demonstration humanoid robot controlled via a BCI system for telepresence

Cited by 15 publications

References 13 publications

Brain-Computer Interface-Based Humanoid Control: A Review

Brain-Computer Interface-Based Humanoid Control: A Review

Design and Optimization of a BCI-Driven Telepresence Robot Through Programming by Demonstration

A Human-Machine Interface Based on an EOG and a Gyroscope for Humanoid Robot Control and Its Application to Home Services

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