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GOM-Face: GKP, EOG, and EMG-based multimodal interface with application to humanoid robot control.

Yunjun Nam, Bonkon Koo, Andrzej Cichocki

IEEE Transactions on Bio-Medical Engineering

|September 12, 2013

Summary

GOM-Face is a new human-machine interface using facial electric potentials for robot control. This system enables communication for individuals with motor disabilities by interpreting eye and tongue movements.

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Area of Science:

Biomedical Engineering
Human-Computer Interaction
Robotics

Background:

Assistive interfaces are crucial for individuals with motor disabilities.
Existing interfaces often utilize single physiological signals, limiting functionality.
Integrating multiple signals can enhance control and communication capabilities.

Purpose of the Study:

To introduce GOM-Face, a novel human-machine interface.
To demonstrate the combined use of glossokinetic potential (GKP), electrooculogram (EOG), and electromyogram for control.
To apply GOM-Face to humanoid robot control for enhanced human-robot interaction.

Main Methods:

Developed GOM-Face by measuring three facial electric potentials: GKP (tongue movement), EOG (eye movement), and electromyogram (teeth clenching).
Resolved signal interference between GKP and EOG using feature extraction from distinct covariance matrices.
Implemented a feature extraction method to differentiate between tongue and eye movements.

Main Results:

Achieved 86.7% accuracy in detecting four types of horizontal tongue or eye movements within 2.77 seconds.
Successfully demonstrated GOM-Face's applicability in controlling a humanoid robot.
Enabled users to interact with a robot via a predefined menu using eye and tongue movements.

Conclusions:

GOM-Face represents a significant advancement in multi-modal human-machine interfaces.
The system offers a promising alternative communication and control channel for individuals with severe motor impairments.
This technology has the potential to improve assistive robotics and communication aids.