Assessment of Force Feedback Models in a Haptic Device Using Alignment Accuracy and Brain Activity

Area of Science:

• Robotics
• Human-Computer Interaction
• Neuroscience

Background:

• Effective robot velocity control requires user force feedback to indicate input deviation from a neutral position.
• Haptic devices are crucial for transmitting force feedback in human-robot interaction.

Purpose of the Study:

• To investigate the impact of three distinct force feedback models on robot operability and user brain activity.
• To analyze how changes in force feedback model parameters affect performance and neural responses during a virtual alignment task.

Main Methods:

• Developed a haptic interface for an alignment task in virtual space.
• Implemented three force feedback models with one or two parameters.
• Measured operational performance and brain activation using functional near-infrared spectroscopy (fNIRS).
• Utilized quadratic function fitting with model parameters as design variables.

Main Results:

• Two force feedback models demonstrated superior alignment performance, particularly for targets distant from the neutral position.
• Significant changes in brain activation were observed for one model as its parameters were adjusted.
• Model parameter variations influenced both task performance and neural correlates of control.

Conclusions:

• The choice of force feedback model significantly impacts robot control operability and user cognitive load.
• Specific force feedback parameters can be optimized to enhance alignment task performance and modulate brain activity.
• Findings suggest a pathway for designing more intuitive and effective human-robot interfaces through tailored haptic feedback.