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Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

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Vision-Based Human-Machine Interface for an Assistive Robotic Exoskeleton Glove.

Yunfei Guo¹, Wenda Xu², Pinhas Ben-Tzvi^1,2

¹Electrical and Computer Engineering Department, Virginia Tech, Blacksburg, VA, USA.

Research Square

|September 11, 2023

Summary

This study introduces a vision-based Human-Machine Interface (HMI) for exoskeleton gloves, improving grasp success rates. The new system estimates grasp force using object size and material, outperforming older methods.

Keywords:

Exoskeleton Glove Force Planning Human Machine Interface Material Classification

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Area of Science:

Robotics
Human-Machine Interaction
Computer Vision

Background:

Traditional force planning in Human-Machine Interfaces (HMIs) for assistive gloves faces limitations with unfamiliar objects and inadequate initial grasp force.
Electroencephalogram (EEG) and Electromyography (EMG) based HMIs offer direct control but have usability constraints.
Voice and vision-based HMIs struggle with accurate grasp force planning.

Conclusions:

The proposed vision-based HMI significantly enhances the grasp success rate for assistive exoskeleton gloves.
Estimating grasp force based on object properties (size, material) is a viable and effective strategy.
This approach offers a promising solution for more robust and reliable human-machine interaction in assistive robotics.