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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

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Depth camera based dataset of hand gestures.

Sindhusha Jeeru¹, Arun Kumar Sivapuram², David González León³

¹Department of Information and Communication Technology, University of Agder, Norway.

|November 25, 2022

Summary

This study introduces a comprehensive dataset of hand gestures captured using RGB and depth data. The dataset aids in developing accurate hand gesture recognition models for complex environments.

Keywords:

Depth image Machine learning RGB image RGB-D Camera Video hand gestures

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Area of Science:

Computer Vision
Human-Computer Interaction
Machine Learning

Background:

Accurate hand gesture recognition is crucial for intuitive human-computer interaction.
Existing datasets often lack diversity in backgrounds and capture conditions, limiting model generalizability.

Purpose of the Study:

To create a large-scale, diverse dataset for robust hand gesture classification.
To facilitate the development of advanced hand gesture recognition systems.

Main Methods:

Collected synchronized RGB and depth video frames using an Intel RealSense Depth Camera D435.
Captured 29,718 frames across 662 sequences (40 frames each) representing various hand gestures (scroll, zoom) from diverse individuals in complex backgrounds.
Ensured variations in hand orientation to enhance dataset representativeness.

Main Results:

A substantial dataset comprising both RGB and depth information for six distinct hand gestures.
The dataset includes significant variations in background, lighting, and hand poses.
Each gesture is represented by multiple sequences to capture dynamic variations.

Conclusions:

The developed dataset provides a valuable resource for training and evaluating hand gesture recognition models.
This dataset can advance the accuracy and reliability of gesture-based interfaces in real-world scenarios.