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6DMG: A 6D Motion Gesture Dataset

This repository contains the dataset, associated codes, and the PDF copy for the work in the following paper:

M. Chen, G. AlRegib, B. Juang, 6DMG: A New 6D Motion Gesture Database, Proceedings of the Second ACM Multimedia Systems Conference (MMSys), Chapel Hill, NC, USA, February 22-24, 2012.

Introduction

Motion-based control is gaining popularity, and motion gestures form a complementary modality in human-computer interactions. To achieve more robust userindependent motion gesture recognition in a manner analogous to automatic speech recognition, we need a deeper understanding of the motions in gesture, which arouses the need for a 6D motion gesture database. Presented database contains comprehensive motion data, including the position, orientation, acceleration, and angular speed, for a set of common motion gestures performed by different users. This motion gesture database can be a useful platform for researchers and developers to build their recognition algorithms as well as a common test bench for performance comparisons.

Dataset Link

Download the database files from Zenodo: https://zenodo.org/records/10471794

6DMG Dataset

Description: Contain 28 participants (21 right-handed and 7 left-handed, 22 male and 6 female) Last update (Apr. 25. 2011)

Air-handwriting

Description: Contain 22 participants (all right-handed, 17 male and 5 female). Use SQLite to open the .db file.

Air-fingerwriting

Description: Contain 18 participants (all right-handed, 13 male and 5 female).

6DMG loader

Description: 6DMG loader shows how to load gestures from 6DMG database into the C++ struct. It is a good start point to wrap 6DMG into other applications. The code should work on major compilers, but is only tested on VS2008.

6DMG viewer

Description: 6DMG viewer loads gestures and render the motion on screen. To compile the source code of 6DMG viewer, you have to install Ogre SDK first. See the wiki for “how to”.

Technical Paper

The details about how the motion tracking and gesture recording are done. This work was first presented as a 2-page abstract poster in the workshop on Gesture Recognition in CVPR11. It will be appeared as a dataset paper in MMSys12.

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