EGGNOG: A Continuous, Multi-modal Data Set of Naturally Occurring Gestures with Ground Truth LabelsIsaac Wang, Mohtadi Ben Fraj, Pradyumna Narayana, Dhruva Patil, Gururaj Mulay, Rahul Bangar, J. Ross Beveridge, Bruce A. Draper, and Jaime Ruiz
People communicate through words and gestures, but current voice-based computer interfaces such as Siri exploit only words. This is a shame: human-computer interfaces would be natural if they incorporated gestures as well as words. To support this goal, we present a new dataset of naturally occurring gestures made by people working collaboratively on blocks world tasks. The dataset, called EGGNOG, contains over 8 hours of RGB video, depth video, and Kinect v2 body position data of 40 subjects. The data has been semi-automatically segmented into 24,503 movements, each of which has been labeled according to (1) its physical motion and (2) the intent of the participant. We believe this dataset will stimulate research into natural and gestural human-computer interfaces.
Authors
Isaac Wang
Jaime Ruiz
As well as: EGGNOG A Continuous, Multi-modal Data Set of Naturally Occurring Gestures with Ground Truth Labels
Citation
Isaac Wang, Mohtadi Ben Fraj, Pradyumna Narayana, Dhruva Patil, Gururaj Mulay, Rahul Bangar, J. Ross Beveridge, Bruce A. Draper, and Jaime Ruiz. 2017. EGGNOG: A Continuous, Multi-modal Data Set of Naturally Occurring Gestures with Ground Truth Labels. In 2017 12th IEEE International Conference on Automatic Face Gesture Recognition (FG 2017), 414–421. DOI: https://doi.org/10.1109/FG.2017.145
Bibtex
@inproceedings{wang_eggnog:2017,
author={I. Wang and M. B. Fraj and P. Narayana and D. Patil and G. Mulay and R. Bangar and J. R. Beveridge and B. A. Draper and J. Ruiz},
booktitle={2017 12th IEEE International Conference on Automatic Face Gesture Recognition (FG 2017)},
title={EGGNOG: A Continuous, Multi-modal Data Set of Naturally Occurring Gestures with Ground Truth Labels},
year={2017},
month={May},
pages={414-421},
keywords={gesture recognition;human computer interaction;EGGNOG dataset;Kinect v2 body position data;gestural human-computer interface;ground truth label;human-computer interface;natural human-computer interface;naturally occurring gesture;voice-based computer interface;Computer science;Gesture recognition;Human computer interaction;Layout;Motion segmentation;Sensors;Skeleton},
doi={10.1109/FG.2017.145},
}