American Sign Language (ASL) Fingerspelling dataset for Myo Sensor

This is the dataset used in the following publication. Please cite this publication if use this dataset:


This work was published on the 2017 ACM IUI .

@inproceedings{paudyal2016sceptre,
title={Sceptre: a pervasive, non-invasive, and programmable gesture recognition technology},
author={Paudyal, Prajwal and Banerjee, Ayan and Gupta, Sandeep KS},
booktitle={Proceedings of the 21st International Conference on Intelligent User Interfaces},
pages={282--293},
year={2016},
organization={ACM}
}

@inproceedings{paudyal2017dyfav,
title={Dyfav: Dynamic feature selection and voting for real-time recognition of fingerspelled alphabet using wearables},
author={Paudyal, Prajwal and Lee, Junghyo and Banerjee, Ayan and Gupta, Sandeep KS},
booktitle={Proceedings of the 22nd International Conference on Intelligent User Interfaces},
pages={457--467},
year={2017},
organization={ACM}
}

9 users wore the Myo Armband and data was collected for 5s. for each letter of the alphabet. The first 8 columns contain data for the 8 EMG pods, the next 3 are for Accelerometer, the next 3 are for Gyroscope and the final 3are for Orientation (Roll, Pitch and Yaw)