Intersected EMG heatmaps and deep learning based gesture recognition

Tools

Ke, Weijie and Xing, Yannan and Di Caterina, Gaetano and Petropoulakis, Lykourgos and Soraghan, John (2020) Intersected EMG heatmaps and deep learning based gesture recognition. In: 12th International Conference on Machine Learning and Computing, 2020-02-15 - 2020-02-17. (https://doi.org/10.1145/3383972.3383982)

[thumbnail of Ke-etal-ICMLC2020-Intersected-EMG-heatmaps-and-deep-learning-based-gesture-recognition]
Preview
 Text. Filename: Ke_etal_ICMLC2020_Intersected_EMG_heatmaps_and_deep_learning_based_gesture_recognition.pdf
Accepted Author Manuscript
Download (495kB)| Preview

Abstract

Hand gesture recognition in myoelectric based prosthetic devices is a key challenge to offering effective solutions to hand/lower arm amputees. A novel hand gesture recognition methodology that employs the difference of EMG energy heatmaps as the input of a specific designed deep learning neural network is presented. Experimental results using data from real amputees indicate that the proposed design achieves 94.31% as average accuracy with best accuracy rate of 98.96%. A comparison of experimental results between the proposed novel hand gesture recognition methodology and other similar approaches indicates the superior effectiveness of the new design.

ORCID iDs

Ke, Weijie, Xing, Yannan, Di Caterina, Gaetano ORCID logoORCID: https://orcid.org/0000-0002-7256-0897, Petropoulakis, Lykourgos ORCID logoORCID: https://orcid.org/0000-0003-3230-9670 and Soraghan, John ORCID logoORCID: https://orcid.org/0000-0003-4418-7391;
CORE (COnnecting REpositories)