Gesture Classification Using LSTM Recurrent Neural Networks
DOI
https://doi.org/10.1109/EMBC.2019.8857592
Document Type
Conference Proceeding
Publication Date
7-1-2019
Publication Title
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Abstract
The classification of human hand gestures has gained widespread recognition as a natural and powerful way to interact intuitively and efficiently with computers. Specifically, this approach has facilitated the development of a number of important applications in the medical training field, specially as a way to objectively evaluate surgical tasks of novices compared to an expert surgeon. In this paper, 3D medical gestures, acquired by an instrumented laparoscopic forceps, were classified using Long Short Term Memory (LSTM) recurrent neural networks (RNN). Recognition results are based on gesture dynamics and a comparison of gesture trajectories between novices to an expert motion are presented. Using LSTM RNN, we were able to achieve a recognition rate of 99.1%.
First Page
6864
Last Page
6867
ISSN
1557170X
ISBN
9781538613115
Recommended Citation
Cifuentes, Jenny; Boulanger, Pierre; Pham, Minh Tu; Prieto, Flavio; and Moreau, Richard, "Gesture Classification Using LSTM Recurrent Neural Networks" (2019). Scopus Unisalle. 145.
https://ciencia.lasalle.edu.co/scopus_unisalle/145
PubMed ID
31947417
Identifier
SCOPUS_ID:85077871506