Title

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

PubMed ID

31947417

Identifier

SCOPUS_ID:85077871506

Documento original

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