@inproceedings{8842b5a4c2da4602994bb754c77f3dc1,
title = "Surgical hand gesture prediction for the operating room",
abstract = "Technological advancements in smart assistive technology enable navigating and manipulating various types of computer-aided devices in the operating room through a contactless gesture interface. Understanding surgeon actions is crucial to natural human-robot interaction in operating room since it means a sort of prediction a human behavior so that the robot can foresee the surgeon's intention, early choose appropriate action and reduce waiting time. In this paper, we present a new deep network based on Convolution Long Short-Term Memory (ConvLSTM) for gesture prediction configured to provide natural interaction between the surgeon and assistive robot and improve operating-room efficiency. The experimental results prove the capability of reliably recognizing unfinished gestures on videos. We quantitatively demonstrate the latter ability and the fact that GestureConvLSTM improves the baseline system performance on LSA64 dataset.",
keywords = "ConvLSTM, GestureConvLSTM, Hand gesture, Operating room, Prediction, Surgeon",
author = "Inna Skarga-Bandurova and Rostislav Siriak and Tetiana Biloborodova and Fabio Cuzzolin and Bawa, \{Vivek Singh\} and Mohamed, \{Mohamed Ibrahim\} and \{Dinesh Jackson Samuel\}, R.",
note = "Publisher Copyright: {\textcopyright} 2020 The authors and IOS Press.; 17th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2020 ; Conference date: 14-09-2020 Through 16-09-2020",
year = "2020",
month = sep,
day = "4",
doi = "10.3233/SHTI200621",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "97--103",
editor = "Bernd Blobel and Lenka Lhotska and Peter Pharow and Filipe Sousa",
booktitle = "pHealth 2020 - Proceedings of the 17th International Conference on Wearable Micro and Nano Technologies for Personalized Health",
address = "Netherlands",
}