Skarga-Bandurova Inna, Siriak Rostislav, Biloborodova Tetiana, Cuzzolin Fabio, Bawa Vivek Singh, Mohamed Mohamed Ibrahim, Samuel R Dinesh Jackson
Oxford Brookes University.
Volodymyr Dahl East Ukrainian National University.
Stud Health Technol Inform. 2020 Sep 4;273:97-103. doi: 10.3233/SHTI200621.
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.
智能辅助技术的技术进步使得通过非接触式手势界面在手术室中导航和操作各种类型的计算机辅助设备成为可能。理解外科医生的动作对于手术室中自然的人机交互至关重要，因为这意味着对人类行为的一种预测，以便机器人能够预见外科医生的意图，提前选择合适的动作并减少等待时间。在本文中，我们提出了一种基于卷积长短期记忆（ConvLSTM）的新深度网络，用于手势预测，其配置旨在提供外科医生与辅助机器人之间的自然交互并提高手术室效率。实验结果证明了可靠识别视频中未完成手势的能力。我们定量地展示了后一种能力以及GestureConvLSTM在LSA64数据集上提高基线系统性能这一事实。
