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Surgical Hand Gesture Prediction for the Operating Room.
Inna Skarga-Bandurova1, Rostislav Siriak2, Tetiana Biloborodova2
1Oxford Brookes University.
Studies in Health Technology and Informatics
|October 22, 2020
Summary
This study introduces a new deep learning model for predicting surgeon gestures using contactless interfaces. The GestureConvLSTM model enhances human-robot interaction and operating room efficiency by recognizing unfinished gestures.
Area of Science:
- Medical technology
- Artificial intelligence
- Robotics
Background:
- Smart assistive technology allows contactless control of devices in operating rooms.
- Understanding surgeon actions is key for effective human-robot interaction and predicting intentions.
Purpose of the Study:
- To develop a deep network for gesture prediction to improve surgeon-robot interaction.
- To enhance operating room efficiency through early action selection by robots.
Main Methods:
- A novel deep network based on Convolution Long Short-Term Memory (ConvLSTM) was developed.
- The network was configured for gesture prediction using contactless interfaces.
Main Results:
- The ConvLSTM model reliably recognizes unfinished gestures from video data.
- The GestureConvLSTM system demonstrated improved performance over baseline systems on the LSA64 dataset.
Conclusions:
- The proposed GestureConvLSTM model enables natural human-robot interaction in surgical environments.
- This technology has the potential to improve operating room efficiency by reducing waiting times.
