Mahtab J Fard1, Abhilash K Pandya2, Ratna B Chinnam1, Michael D Klein3, R Darin Ellis1. 1. Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI, USA. 2. Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, USA. 3. Department of Surgery, Wayne State University School of Medicine and Pediatric Surgery, Children's Hospital of Michigan, Detroit, MI, USA.
Abstract
BACKGROUND: Robotic-assisted surgery allows surgeons to perform many types of complex operations with greater precision than is possible with conventional surgery. Despite these advantages, in current systems, a surgeon should communicate with the device directly and manually. To allow the robot to adjust parameters such as camera position, the system needs to know automatically what task the surgeon is performing. METHODS: A distance-based time series classification framework has been developed which measures dynamic time warping distance between temporal trajectory data of robot arms and classifies surgical tasks and gestures using a k-nearest neighbor algorithm. RESULTS: Results on real robotic surgery data show that the proposed framework outperformed state-of-the-art methods by up to 9% across three tasks and by 8% across gestures. CONCLUSION: The proposed framework is robust and accurate. Therefore, it can be used to develop adaptive control systems that will be more responsive to surgeons' needs by identifying next movements of the surgeon.
BACKGROUND: Robotic-assisted surgery allows surgeons to perform many types of complex operations with greater precision than is possible with conventional surgery. Despite these advantages, in current systems, a surgeon should communicate with the device directly and manually. To allow the robot to adjust parameters such as camera position, the system needs to know automatically what task the surgeon is performing. METHODS: A distance-based time series classification framework has been developed which measures dynamic time warping distance between temporal trajectory data of robot arms and classifies surgical tasks and gestures using a k-nearest neighbor algorithm. RESULTS: Results on real robotic surgery data show that the proposed framework outperformed state-of-the-art methods by up to 9% across three tasks and by 8% across gestures. CONCLUSION: The proposed framework is robust and accurate. Therefore, it can be used to develop adaptive control systems that will be more responsive to surgeons' needs by identifying next movements of the surgeon.
Keywords:
automatic camera control; distance-based classification; dynamic time warping; k-nearest neighbor; robotic surgery; task and gesture recognition; time series classification