Filippo Cavallo1, Stefano Sinigaglia2, Giuseppe Megali3, Andrea Pietrabissa4, Paolo Dario2, Franco Mosca3, Alfred Cuschieri5. 1. The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy f.cavallo@sssup.it. 2. The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy. 3. Transplantation and New Technologies at Cisanello Hospital, Pisa, Italy. 4. University of Pavia, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy. 5. University of Dundee, Dundee, United Kingdom.
Abstract
BACKGROUND: The uptake of minimal access surgery (MAS) has by virtue of its clinical benefits become widespread across the surgical specialties. However, despite its advantages in reducing traumatic insult to the patient, it imposes significant ergonomic restriction on the operating surgeons who require training for the safe execution. Recent progress in manipulator technologies (robotic or mechanical) have certainly reduced the level of difficulty, however it requires information for a complete gesture analysis of surgical performance. This article reports on the development and evaluation of such a system capable of full biomechanical and machine learning. METHODS: The system for gesture analysis comprises 5 principal modules, which permit synchronous acquisition of multimodal surgical gesture signals from different sources and settings. The acquired signals are used to perform a biomechanical analysis for investigation of kinematics, dynamics, and muscle parameters of surgical gestures and a machine learning model for segmentation and recognition of principal phases of surgical gesture. RESULTS: The biomechanical system is able to estimate the level of expertise of subjects and the ergonomics in using different instruments. The machine learning approach is able to ascertain the level of expertise of subjects and has the potential for automatic recognition of surgical gesture for surgeon-robot interactions. CONCLUSIONS: Preliminary tests have confirmed the efficacy of the system for surgical gesture analysis, providing an objective evaluation of progress during training of surgeons in their acquisition of proficiency in MAS approach and highlighting useful information for the design and evaluation of master-slave manipulator systems.
BACKGROUND: The uptake of minimal access surgery (MAS) has by virtue of its clinical benefits become widespread across the surgical specialties. However, despite its advantages in reducing traumatic insult to the patient, it imposes significant ergonomic restriction on the operating surgeons who require training for the safe execution. Recent progress in manipulator technologies (robotic or mechanical) have certainly reduced the level of difficulty, however it requires information for a complete gesture analysis of surgical performance. This article reports on the development and evaluation of such a system capable of full biomechanical and machine learning. METHODS: The system for gesture analysis comprises 5 principal modules, which permit synchronous acquisition of multimodal surgical gesture signals from different sources and settings. The acquired signals are used to perform a biomechanical analysis for investigation of kinematics, dynamics, and muscle parameters of surgical gestures and a machine learning model for segmentation and recognition of principal phases of surgical gesture. RESULTS: The biomechanical system is able to estimate the level of expertise of subjects and the ergonomics in using different instruments. The machine learning approach is able to ascertain the level of expertise of subjects and has the potential for automatic recognition of surgical gesture for surgeon-robot interactions. CONCLUSIONS: Preliminary tests have confirmed the efficacy of the system for surgical gesture analysis, providing an objective evaluation of progress during training of surgeons in their acquisition of proficiency in MAS approach and highlighting useful information for the design and evaluation of master-slave manipulator systems.
Authors: Behnaz Poursartip; Marie-Eve LeBel; Laura C McCracken; Abelardo Escoto; Rajni V Patel; Michael D Naish; Ana Luisa Trejos Journal: Sensors (Basel) Date: 2017-08-05 Impact factor: 3.576