title: Biomechanics-machine learning system for surgical gesture analysis and development of technologies for minimal access surgery.
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.
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy f.cavallo@sssup.it.
authors.forename: Filippo
authors.initials: F
authors.lastname: Cavallo
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Sinigaglia
authors.affiliation: Transplantation and New Technologies at Cisanello Hospital, Pisa, Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Megali
authors.affiliation: University of Pavia, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Pietrabissa
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Dario
authors.affiliation: Transplantation and New Technologies at Cisanello Hospital, Pisa, Italy.
authors.forename: Franco
authors.initials: F
authors.lastname: Mosca
authors.affiliation: University of Dundee, Dundee, United Kingdom.
authors.forename: Alfred
authors.initials: A
authors.lastname: Cuschieri
mesh_terms: D001132:Arm / Q000502:physiology; D001185:Artificial Intelligence; D001696:Biomechanical Phenomena / Q000502:physiology*; D006804:Ergonomics / Q000379:methods*; D005868:Gestures; D006801:Humans; D019060:Minimally Invasive Surgical Procedures / Q000379:methods*; D018482:Muscle, Skeletal / Q000502:physiology; D065287:Robotic Surgical Procedures / Q000379:methods*; D066231:Surgeons / Q000193:education
keywords: biomechanical analysis of movement; ergonomics; machine learning approach; metrics and benchmarks; surgical gesture analysis; surgical robotics