Authors:
Giovanni Costantini
1
;
Giovanni Saggio
1
;
Laura Sbernini
2
;
Nicola Di Lorenzo
2
;
Franco Di Paolo
1
and
Daniele Casali
1
Affiliations:
1
University of Tor Vergata, Italy
;
2
Tor Vergata University, Italy
Keyword(s):
Neural Networks, Data Glove, Hand-Gesture, Classification, Surgery.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Data Manipulation
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neuroinformatics and Bioinformatics
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
In this work we used the HiTEg data glove to measure the skill of a physician or physician student in the execution of a typical surgical task: the suture. The aim of this project is to develop a system that, analyzing the movements of the hand, could tell if they are correct. To collect a set of measurements, we asked 18 subjects to performing the same task wearing the sensory glove. Nine subjects were skilled surgeons and nine subjects were non-surgeons, every subject performed ten repetitions of the same task, for two sessions, yielding to a dataset of 36 instances. Acquired data has been processed and classified with a neural network. A feature selection has been done considering only the features that have less variance among the expert subjects. The cross-validation of the classifier shows an error of 5.6%.