Enhancement of gesture recognition for contactless interface using a personalized classifier in the operating room

Abstract Background and objective Contactless operating room (OR) interfaces are important for computer-aided surgery, and have been developed to decrease the risk of contamination during surgical procedures. Methods In this study, we used Leap Motion™, with a personalized automated classifier, to enhance the accuracy of gesture recognition for contactless interfaces. This software was trained and tested on a personal basis that means the training of gesture per a user. We used 30 features including finger and hand data, which were computed, selected, and fed into a multiclass support vector machine (SVM), and Naive Bayes classifiers and to predict and train five types of gestures including hover, grab, click, one peak, and two peaks. Results Overall accuracy of the five gestures was 99.58% ± 0.06, and 98.74% ± 3.64 on a personal basis using SVM and Naive Bayes classifiers, respectively. We compared gesture accuracy across the entire dataset and used SVM and Naive Bayes classifiers to examine the strength of personal basis training. Conclusions We developed and enhanced non-contact interfaces with gesture recognition to enhance OR control systems.