Enhancement of gesture recognition for contactless interface using a personalized classifier in the operating room. (July 2018)

• Record Type: Journal Article
• Authors: Cho, Yongwon; Lee, Areum; Park, Jongha; Ko, Bemseok; Kim, Namkug
• Is Part Of: Computer methods and programs in biomedicine. Volume 161(2018)
• Page Start: 39
• Page End: 44
• Publication Date: 2018-07
• Subjects: Gesture recognition -- Human–computer interaction -- Support vector machine -- Surgeon–computer interaction; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.04.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The risk of contamination could be decreased during surgical procedures. We used Leap Motion™, with a personalized automated classifier, to enhance the accuracy of gesture recognition. We used a multiclass support vector machine classifier and Naïve Bayes classifiers to predict and train five types of gestures including hover, grab, click, one peak, and two peak. We compared gesture accuracy across the entire dataset to examine the strength of personal basis training. We developed and enhanced non-contact interfaces with gesture recognition to enhance OR control systems. 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 Naïve 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 Naïve Bayes classifiers, respectively. We compared gesture accuracy across the entire dataset and used SVM and Naïve 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.