Yongwon Cho1, Areum Lee1, Jongha Park1, Bemseok Ko1, Namkug Kim2. 1. ASAN Medical Center, Seoul 05505, Republic of Korea. 2. ASAN Medical Center, Seoul 05505, Republic of Korea. Electronic address: nkkim@amc.seoul.kr.
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.
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.
Authors: Fahmid Al Farid; Noramiza Hashim; Junaidi Abdullah; Md Roman Bhuiyan; Wan Noor Shahida Mohd Isa; Jia Uddin; Mohammad Ahsanul Haque; Mohd Nizam Husen Journal: J Imaging Date: 2022-05-26