ASAN Medical Center, Seoul 05505, Republic of Korea.
ASAN Medical Center, Seoul 05505, Republic of Korea.
Comput Methods Programs Biomed. 2018 Jul;161:39-44. doi: 10.1016/j.cmpb.2018.04.003. Epub 2018 Apr 6.
Contactless operating room (OR) interfaces are important for computer-aided surgery, and have been developed to decrease the risk of contamination during surgical procedures.
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.
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.
We developed and enhanced non-contact interfaces with gesture recognition to enhance OR control systems.
非接触式手术室（OR）界面对于计算机辅助手术至关重要，其旨在降低手术过程中污染的风险。
在本研究中，我们使用 Leap Motion™和个性化自动化分类器来提高非接触式界面的手势识别准确性。该软件在个人基础上进行训练和测试，即针对每个用户的手势进行训练。我们使用了 30 个特征，包括手指和手部数据，这些特征经过计算、选择，并输入到多类支持向量机（SVM）和朴素贝叶斯分类器中，以预测和训练包括悬停、抓取、点击、单峰和双峰在内的五种手势。
使用 SVM 和朴素贝叶斯分类器，总体而言，五种手势的准确率分别为 99.58%±0.06 和 98.74%±3.64。我们比较了整个数据集的手势准确性，并使用 SVM 和朴素贝叶斯分类器来检验个人基础训练的优势。
我们开发并增强了具有手势识别功能的非接触式界面，以增强手术室控制系统。
