PDF(Pubmed)
Abstract：
UNASSIGNED: Hand gestures are an effective communication tool that may convey a wealth of information in a variety of sectors, including medical and education. E-learning has grown significantly in the last several years and is now an essential resource for many businesses. Still, there has not been much research conducted on the use of hand gestures in e-learning. Similar to this, gestures are frequently used by medical professionals to help with diagnosis and treatment.
UNASSIGNED: We aim to improve the way instructors, students, and medical professionals receive information by introducing a dynamic method for hand gesture monitoring and recognition. Six modules make up our approach: video-to-frame conversion, preprocessing for quality enhancement, hand skeleton mapping with single shot multibox detector (SSMD) tracking, hand detection using background modeling and convolutional neural network (CNN) bounding box technique, feature extraction using point-based and full-hand coverage techniques, and optimization using a population-based incremental learning algorithm. Next, a 1D CNN classifier is used to identify hand motions.
UNASSIGNED: After a lot of trial and error, we were able to obtain a hand tracking accuracy of 83.71% and 85.71% over the Indian Sign Language and WLASL datasets, respectively. Our findings show how well our method works to recognize hand motions.
UNASSIGNED: Teachers, students, and medical professionals can all efficiently transmit and comprehend information by utilizing our suggested system. The obtained accuracy rates highlight how our method might improve communication and make information exchange easier in various domains.
UNASSIGNED: We aim to improve the way instructors, students, and medical professionals receive information by introducing a dynamic method for hand gesture monitoring and recognition. Six modules make up our approach: video-to-frame conversion, preprocessing for quality enhancement, hand skeleton mapping with single shot multibox detector (SSMD) tracking, hand detection using background modeling and convolutional neural network (CNN) bounding box technique, feature extraction using point-based and full-hand coverage techniques, and optimization using a population-based incremental learning algorithm. Next, a 1D CNN classifier is used to identify hand motions.
UNASSIGNED: After a lot of trial and error, we were able to obtain a hand tracking accuracy of 83.71% and 85.71% over the Indian Sign Language and WLASL datasets, respectively. Our findings show how well our method works to recognize hand motions.
UNASSIGNED: Teachers, students, and medical professionals can all efficiently transmit and comprehend information by utilizing our suggested system. The obtained accuracy rates highlight how our method might improve communication and make information exchange easier in various domains.
摘要：
■手势是一种有效的沟通工具，可以在各个领域传达丰富的信息，包括医疗和教育。电子学习在过去几年中取得了显着增长，现在已成为许多企业的重要资源。尽管如此，关于在电子学习中使用手势的研究并不多。与此类似,医疗专业人员经常使用手势来帮助诊断和治疗。
■我们的目标是改进教师的方式，学生,和医疗专业人员通过引入动态的手势监测和识别方法来接收信息。六个模块组成了我们的方法：视频到帧转换，质量增强的预处理，手骨架映射与单发多盒检测器（SSMD）跟踪，使用背景建模和卷积神经网络(CNN)边界框技术的手检测，使用基于点的和全手覆盖技术的特征提取，并使用基于种群的增量学习算法进行优化。接下来,1DCNN分类器用于识别手部运动。
■经过大量的试验和错误，我们能够在印度手语和WLASL数据集上获得83.71％和85.71％的手跟踪精度，分别。我们的发现表明了我们的方法识别手部动作的效果。
■教师,学生,和医疗专业人员都可以通过利用我们建议的系统有效地传输和理解信息。获得的准确率凸显了我们的方法如何改善通信并使各个领域的信息交换更加容易。
■我们的目标是改进教师的方式，学生,和医疗专业人员通过引入动态的手势监测和识别方法来接收信息。六个模块组成了我们的方法：视频到帧转换，质量增强的预处理，手骨架映射与单发多盒检测器（SSMD）跟踪，使用背景建模和卷积神经网络(CNN)边界框技术的手检测，使用基于点的和全手覆盖技术的特征提取，并使用基于种群的增量学习算法进行优化。接下来,1DCNN分类器用于识别手部运动。
■经过大量的试验和错误，我们能够在印度手语和WLASL数据集上获得83.71％和85.71％的手跟踪精度，分别。我们的发现表明了我们的方法识别手部动作的效果。
■教师,学生,和医疗专业人员都可以通过利用我们建议的系统有效地传输和理解信息。获得的准确率凸显了我们的方法如何改善通信并使各个领域的信息交换更加容易。
