Table3_Innovative healthcare solutions: robust hand gesture recognition of daily life routines using 1D CNN.docx

IntroductionHand 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.
MethodWe 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.
ResultsAfter 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.
DiscussionTeachers, 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.

引言：手势是一种高效的沟通媒介，可在医疗、教育等诸多领域传递丰富信息。在线学习（E-learning）近年得到长足发展，现已成为众多机构的核心资源，但目前针对在线学习场景下手势应用的研究仍较为匮乏。与之类似，医疗从业者也常借助手势辅助诊断与治疗工作。
研究方法：本研究旨在优化教育者、学习者与医疗从业者的信息接收方式，提出一种用于手部手势监测与识别的动态方案。该方案共包含六大模块：视频转帧处理、画质增强预处理、基于单发多框检测器（Single Shot MultiBox Detector, SSMD）跟踪的手部骨骼映射、采用背景建模与卷积神经网络（Convolutional Neural Network, CNN）边界框技术的手部检测、基于点特征与全手覆盖技术的特征提取，以及基于种群增量学习算法的参数优化。最终通过一维卷积神经网络（1D CNN）分类器完成手部动作识别。
实验结果：经过大量反复试验，本方案在印度手语（Indian Sign Language）与WLASL数据集上的手部跟踪准确率分别达到83.71%与85.71%。实验结果证实，本方案在手部动作识别任务中具备优异性能。
讨论：借助本研究所提出的系统，教育者、学习者与医疗从业者均可高效完成信息的传递与理解。本次实验得到的准确率验证了本方案可有效优化多领域内的信息沟通与交互效率。