Enhancing User Experience Via Calibration Minimization using ML Techniques

IF 0.7 Q3 COMPUTER SCIENCE, THEORY & METHODS
Sarah N. Abdulkader, Taha M. Mohamed
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引用次数: 0

Abstract

—Electromyogram (EMG) signals are used to recognize gestures that could be used for prosthetic-based and hands-free human computer interaction. Minimizing calibration times for users while preserving the accuracy, is one of the main challenges facing the practicality, user acceptance and spread of upper limb movements’ detection systems. This paper studies the effect of minimized user involvement, thus user calibration time and effort, on the user-independent system accuracy. It exploits time based features extracted from EMG signals. One-versus-all kernel based Support Vector Machine (SVM) and K Nearest Neighbors (KNN) are used for classification. The experiments are conducted using a dataset having five subjects performing six distinct movements. Two experiments performed, one with complete user dependence condition and the other with the partial dependence. The results show that the involvement of at least two samples, representing around 2% of sample space, increase the performance by 62.6% in case of SVM, achieving accuracy result equal to 89.6% on average; while the involvement of at least three samples, representing around 3% of sample space, cause the increase by 50.6% in case of KNN, achieving accuracy result equal to 78.2% on average. The results confirmed the great impact on system accuracy when involving only small number of user samples in the model-building process using traditional classification methods.
通过使用ML技术的校准最小化来增强用户体验
-肌电图(EMG)信号用于识别手势,这些手势可用于基于假肢和免提的人机交互。在保持精度的同时,最大限度地减少用户的校准时间,是上肢运动检测系统的实用性、用户接受度和推广面临的主要挑战之一。本文研究了用户参与最小化(即用户校准时间和精力)对用户独立系统精度的影响。它利用从肌电信号中提取的基于时间的特征。使用基于单对全核的支持向量机(SVM)和K近邻(KNN)进行分类。实验使用一个数据集进行,其中有五个受试者执行六种不同的动作。进行了完全用户依赖条件和部分用户依赖条件下的两个实验。结果表明,在支持向量机的情况下,至少两个样本的参与,占样本空间的2%左右,使SVM的性能提高62.6%,平均准确率达到89.6%;而在KNN情况下,至少三个样本的参与,约占样本空间的3%,使准确率提高了50.6%,平均达到78.2%。结果证实了传统分类方法在建模过程中只涉及少量用户样本对系统精度的影响很大。
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来源期刊
CiteScore
2.30
自引率
22.20%
发文量
519
期刊介绍: IJACSA is a scholarly computer science journal representing the best in research. Its mission is to provide an outlet for quality research to be publicised and published to a global audience. The journal aims to publish papers selected through rigorous double-blind peer review to ensure originality, timeliness, relevance, and readability. In sync with the Journal''s vision "to be a respected publication that publishes peer reviewed research articles, as well as review and survey papers contributed by International community of Authors", we have drawn reviewers and editors from Institutions and Universities across the globe. A double blind peer review process is conducted to ensure that we retain high standards. At IJACSA, we stand strong because we know that global challenges make way for new innovations, new ways and new talent. International Journal of Advanced Computer Science and Applications publishes carefully refereed research, review and survey papers which offer a significant contribution to the computer science literature, and which are of interest to a wide audience. Coverage extends to all main-stream branches of computer science and related applications
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