首页|Ho Chi Minh City University of Technology Reports Findings in Machine Learning ( Machine learning-enhanced gesture recognition through impedance signal analysis)
Ho Chi Minh City University of Technology Reports Findings in Machine Learning ( Machine learning-enhanced gesture recognition through impedance signal analysis)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Ho Chi Minh City, Viet nam, by NewsRx correspondents, research stated, "Gesture recognition is a crucia l aspect in the advancement of virtual reality, healthcare, and human-computer i nteraction, and requires innovative methodologies to meet the increasing demands for precision. This paper presents a novel approach that combines Impedance Sig nal Spectrum Analysis (ISSA) with machine learning to improve gesture recognitio n precision." Our news journalists obtained a quote from the research from the Ho Chi Minh Cit y University of Technology, "A diverse dataset that included participants from v arious demographic backgrounds (five individuals) who were each executing a rang e of predefined gestures. The predefined gestures were designed to encompass a b road spectrum of hand movements, including intricate and subtle variations, to c hallenge the robustness of the proposed methodology. The machine learning model using the K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Naive Baye s (NB), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms demonstrated notable precision in performance evaluations. The individual accuracy values for each algorithm are as follows: KNN, 86% ; GBM, 86%; NB, 84%; LR, 89%; RF, 87% ; and SVM, 87%. These results emphasize the importance of impedance features in the refinement of gesture recognition."
Ho Chi Minh CityVietnamAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
