首页|Data from Cracow University of Technology Provide New Insights into Machine Lear ning (Machine Learning Methods Used in the Automatic System for Teaching Human M otions-Key Aspects of CNN, HMM, and Minimum Distance Algorithms)
Data from Cracow University of Technology Provide New Insights into Machine Lear ning (Machine Learning Methods Used in the Automatic System for Teaching Human M otions-Key Aspects of CNN, HMM, and Minimum Distance Algorithms)
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-Researchers detail new data in artificial intelli gence. According to news originating from Cracow University of Technology by New sRx editors, the research stated, "The article describes problems related to the construction of an automatic system for teaching human motor activities. Teachi ng these activities in rehabilitation, sports, and professional work is of great importance in both social and individual dimensions." The news editors obtained a quote from the research from Cracow University of Te chnology: "The prospect of using automated systems is therefore highly significa nt. The system can use signals from any motion sensors, e.g., cameras or MEMS (M icro-Electro-Mechanical Systems) inertial sensors. A significant problem is real -time signal analysis. In the system presented, this analysis involves a classif ication process. It enables the selection of an optimal motor learning algorithm for a given situation. The learner is provided with information about required movement corrections through haptic devices. The primary aim of the research des cribed in the article is to identify key features of classification methods that ensure the construction of an effective teaching system. To achieve this goal, three classification methods were statistically tested, namely: a method using C NN (Convolutional Neural Network), a minimum distance method, and a method based on hidden Markov models. The main result of the study is the statement that the key feature of the methods is their interpretability. This property enables the efficient transfer of knowledge from experts to the system and facilitates its improvement."
Cracow University of TechnologyAlgorit hmsCyborgsDistance AlgorithmsEmerging TechnologiesMachine Learning
NETL
NSTL
