首页|New Findings in Machine Learning Described from Northeast Normal University (Prediction and Classification of Semg-based Pinch Force Between Different Fingers)
New Findings in Machine Learning Described from Northeast Normal University (Prediction and Classification of Semg-based Pinch Force Between Different Fingers)
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NETL
NSTL
Elsevier
New research on Machine Learning is the subject of a report. According to news reporting originating in Changchun, People’s Republic of China, by NewsRx journalists, research stated, “The movement of intelligent EMG-driven prosthesis mainly relies on the synergy of different fingers to achieve function of grasping objects. The paper proposes a novel scheme for force prediction and movement classification about pinch between different fingers based on surface electromyography (sEMG) using machine learning.” Financial support for this research came from Jilin Provincial Science and Technology Department. The news reporters obtained a quote from the research from Northeast Normal University, “The pinch force and sEMG signals are recorded synchronously by a data acquisition device. Eight features are extracted, which are proven to have better performance in the estimation of sEMG-to-force. We present a novel feature selection method that uses the one-dimensional time series similarity assessment based on Manhattan distance to eliminate the repetitive information between features. Three optimal features carrying less repetitive information are retained. Seven machine learning algorithms are used to predict 100 force strength. The results show that the Long Short Term Memory (LSTM) has the best performance of force prediction, achieving a R2 of 0.9517 and RMSE of 3.2723. The paper proposes a novel method of converting EMG feature sequences to the normalized gray image in order to classify the finger movement. Five classifiers based on image feature extraction and the Convolutional Neural Network (CNN) are developed respectively. The experimental results indicate that the CNN performs best, achieving an accuracy of 97.66%. In this way, it not only realizes the accurate force prediction, but also realizes the movement classification between different fingers.”
ChangchunPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningNortheast Normal University
NETL
NSTL
Elsevier
