首页|Investigators at Xi'an Jiaotong University Describe Findings in Robotics (Semg-based End-to-end Continues Prediction of Human Knee Joint Angles Using the Tightly Coupled Convolutional Transformer Model)
Investigators at Xi'an Jiaotong University Describe Findings in Robotics (Semg-based End-to-end Continues Prediction of Human Knee Joint Angles Using the Tightly Coupled Convolutional Transformer Model)
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2024 FEB 05 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Robotics are presented in a new report. According to news originating from Xi'an, People's Republic of China, by NewsRx correspondents, research stated, “Wearable exoskeleton robots can promote the rehabilitation of patients with physical dysfunction. And improving human-computer interaction performance is a significant challenge for exoskeleton robots.” Funders for this research include National Key Research and Development Program, Shaanxi Province Key Research and Development Program. Our news journalists obtained a quote from the research from Xi'an Jiaotong University, “The traditional feature extraction process based on surface Electromyography(sEMG) is complex and requires manual intervention, making real-time performance difficult to guarantee. In this study, we propose an end-to-end method to predict human knee joint angles based on sEMG signals using a tightly coupled convolutional transformer (TCCT) model. We first collected sEMG signals from 5 healthy subjects. Then, the envelope was extracted from the noise-removed sEMG signal and used as the input to the model. Finally, we developed the TCCT model to predict the knee joint angle after 100 ms. For the prediction performance, we used the Root Mean Square Error(RMSE), Pearson Correlation Coefficient(CC), and Adjustment R-2 as metrics to evaluate the error between the actual knee angle and the predicted knee angle. The results show that the model can predict the human knee angle quickly and accurately. The mean RMSE, Adjustment R-2, and (CC) values of the model are 3.79(degrees), 0.96, and 0.98, respectively, which are better than traditional deep learning models such as Informer (4.14, 0.95, 0.98), CNN (5.56, 0.89, 0.96) and CNN- BiLSTM (3.97, 0.95, 0.98). In addition, the prediction time of our proposed model is only 11.67 +/-0.67 ms, which is less than 100 ms.”
Xi’anPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRoboticsXi’an Jiaotong University
NETL
NSTL
IEEE
