Grasping force prediction based on sEMG signals

Ma, Ruyi; Zhang, Leilei; Li, Gongfa; Jiang, Du; Xu, Shuang; Chen, Disi

doi:10.1016/j.aej.2020.01.007

All Outputs (3)

Grasping detection of dual manipulators based on Markov decision process with neural network (2023)
Journal Article

With the development of artificial intelligence, robots are widely used in various fields, grasping detection has been the focus of intelligent robot research. A dual manipulator grasping detection model based on Markov decision process is proposed t... Read More about Grasping detection of dual manipulators based on Markov decision process with neural network.

A novel curved gaussian mixture model and its application in motion skill encoding (2021)
Presentation / Conference Contribution

The purpose of this paper is to present a novel curved Gaussian Mixture Model (CGMM) and to study the application of it in motion skill encoding. Primarily, Gaussian mixture model (GMM) has been widely applied on many occasions when a probability den... Read More about A novel curved gaussian mixture model and its application in motion skill encoding.

Grasping force prediction based on sEMG signals (2020)
Journal Article

In order to realize the force control, when the prosthetic hand grasps the object, the forearm electromyography signal is collected by the multi-channel surface electromyography (sEMG) acquisition system. The grasping force information of the human h... Read More about Grasping force prediction based on sEMG signals.