Deciphering hand motion intention from surface electromyography(sEMG)encounters challenges posed by the requisites of multiple degrees of freedom(DOFs)and adaptability.Unlike discrete action classification grounded in...Deciphering hand motion intention from surface electromyography(sEMG)encounters challenges posed by the requisites of multiple degrees of freedom(DOFs)and adaptability.Unlike discrete action classification grounded in pattern recognition,the pursuit of continuous kinematics estimation is appreciated for its inherent naturalness and intuitiveness.However,prevailing estimation techniques contend with accuracy limitations and substantial computational demands.Kalman estimation technology,celebrated for its ease of implementation and real-time adaptability,finds extensive application across diverse domains.This study introduces a continuous Kalman estimation method,leveraging a system model with sEMG and joint angles as inputs and outputs.Facilitated by model parameter training methods,the approach deduces multiple DOF finger kinematics simultaneously.The method’s efficacy is validated using a publicly accessible database,yielding a correlation coefficient(CC)of 0.73.With over 45,000 windows for training Kalman model parameters,the average computation time remains under 0.01 s.This pilot study amplifies its potential for further exploration and application within the realm of continuous finger motion estimation technology.展开更多
基金supported in part by the National Key R&D Program of China(#2020YFC2007900)the National Natural Science Foundation of China(#82161160341,#62271477,and #61901464)+2 种基金the Science and Technology Program of Guangdong Province(#2022A0505090007)“The Belt and Road”Innovative Talent Exchange program for foreign experts(DL2022024002L)Jinan 5150 Program for Talents Introduction.
文摘Deciphering hand motion intention from surface electromyography(sEMG)encounters challenges posed by the requisites of multiple degrees of freedom(DOFs)and adaptability.Unlike discrete action classification grounded in pattern recognition,the pursuit of continuous kinematics estimation is appreciated for its inherent naturalness and intuitiveness.However,prevailing estimation techniques contend with accuracy limitations and substantial computational demands.Kalman estimation technology,celebrated for its ease of implementation and real-time adaptability,finds extensive application across diverse domains.This study introduces a continuous Kalman estimation method,leveraging a system model with sEMG and joint angles as inputs and outputs.Facilitated by model parameter training methods,the approach deduces multiple DOF finger kinematics simultaneously.The method’s efficacy is validated using a publicly accessible database,yielding a correlation coefficient(CC)of 0.73.With over 45,000 windows for training Kalman model parameters,the average computation time remains under 0.01 s.This pilot study amplifies its potential for further exploration and application within the realm of continuous finger motion estimation technology.
