Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in cyborg and bionic systems. According to news originating from Shenzhen, People's Republic of China, by NewsRx correspondents, research stated, "Deciphering hand motion intention from surface electromyography (sEMG) encounters challenges pos ed by the requisites of multiple degrees of freedom (DOFs) and adaptability." Financial supporters for this research include National Natural Science Foundati on of China Under Grant; Guangdong Science And Technology Department. The news correspondents obtained a quote from the research from Chinese Academy of Sciences: "Unlike discrete action classification grounded in pattern recognit ion, the pursuit of continuous kinematics estimation is appreciated for its inhe rent naturalness and intuitiveness. However, prevailing estimation techniques co ntend with accuracy limitations and substantial computational demands. Kalman es timation technology, celebrated for its ease of implementation and real-time ada ptability, finds extensive application across diverse domains. This study introd uces 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."
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NSTL