Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography

TL;DR

This paper presents a new method using Kalman estimation to track finger movements from muscle signals, offering a natural and efficient way to estimate hand motion.

Contribution

The novel approach introduces a continuous Kalman estimation method for simultaneous multi-DOF finger kinematics tracking from sEMG.

Findings

The method achieved a correlation coefficient of 0.73 using a public database for validation.

Computation time averaged under 0.01 seconds with over 45,000 training windows.

The approach demonstrates potential for real-time and adaptive finger motion estimation.

Abstract

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.

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