  Regression-based sEMG prosthetic hands are widely used for their ability to
provide continuous kinematic parameters. However, establishing these models
traditionally requires complex kinematic sensor systems to collect
corresponding kinematic data in synchronization with EMG, which is cumbersome
and user-unfriendly. This paper presents a simplified approach utilizing only
two data points to depict kinematic parameters. Finger flexion is recorded as
1, extension as -1, and a near-linear model is employed to interpolate
intermediate values, offering a viable alternative for kinematic data. We
validated the approach with twenty participants through offline analysis and
online experiments. The offline analysis confirmed the model's capability to
fill in intermediate points and the online experiments demonstrated that
participants could control gestures, adjust force accurately. This study
significantly reduces the complexity of collecting dynamic parameters in
EMG-based regression prosthetics, thus enhancing usability for prosthetic
hands.
