A Continuous sEMG-Based Prosthetic Hand Control System Without Motion or Force Sensors

TL;DR

This paper introduces a simplified near-linear model for sEMG-based prosthetic hand control that eliminates the need for complex sensors, enabling accurate and continuous finger force and gesture prediction.

Contribution

The work proposes a novel near-linear relationship model (ResDD) for sEMG-force mapping that simplifies calibration and improves practical prosthetic control.

Findings

The model accurately predicts finger force and gestures from sEMG signals.

Offline experiments show high classification accuracy and model fit.

Online tests confirm effective real-time control and gesture tracking.

Abstract

Regressively-based surface electromyography (sEMG) prosthetics are widely used for their ability to continuously convert muscle activity into finger force and motion. However, they typically require additional kinematic or dynamic sensors, which increases complexity and limits practical application. To address this, this paper proposes a method based on the simplified near-linear relationship between sEMG and finger force, using the near-linear model ResDD proposed in this work. By applying the principle that a line can be determined by two points, we eliminate the need for complex sensor calibration. Specifically, by recording the sEMG during maximum finger flexion and extension, and assigning corresponding forces of 1 and -1, the ResDD model can fit the simplified relationship between sEMG signals and force, enabling continuous prediction and control of finger force and gestures. Offline experiments were conducted to evaluate the model's classification accuracy and its ability to learn sufficient information. It uses interpolation analysis to open up the internal structure of the trained model and checks whether the fitted curve of the model conforms to the nearly linear relationship between sEMG and force. Finally, online control and sine wave tracking experiments were carried out to further verify the practicality of the proposed method. The results show that the method effectively extracts meaningful information from sEMG and accurately decodes them. The near-linear model sufficiently reflects the expected relationship between sEMG and finger force. Fitting this simplified near-linear relationship is adequate to achieve continuous and smooth control of finger force and gestures, confirming the feasibility and effectiveness of the proposed approach.