Technology Category
Intelligent Robots: Multimodal Perception & Sensor FusionMachine Learning: Calibration & Uncertainty QuantificationComputer Vision: Biometrics, Face, Gesture & Pose
Kinetic and Kinematic Sensors-free Approach for Estimation of Continuous Force and Gesture in sEMG Prosthetic Hands
🤖 AI Summary
Current sEMG-based prosthetic hand control systems rely on synchronized force/motion sensors, increasing system complexity and hindering clinical deployment. This paper proposes a sensor-free regression-based control framework: using only two calibration points—maximum flexion and extension effort (±1)—along with their corresponding sEMG signals, it constructs a lightweight, near-linear model to decode continuous finger force magnitude and direction, and enable real-time classification of three hand gestures. We introduce the novel “two-point calibration, zero-sensor calibration” paradigm, overcoming the bottleneck of multimodal synchronous sensing. Comparative evaluations across Dendritic Net, Linear Net, MLP, and CNN show Dendritic Net and Linear Net achieve superior offline performance in force-direction classification and intermediate-force interpolation. Online experiments confirm high accuracy, low-latency gesture transitions, and smooth continuous force modulation—significantly enhancing clinical practicality and user-friendliness.
Technology Category
Application Category
📝 Abstract
Regression-based sEMG prosthetic hands are widely used for their ability to provide continuous kinetic and kinematic parameters. However, establishing these models requires complex sensors systems to collect corresponding kinetic and kinematic data in synchronization with sEMG, which is cumbersome and user-unfriendly. This paper proposes a kinetic and kinematic sensors-free approach for controlling sEMG prosthetic hands, enabling continuous decoding and execution of three hand movements: individual finger flexion/extension, multiple finger flexion/extension, and fist opening/closing. This approach utilizes only two data points (-1 and 1), representing maximal finger flexion force label and extension force label respectively, and their corresponding sEMG data to establish a near-linear model based on sEMG data and labels. The model's output labels values are used to control the direction and magnitude of fingers forces, enabling the estimation of continuous gestures. To validate this approach, we conducted offline and online experiments using four models: Dendritic Net (DD), Linear Net (LN), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The offline analysis assessed each model's ability to classify finger force direction and interpolate intermediate force values, while online experiments evaluated real-time control performance in controlling gestures and accurately adjusting forces. Our results demonstrate that the DD and LN models provide excellent real-time control of finger forces and gestures, highlighting the practical potential of this sensors-free approach for prosthetic applications. This study significantly reduces the complexity of collecting kinetic and kinematic parameters in sEMG-based regression prosthetics, thus enhancing the usability and convenience of prosthetic hands.
Problem
Research questions and friction points this paper is trying to address.
Eliminating need for kinematic sensors in prosthetic control
Using simplified sEMG-force relationship for continuous prediction
Achieving smooth finger gesture control without complex calibration
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses simplified near-linear sEMG-force relationship
Eliminates need for kinematic or dynamic sensors
Employs ResDD model for continuous force prediction
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