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Updated: Jun 18, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Prosthetic hand control using motion discrimination from EMG signals.

Naoyuki Kurisu¹, Nobutaka Tsujiuchi, Takayuki Koizumi

¹Mechanical Engineering Department, Doshisha University, Kyotanabe, Kyoto 610-0321, Japan. dtj0335@mail4.doshisha.ac.jp

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 8, 2009

Summary

This study enhances prosthetic hand control by improving electromyogram (EMG) motion discrimination using quadratic polynomials and multiple regression for angular velocity. The new method accurately identifies five hand motions, enabling precise prosthetic control.

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Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

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Published on: January 7, 2019

Area of Science:

Biomedical Engineering
Rehabilitation Engineering
Robotics

Background:

Electromyogram (EMG) based prosthetic hand control is challenging due to motion discrimination inaccuracies.
Previous conic model methods distinguished three hand motions but were susceptible to elbow motion interference.
Existing prosthetic hands often lack controllable motion speed, limiting user dexterity.

Purpose of the Study:

To enhance EMG-based motion discrimination accuracy for prosthetic hands.
To develop a controllable angular velocity generation method for prosthetic hand movements.
To improve the overall functionality and user control of prosthetic hands.

Main Methods:

Implemented a novel feature extraction method utilizing quadratic polynomials for EMG signal processing.
Developed an angular velocity generation system employing multiple regression models.
Validated the proposed methods through real-time control of a 3D prosthetic hand model.

Main Results:

Achieved over 90% accuracy in discriminating five distinct hand motions.
Eliminated incorrect discriminations caused by confounding elbow motions.
Enabled precise wrist joint angle control with a standard deviation of 3 degrees or less.

Conclusions:

The proposed quadratic polynomial feature extraction and multiple regression angular velocity methods significantly improve EMG-based prosthetic hand control.
This advancement offers more accurate and natural hand motion discrimination and control.
The developed system demonstrates potential for enhanced prosthetic limb functionality and user experience.