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Related Experiment Video

Updated: Feb 8, 2026

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Published on: January 15, 2018

Decoding the grasping intention from electromyography during reaching motions.

Iason Batzianoulis¹, Nili E Krausz^2,3, Ann M Simon^2,3

¹Learning Algorithms and Systems Laboratory (LASA), School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, Lausanne, CH-1015, Switzerland. iason.batzianoulis@epfl.ch.

Journal of Neuroengineering and Rehabilitation

|June 27, 2018

Summary

This summary is machine-generated.

This study introduces an electromyography-based system to improve prosthetic hand control during dynamic movements. By decoding grasping intentions during reaching, it enhances prosthesis responsiveness and natural user interaction.

Keywords:

Myoelectric control Pattern recognition Reach-to-grasp motion Upper limb prosthesis

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Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Area of Science:

Biomedical Engineering
Rehabilitation Technology
Neuroprosthetics

Background:

Active upper-limb prostheses aim to restore grasping functions.
Conventional methods train classifiers on static gestures, leading to poor accuracy during dynamic motions like reach-to-grasp.
A novel electromyography-based approach is proposed to decode grasping intentions during the reaching phase.

Purpose of the Study:

To develop and evaluate an electromyography-based learning approach for decoding grasping intentions during the reach-to-grasp motion.
To improve the speed and naturalness of upper-limb prosthesis response.
To enhance the coordination between prosthetic control and user's arm movement.

Main Methods:

Electromyographic (EMG) activity and arm extension were recorded from eight able-bodied subjects and four individuals with transradial amputation performing five grasp types.
The reach-to-grasp motion was segmented into three phases based on arm extension.
Classification performance of Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Echo State Network (ESN) was evaluated off-line and on-line.

Main Results:

Multivariate analysis of variance (MANOVA) indicated significant differences in muscular activity across motion phases.
High classification performance (above 80%) was achieved for three grasp types before the motion's completion in off-line analysis.
On-line evaluation demonstrated that incorporating the reaching motion into classifier training significantly improved accuracy and enabled early grasp intention detection.

Conclusions:

The proposed electromyography-based method allows for more natural and intuitive control of prosthetic devices by integrating grasp closure with the reaching motion.
This approach reduces the delay between user intention and prosthesis response.
It enhances the coordination of the prosthetic device with the user's natural arm movements.