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Toward More Robust Hand Gesture Recognition on EIT Data.

David P Leins¹, Christian Gibas², Rainer Brück²

¹Research Institute Cognitive Interaction Technology, Bielefeld University, Bielefeld, Germany.

Frontiers in Neurorobotics

|August 30, 2021

Summary

This summary is machine-generated.

This study enhances control for prosthetic hands using electrical impedance tomography (EIT) by developing deep learning models. New methods significantly improve cross-session gesture recognition accuracy for prosthetic hand control.

Keywords:

artificial intelligence data analysis deep learning electrical impedance tomography gesture recognition neural networks

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Area of Science:

Biomedical Engineering
Rehabilitation Technology
Machine Learning in Healthcare

Background:

Electrical impedance tomography (EIT) shows potential for monitoring muscle activity in prosthetic hand control.
Existing EIT-based gesture recognition methods lack generalization across users and sessions.
Significant inter-session and inter-user variability, along with signal drift, challenge EIT signal analysis.

Purpose of the Study:

To analyze an existing EIT dataset for multi-fingered hand prostheses.
To develop advanced machine learning architectures to overcome EIT signal variability.
To improve cross-session and cross-user classification accuracy for hand gesture recognition.

Main Methods:

Utilized t-SNE analysis to investigate EIT data variance.
Developed novel deep learning architectures to differentiate signal variations.
Implemented three calibration methods based on data analysis.

Main Results:

Deep learning architectures improved cross-session accuracy from 19.55% to 30.45%.
Calibration methods further boosted cross-session accuracy to 39.01%, 55.37%, and 56.34%.
The study identified and addressed key challenges in EIT signal processing for prosthetics.

Conclusions:

Deep learning and novel calibration techniques significantly enhance EIT-based prosthetic hand control.
The developed methods offer a pathway to more robust and natural prosthetic limb functionality.
Further research can leverage these findings for improved human-machine interfaces in prosthetics.