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Development of a Wearable Controller for Gesture-Recognition-Based Applications Using Polyvinylidene Fluoride.

Kyle Van Volkinburg, Gregory Washington

IEEE Transactions on Biomedical Circuits and Systems

|June 3, 2017

Summary

This study presents a wearable controller using polyvinylidene fluoride (PVDF) that accurately detects right and left hand gestures in real time. The flexible device uses muscle movements to provide precise gesture recognition for enhanced human-computer interaction.

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Area of Science:

Wearable technology
Biomedical engineering
Materials science

Background:

Gesture-based control offers intuitive human-computer interaction.
Developing accurate and non-invasive gesture recognition systems is crucial for various applications.
Flexible piezoelectric polymers present promising materials for wearable sensing.

Purpose of the Study:

To develop and evaluate a wearable gesture-based controller using polyvinylidene fluoride (PVDF).
To achieve real-time, accurate differentiation between right and left hand gestures.
To investigate the efficacy of forearm muscle movement detection for gesture recognition.

Main Methods:

Fabrication of a wearable controller using flexible thin-film piezoelectric polyvinylidene fluoride (PVDF).
Affixing the PVDF sensor to a compression sleeve worn on the forearm.
Utilizing a microcontroller-based board for signal processing and an artificial neural network for gesture recognition.
Spatially shading (etching) the PVDF to enhance sensitivity to specific muscle deformations.

Main Results:

The PVDF-based controller accurately discerns between right and left hand gestures in real time.
The wearable device demonstrates flexibility, adaptability, and shape conformity.
The artificial neural network successfully recognized gesture patterns from PVDF voltage signals.
The device maintained high accuracy even when rotated or translated on the forearm.

Conclusions:

A novel wearable gesture controller utilizing PVDF has been successfully developed.
The PVDF sensor effectively translates forearm muscle movements into recognizable hand gestures.
The system offers a robust and accurate solution for real-time gesture recognition, adaptable to user movement.