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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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An Easily Customized Gesture Recognizer for Assisted Living Using Commodity Mobile Devices.

Antigoni Mezari1, Ilias Maglogiannis1

  • 1Department of Digital Systems, University of Piraeus, Piraeus, Greece.

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|August 21, 2018
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Summary
This summary is machine-generated.

This study explores using smartwatch accelerometers for automatic gesture recognition, a key area in human-computer interaction. The Symbolic Aggregate approXimation (SAX) method proved most efficient for recognizing natural gestures.

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Area of Science:

  • Human-Computer Interaction
  • Wearable Technology
  • Biometric Recognition

Background:

  • Traditional gesture recognition relies heavily on real-time video processing.
  • Smartwatches offer a novel platform for gesture recognition due to embedded sensors and wireless capabilities.

Purpose of the Study:

  • To propose and evaluate the use of commodity smartwatches for automatic gesture recognition.
  • To develop a gesture recognition system utilizing smartwatch accelerometer data processed on a mobile phone.

Main Methods:

  • Developed an Android application for the Pebble smartwatch.
  • Implemented and evaluated three distinct gesture recognition methodologies.
  • Utilized accelerometer sensor readings from smartwatches as input data.

Main Results:

  • The developed system allows users to define and train custom gestures.
  • Evaluated performance on six distinct 3-D natural gestures.
  • The Symbolic Aggregate approXimation (SAX) method demonstrated superior efficiency.

Conclusions:

  • Commodity smartwatches are viable devices for automatic gesture recognition.
  • The SAX-based approach offers an efficient solution for smartwatch-based gesture recognition.
  • This research opens new avenues for intuitive human-computer interaction using wearable sensors.