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Design Example: Resistive Touchscreen01:14

Design Example: Resistive Touchscreen

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A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...
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Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks.

Daniel Konings1, Fakhrul Alam1, Nathaniel Faulkner1

  • 1Department of Mechanical & Electrical Engineering (MEE), School of Food & Advanced Technology (SF&AT), Massey University, Auckland 0632, New Zealand.

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Summary
This summary is machine-generated.

Capacitive sensing floors can identify individuals and their gender using walking patterns. Advanced deep learning models like Bi-directional Long Short-Term Memory (BLSTM) achieved 98.12% accuracy for subject recognition.

Keywords:
biometricscapacitive floorgender classificationhuman sensingmachine learningneural network

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Capacitive sensing floors offer unobtrusive individual localization.
  • Gait analysis from floor sensors is an emerging biometric identification method.

Purpose of the Study:

  • To investigate the potential of capacitive sensing floors for subject and gender recognition based on walking characteristics.
  • To develop and compare neural network-based machine learning algorithms for this task.

Main Methods:

  • Development of several neural network algorithms, including Deep Neural Networks (DNNs), Bi-directional Long Short-Term Memory (BLSTM), and Convolutional Neural Networks (CNNs).
  • Training and validation using a dataset of walking patterns from 23 subjects on a capacitive sensing floor.
  • Benchmarking against Support Vector Machine (SVM) for performance comparison.

Main Results:

  • A BLSTM network achieved the highest accuracy for identity recognition at 98.12%.
  • A CNN model demonstrated superior performance for gender recognition with 93.3% accuracy.
  • Most neural network models outperformed SVM in accuracy metrics for floor-based recognition tasks.

Conclusions:

  • Capacitive sensing floors, combined with advanced machine learning, can effectively recognize individuals and their gender based on gait.
  • Deep learning approaches, particularly BLSTM and CNNs, show significant promise for enhancing biometric identification systems using unobtrusive sensing floors.