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Synthetic 3D full-body skeletal motion from 2D paths using RNN with LSTM cells and linear networks.

David Carneros-Prado1, Cosmin C Dobrescu1, Luis Cabañero1

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Summary

Researchers developed a system to generate synthetic gait data using artificial intelligence, addressing the high cost and time of data collection for gait analysis. This method creates realistic human skeleton movements for rehabilitation and disease diagnosis.

Keywords:
Data augmentationGait analysisKinematic synthetic dataMotion trackingRecurrent neural networkWearable sensors

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Area of Science:

  • Biomechanics
  • Artificial Intelligence
  • Data Science

Background:

  • Gait analysis is crucial for functional assessment in fields like disease diagnosis and rehabilitation.
  • Acquiring comprehensive gait data can be expensive and time-consuming.
  • Augmenting datasets with artificial data is a common solution to data acquisition challenges.

Purpose of the Study:

  • To propose a novel, parametrizable generative system for synthetic walking human skeleton data.
  • To address the limitations of traditional gait data collection methods.

Main Methods:

  • Conducted a data gathering experiment with 26 individuals.
  • Developed a system using two artificial neural networks: a recurrent neural network for movement generation and a multilayer perceptron for skeleton segment sizing.
  • Evaluated the system through observational appraisal, visual data distribution representation, numerical analysis (normalized cross-correlation coefficient), and kinematic angular evaluation.

Main Results:

  • The system successfully generated realistic and accurate synthetic gait data.
  • The generated data demonstrated kinematic validity.
  • The evaluation methods confirmed the system's effectiveness.

Conclusions:

  • The proposed system offers a promising approach for generating synthetic gait data.
  • This technology can significantly aid in gait analysis for various applications.
  • Future improvements include increasing movement variety and user sample size.