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Related Experiment Video

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Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
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Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model.

Yukihiko Aoyagi1, Shigeki Yamada2,3,4,5, Shigeo Ueda6

  • 1Digital Standard Co., Ltd., Osaka 536-0013, Japan.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

A new smartphone app uses deep learning for markerless, real-time human motion tracking to quantitatively assess pathological gait. This technology enables easy, 3D joint angle estimation for gait analysis without specialized equipment.

Keywords:
deep learningmarkerless motion capturemotion trackingquantitative gait assessmentsmartphone device

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Human Motion Analysis

Background:

  • Pathological gait assessment traditionally requires specialized equipment and controlled environments.
  • Accurate, quantitative analysis of human motion is crucial for diagnosing and monitoring various medical conditions.

Purpose of the Study:

  • To develop a novel smartphone application for real-time, markerless, full-body human motion tracking.
  • To enable quantitative assessment of pathological gait using accessible technology.

Main Methods:

  • Utilized deep learning (modified ResNet34) with a large 3D and 2D dataset for training.
  • Developed an iOS application (Three-Dimensional Pose Tracker for Gait Test - TDPT-GT) using a smartphone monocular camera.
  • Extracted 3D coordinates of 24 key body points and estimated 3D joint angles.

Main Results:

  • Achieved real-time, markerless motion tracking of 24 whole-body key points.
  • Successfully estimated 3D angles of major joints (neck, lumbar, hip, knee, ankle).
  • Demonstrated the capability for quantitative and easy assessment of human motion.

Conclusions:

  • The TDPT-GT application provides a novel, accessible tool for quantitative gait analysis.
  • Markerless, smartphone-based motion tracking can effectively assess pathological gait.
  • This technology has the potential to simplify clinical gait assessments.