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Related Concept Videos

Visual System01:26

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

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A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis.

Xiaofeng Han1, Diego Guffanti2, Alberto Brunete1

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Vision-based human gait analysis is advancing rapidly. Non-invasive depth cameras and deep learning algorithms like CNNs and LSTMs are increasingly used for better mobility and health insights.

Keywords:
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Area of Science:

  • Biomechanics
  • Clinical Research
  • Interdisciplinary Studies

Background:

  • Human gait analysis is crucial in biomechanics and clinical research.
  • Advancements in visual sensors and machine learning have driven progress in gait analysis systems.
  • This review focuses on vision-based systems, examining recent developments over the last five years.

Purpose of the Study:

  • To provide a comprehensive review of advancements in vision-based human gait analysis systems.
  • To emphasize the roles of vision sensors, machine learning algorithms, and technological innovations.
  • To identify trends and establish foundations for future gait analysis tool development.

Main Methods:

  • Systematic literature review using the PRISMA method.
  • Analysis of 72 selected research articles.
  • Detailed examination of sensor systems, algorithms, parameters, and methods.

Main Results:

  • Non-invasive depth cameras are emerging as a popular choice for gait analysis.
  • Deep learning algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, are increasingly utilized.
  • Key aspects reviewed include sensor types, algorithms, gait parameters, camera placement, and extraction techniques.

Conclusions:

  • The field is trending towards non-invasive depth sensing and advanced deep learning.
  • Future innovations aim for more effective, versatile, and user-friendly gait analysis tools.
  • Enhanced gait analysis holds significant potential to improve human mobility, health, and quality of life.