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Related Concept Videos

Anatomical Positions01:11

Anatomical Positions

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In anatomy, several standard anatomical positions are used as references for describing the position and orientation of different body parts. These positions help provide a common frame of reference when discussing anatomical structures. The anatomical position is the standard reference point for describing the body's position and orientation. In this position:
The body is upright, facing forward, and standing erect.
The feet are parallel and flat on the floor.
The arms are hanging by the...
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Related Experiment Video

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Computerized Dynamic Posturography for Postural Control Assessment in Patients with Intermittent Claudication
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Three-dimensional, clinically rated posture data from people aged 10 to 69 years.

Carlo Dindorf1, Oliver Ludwig1, Michael Fröhlich1

  • 1Department of Sport Science, University of Kaiserslautern-Landau (RPTU), 67663 Kaiserslautern, Germany.

Data in Brief
|July 31, 2024
PubMed
Summary

This study provides posture data for 1,149 individuals, detailing spinal parameters and expert classifications of hyperkyphosis and hyperlordosis. This dataset supports developing objective posture assessment tools and machine learning applications for public health.

Keywords:
Flèche cervicaleFlèche lombaireKyphosisLordosisMachine learningNormative dataPosture deviationReference values

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Area of Science:

  • Biomedical Engineering
  • Public Health
  • Data Science

Background:

  • Weak posture is a prevalent issue across all age groups, contributing to back pain and imposing a significant socio-economic burden.
  • Early detection of postural deficiencies through posture assessment is crucial for proactive interventions and public health promotion.
  • Existing methods for posture assessment may lack objectivity, highlighting the need for data-driven approaches.

Purpose of the Study:

  • To provide a comprehensive dataset of spinal posture parameters for a diverse age range (10-69 years).
  • To enable the development of objective, data-driven methods for posture assessment.
  • To facilitate the creation of machine learning applications for diagnostic support in identifying spinal conditions.

Main Methods:

  • Stereophotogrammetry was used to measure posture in 1,149 subjects.
  • Raw and normalized sagittal posture parameters (flèche cervicale, flèche lombaire, kyphosis index) were calculated.
  • Biomedical experts classified spinal conditions (hyperkyphosis, hyperlordosis), with algorithmic quality checks and expert reassessment for label refinement.

Main Results:

  • A dataset including anthropometrics, raw and normalized posture parameters (FC, FL, KI, FC%, FL%, KI%), and expert-validated classifications of hyperkyphosis and hyperlordosis is presented.
  • The data encompasses measurements from 1,149 individuals aged 10 to 69 years.
  • Refined labels, resulting from algorithmic quality checks and expert reassessment, are provided alongside original expert ratings.

Conclusions:

  • The presented posture data is valuable for developing objective posture assessment techniques.
  • The dataset has significant potential for the creation of machine learning models to support the diagnosis of postural abnormalities.
  • This work contributes to advancing public health by enabling more accurate and objective methods for evaluating spinal health.