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Computerized Dynamic Posturography for Postural Control Assessment in Patients with Intermittent Claudication
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Published on: December 11, 2013

Automatic recognition of postural allocations.

Edward Sazonov1, Vidya Krishnamurthy, Oleksandr Makeyev

  • 1Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13699, USA. esazonov@cias.clarkson.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
Summary
This summary is machine-generated.

This study introduces a shoe-based device for automatically recognizing posture, like standing or sitting. This technology aids in behavioral modification by tracking activity thermogenesis and minimizing sedentary time.

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Human Activity Recognition

Background:

  • Daily energy expenditure is significantly influenced by non-exercise activity thermogenesis (NEAT) and exercise activity thermogenesis (EAT).
  • Minimizing prolonged static postures (e.g., prolonged sitting) is crucial for health and metabolic regulation.
  • Automatic posture recognition offers potential for behavioral interventions to manage sedentary lifestyles.

Purpose of the Study:

  • To propose and validate a shoe-based device and pattern recognition methodology for automatic recognition of postural allocations (standing, sitting).
  • To assess the feasibility of integrating this technology into everyday footwear.
  • To evaluate the efficiency and reliability of the proposed system for monitoring posture.

Main Methods:

  • Development of a novel shoe-based sensing device incorporating inexpensive technology.
  • Implementation of a pattern recognition methodology for analyzing sensor data to classify postures.
  • Experimental validation of the system's performance in recognizing different postural states.

Main Results:

  • The proposed shoe-based device and methodology demonstrated high efficiency in recognizing postural allocations.
  • Experimental results confirmed the reliability of the system for distinguishing between postures like standing and sitting.
  • The use of inexpensive technology facilitates practical implementation within footwear.

Conclusions:

  • The developed shoe-based system offers a reliable and efficient method for automatic postural allocation recognition.
  • This technology has the potential to be integrated into footwear for unobtrusive, long-term monitoring of daily activities.
  • The findings support the use of this approach in behavioral modification programs targeting sedentary behavior and energy expenditure.