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The Automated Assessment of Postural Stability: Balance Detection Algorithm.

Alessandro Napoli1, Stephen M Glass2, Carole Tucker3,2

  • 1Department of Electrical & Computer Engineering, Temple University, Philadelphia, PA, 19122, USA. a.napoli@temple.edu.

Annals of Biomedical Engineering
|September 1, 2017
PubMed
Summary
This summary is machine-generated.

An automated system for postural stability assessment (AAPS) accurately evaluates balance deficits after concussion, comparable to human experts. This technology offers reliable on-field evaluations for mild traumatic brain injury and musculoskeletal injuries.

Keywords:
Automated BEESAutomatic balance error scoring detectionConcussion detectionField-expedient balance testKinectMild traumatic brain injuryOn-field automatic balance detectionReturn-to-duty evaluation

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Area of Science:

  • Biomedical Engineering
  • Neurology
  • Sports Medicine

Background:

  • Impaired balance is a key indicator of mild traumatic brain injury (TBI), concussion, and musculoskeletal injuries.
  • Accurate on-field evaluation tools are crucial, particularly in military settings.
  • Current methods for assessing balance deficits can be subjective and lack precision.

Purpose of the Study:

  • To design and implement an automated assessment of postural stability (AAPS) system for on-field concussion evaluations.
  • To develop a balance error detection algorithm using a Microsoft Kinect sensor to replicate the Balance Error Scoring System (BESS) test.
  • To compare the accuracy and reliability of the AAPS system against experienced human observers.

Main Methods:

  • Developed the AAPS, a computer system using off-the-shelf components and custom software.
  • Implemented a balance error detection algorithm to process Kinect sensor data into BESS scores.
  • Recruited 15 healthy subjects to perform the BESS test while tracked by Kinect and a motion capture system.
  • Scored BESS trials using both the AAPS algorithm and three experienced observers for comparison.

Main Results:

  • The AAPS algorithm demonstrated high agreement with human scores, ranging from 87.9% to 99.8%.
  • No statistically significant differences were found between AAPS and human scores using ANOVA (alpha=0.05).
  • The AAPS system accurately and precisely detected balance deficits, performing comparably to experienced medical personnel.

Conclusions:

  • The AAPS system provides an accurate, reliable, and automated method for evaluating balance deficits following concussion.
  • Automated BESS calculation offers advantages over human scoring, including high temporal resolution and objective data recording.
  • The AAPS system is suitable for rapid deployment in various settings, enhancing on-field injury assessment capabilities.