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Pre-Impact Detection Algorithm to Identify Tripping Events Using Wearable Sensors.

Federica Aprigliano1, Silvestro Micera1,2, Vito Monaco3,4

  • 1The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.

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|August 30, 2019
PubMed
Summary
This summary is machine-generated.

This study presents an updated algorithm using Inertial Measurement Units (IMUs) to detect tripping-induced loss of balance. The system accurately identifies balance loss, enabling timely injury prevention for wearable applications.

Keywords:
lower-limb biomechanicspre-impact detectiontrippingwearable sensors

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Algorithm Development

Background:

  • Falls are a major cause of injury, particularly during daily activities.
  • Early detection of balance loss is crucial for fall prevention systems.
  • Inertial Measurement Units (IMUs) offer a promising approach for wearable motion analysis.

Purpose of the Study:

  • To evaluate an updated pre-impact detection algorithm for identifying loss of balance during tripping events.
  • To assess the algorithm's performance using data from IMUs placed on lower limbs.
  • To determine the algorithm's potential for integration into wearable injury prevention systems.

Main Methods:

  • Eight young subjects experienced induced tripping while walking on a treadmill.
  • An adaptive threshold-based algorithm processed elevation angle data from lower limb IMUs (thighs, shanks, feet).
  • The algorithm was tuned to detect abrupt kinematic changes indicative of tripping and balance loss.

Main Results:

  • The algorithm successfully identified loss of balance approximately 0.37 ± 0.11 seconds after tripping onset.
  • Detection accuracy was high, with a low false alarm rate (<10%).
  • Effective detection was achieved using data solely from the perturbed shank.

Conclusions:

  • The developed algorithm effectively detects tripping-induced balance loss using wearable IMU data.
  • Its multi-purpose nature allows for the identification of various perturbations like slippage and tripping.
  • The algorithm is suitable for implementation in smart garments and wearable robots for on-demand injury prevention.