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Quantitative analysis of fall risk using TUG test.

Nor Aini Zakaria1, Yutaka Kuwae, Toshiyo Tamura

  • 1a Biomedical Imaging and Informatics Department, Nara Institute of Science and Technology , Nara , Japan.

Computer Methods in Biomechanics and Biomedical Engineering
|August 23, 2013
PubMed
Summary
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This study used wearable sensors during the Timed Up and Go (TUG) test to assess fall risk in older adults. Sensor data effectively distinguished between low and high fall risk groups, improving fall risk evaluation.

Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Rehabilitation Science

Background:

  • Falls are a significant concern for elderly individuals, leading to reduced mobility and quality of life.
  • Accurate fall risk assessment is crucial for implementing timely interventions.
  • Traditional methods for assessing fall risk may not capture the nuances of movement dynamics.

Purpose of the Study:

  • To evaluate the effectiveness of wearable inertial sensors in assessing fall risk in the elderly.
  • To identify key kinematic parameters from the Timed Up and Go (TUG) test that differentiate fall risk levels.
  • To propose an improved method for fall risk evaluation using sensor-based analysis.

Main Methods:

  • Subjects performed the Timed Up and Go (TUG) test while wearing a single inertial sensor (accelerometer and gyrosensor) at the waist.
Keywords:
Timed Up and Gofallsgaitwearable inertial sensor

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  • Data analysis focused on distinct phases of the TUG test: sit-bend, bend-stand, walking, turning, stand-bend, and bend-sit.
  • Subjects were categorized into low and high fall risk groups based on a 13.5-second TUG test duration threshold.

    • Main Results:

    • Time parameters, root mean square (RMS) values, and signal amplitudes varied significantly between low and high fall risk groups across different TUG phases.
    • Specific parameters, including RMS of angular velocity (sit-stand), RMS of acceleration (walking), and angular velocity amplitude (turning), were identified as key discriminators.
    • The combination of time parameters and sensor-derived metrics provided improved classification of fall risk.

    Conclusions:

    • Wearable inertial sensors provide valuable data for differentiating fall risk in the elderly during the TUG test.
    • Analysis of specific movement phases and associated sensor parameters offers a more refined approach to fall risk assessment.
    • This sensor-based method holds promise for enhancing the quality of life for older adults by enabling better fall prevention strategies.