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Older Adult Fall Risk Prediction with Deep Learning and Timed Up and Go (TUG) Test Data.

Josu Maiora1,2, Chloe Rezola-Pardo3, Guillermo García4

  • 1Electronic Technology Department, Faculty of Engineering of Gipuzkoa, University of the Basque Country, 20018 San Sebastian, Spain.

Bioengineering (Basel, Switzerland)
|October 25, 2024
PubMed
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This summary is machine-generated.

Predicting fall risk in older adults is crucial. This study shows that data from wearable sensors during the Timed Up and Go (TUG) test, analyzed with deep learning, can accurately estimate future fall probability.

Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Data Science

Background:

  • Falls pose a significant health risk to the aging population, necessitating effective prediction tools.
  • Current fall risk assessments often involve clinical evaluations and functional mobility tests like the Timed Up and Go (TUG) test.
  • Wearable Inertial Measurement Units (IMUs) offer a novel approach to capture motion data for fall risk estimation.

Purpose of the Study:

  • To investigate the efficacy of using IMU data from the TUG test to predict prospective fall risk in older adults.
  • To develop and validate deep learning models for estimating the probability of falls in the near future.

Main Methods:

  • A cohort of 106 older adults wore wireless IMU sensors during TUG tests.
  • Deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), were applied to extracted IMU features.
Keywords:
deep learningfall predictionfall risk assessmentinertial sensorsmachine learning

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  • Supervised learning was used with a binary outcome variable indicating falls within a six-month follow-up period.

    • Main Results:

    • A Bidirectional Long Short-Term Memory (BLSTM) model achieved the best performance.
    • The BLSTM model demonstrated an accuracy of 0.83 and an Area Under the Curve (AUC) of 0.73.
    • The model also exhibited good sensitivity and specificity in predicting fall risk.

    Conclusions:

    • IMU data collected during the TUG test, when analyzed with deep learning, can effectively predict prospective fall risk in older adults.
    • This approach offers a promising, objective method for identifying individuals at high risk of falls.
    • The findings support the integration of wearable sensor technology and AI in geriatric fall prevention strategies.