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Related Concept Videos

Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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Related Experiment Video

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An Instrumented Pull Test to Characterize Postural Responses
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Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a

Tomas Mendoza1, Chia-Hsuan Lee2, Chien-Hua Huang3

  • 1Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan Tung Road, Chungli District, Taoyuan 320, Taiwan.

Sensors (Basel, Switzerland)
|September 10, 2021
PubMed
Summary

This study introduces a new method using inertial sensors and clinical tests to assess elderly fall risk. Combining multi-scale entropy and statistical features from sensor data improves faller classification.

Keywords:
community-dwelling elderlyfall riskfeaturesinertial sensormultiscale entropypermutation entropyrandom forestshort form berg balance scale (SFBBS)timed up and go (TUG)

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Area of Science:

  • Gerontology and Rehabilitation Science
  • Biomedical Engineering and Sensor Technology

Background:

  • Falls are a significant health concern for older adults globally.
  • Postural instability is a primary risk factor contributing to falls in the elderly population.
  • Current assessment methods often require substantial clinical intervention.

Purpose of the Study:

  • To propose a supplementary method for measuring postural stability with reduced doctor intervention.
  • To evaluate the effectiveness of inertial sensor-derived features in classifying fallers versus non-fallers.
  • To compare the screening capabilities of a multifactor clinical test against individual tests.

Main Methods:

  • Utilized clinical tests: Timed-Up and Go (TUG), Short Form Berg Balance Scale (SFBBS), and Short Portable Mental Status Questionnaire (SPMSQ).
  • Attached an inertial sensor to the lower back of elderly subjects during the TUG test to capture tri-axial acceleration.
  • Extracted features including Multi-Scale Entropy (MSE), Permutation Entropy (PE), and statistical features from sensor data.
  • Employed Random Forest for feature selection and classification of participants into fallers and non-fallers.

Main Results:

  • The combination of MSE and statistical features demonstrated the best classification performance.
  • Permutation Entropy (PE) was found to be an unimportant feature across all tested scenarios.
  • A t-test indicated that the multifactor test (TUG and BBS) served as a superior classifier compared to individual tests.

Conclusions:

  • A supplementary method using inertial sensors and clinical tests can effectively assess postural stability and fall risk in older adults.
  • Integrating Multi-Scale Entropy and statistical features from inertial sensor data enhances the accuracy of faller classification.
  • A multifactor clinical assessment approach, combining TUG and BBS, offers improved screening capabilities for fall risk.