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Fall Risk Assessment Using Hybrid Machine Learning and Deep Learning Approaches and a Novel Posturography

NCT05308563 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2022-04-04

No results posted yet for this study

Summary

The purpose of this project is to combine a novel posturogrpahy based on HTC VIVE trackers and hybrid machine learning and deep learning algorithms to establish a set of simple, convenient and valid fall risk assessment tool. This observational and follow up study will community elderly aged over 60 years old. The investigators will collect demographic data, questionnaire surveys, traditional balance tests and the tracker-based posturography to obtain the trunk stability parameters in different standing task. The fall risk will be classified according to self-reported falls n the past one year and verified in a 6-month follow up. The investigators will evaluate the performance of different hybrid machine learning and deep learning algorithm to extract the important features of multiple posturographic parameters and select an optimal model. The investigators will use the receiver operating characteristic curve analysis to compute the sensitivity, specificity and accuracy of different algorithms for risk classification and also compare the performance with traditional balance assessment tools.

Conditions

  • Age Problem
  • Fall

Sponsors & Collaborators

  • National Taiwan University Hospital, Yun-Lin Branch

    collaborator OTHER

  • National Yunlin University of Science and Technology

    collaborator UNKNOWN

  • National Taiwan University Hospital

    lead OTHER

Eligibility

Min Age
60 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-04-30
Primary Completion
2023-06-30
Completion
2023-12-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05308563 on ClinicalTrials.gov