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

Sponsor

National Taiwan University Hospital (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT05308563

Collaborator

National Taiwan University Hospital, Yun-Lin Branch (Other), National Yunlin University of Science and Technology (Other)

500

Enrollment

Anticipated Duration (Months)

Study Details

Study Description

Brief 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.

Condition or Disease	Intervention/Treatment	Phase
Age Problem Fall

Detailed Description

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. The investigators will evaluate the correlation of these posturographic features and data obtained by other methods. Risk factors of previous falls and future falls will also analyzed.

Study Design

Study Type:

Observational

Anticipated Enrollment :

500 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

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

Anticipated Study Start Date :

Apr 1, 2022

Anticipated Primary Completion Date :

Jun 1, 2023

Anticipated Study Completion Date :

Dec 1, 2023

Outcome Measures

Primary Outcome Measures

Number of fall events [6 months]
self-reported fall events according to a followup questionnaire and defined as the sudden, involuntary transfer of body to the ground and at a lower level than the previous one

Eligibility Criteria

Criteria

Ages Eligible for Study:

60 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Yes

Inclusion Criteria:

can walk in the household without device independently

Exclusion Criteria:

with terminal disease
with cognitive impairment to follow verbal instruction
with neurological conditions that are associated with leg weakness
with significant visual impairment that interferes with daily living and walking