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

NCT05308563 | Unknown

• 1 other identifier: 202112114RINA
• Study Type: observational
• Target: 500
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Age Problem; Fall

Keywords

fall risk; posturography; machine learning; fall efficacy; deep learning

Outcome Measures

Primary Outcomes (1)

• Number of fall events

  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

  6 months

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Community-living aged group

You may qualify if:

• can walk in the household without device independently

You may not qualify if:

• 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

Sponsors & Collaborators

• National Taiwan University Hospital: lead
• National Taiwan University Hospital, Yun-Lin Branch: collaborator
• National Yunlin University of Science and Technology: collaborator

Related Publications (1)

• Liang HW, Chi SY, Chen BY, Hwang YH. Reliability and Validity of a Virtual Reality-Based System for Evaluating Postural Stability. IEEE Trans Neural Syst Rehabil Eng. 2021;29:85-91. doi: 10.1109/TNSRE.2020.3034876. Epub 2021 Feb 25.

  PMID: 33125332 BACKGROUND

Biospecimen

Retention: NONE RETAINED

Trunk displacement trajectories recorded by the posturography

Central Study Contacts

Huey-Wen Liang

CONTACT

+886-02-23123456; lianghw@ntu.edu.tw

Jin-Sing Jen

CONTACT

+886-02-23123456; ntuhpmr.4124@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 24, 2022
• First Posted: April 4, 2022
• Study Start: April 1, 2022
• Primary Completion: June 1, 2023
• Study Completion: December 1, 2023
• Last Updated: April 4, 2022

Record last verified: 2022-03