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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Fall; Age Problem

Detailed Description

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 (Berg Balance scale, Timed-up-and-go, 30s-sit-to-stand, four-stage balance tests) and a tracker-based posturography to obtain the trunk stability parameters in different standing task. The fall risk will be classified according to self-reported falls in 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. 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 Type: Observational
• Enrollment (Anticipated): 500

Contacts and Locations

• Study Contact: Name: Huey-Wen Liang; Phone Number: 66697 +886-02-23123456; Email: lianghw@ntu.edu.tw
• Study Contact Backup: Name: Jin-Sing Jen; Phone Number: 67752 +886-02-23123456; Email: ntuhpmr.4124@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 60 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Community-living aged group

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: National Taiwan University Hospital
• Collaborators: National Taiwan University Hospital, Yun-Lin Branch; National Yunlin University of Science and Technology

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start (Anticipated): April 1, 2022
• Primary Completion (Anticipated): June 1, 2023
• Study Completion (Anticipated): December 1, 2023

Study Registration Dates

• First Submitted: March 24, 2022
• First Submitted That Met QC Criteria: April 1, 2022
• First Posted (Actual): April 4, 2022

Study Record Updates

• Last Update Posted (Actual): April 4, 2022
• Last Update Submitted That Met QC Criteria: April 1, 2022
• Last Verified: March 1, 2022

More Information

Terms related to this study

• Keywords: machine learning; deep learning; fall risk; posturography; fall efficacy
• Other Study ID Numbers: 202112114RINA

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No