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

Fall Clinical Trial

Official title:

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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05308563
• Other study ID #: 202112114RINA
• Status: Not yet recruiting
• Start date: April 2022
• Est. completion date: December 2023

Study information

• Verified date: March 2022
• Source: National Taiwan University Hospital
• Contact: Huey-Wen Liang
• Phone: +886-02-23123456
• Email: lianghw[@]ntu.edu.tw
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 500
• Est. completion date: December 2023
• Est. primary completion date: June 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 60 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Age Problem
• Fall

Locations

Country	Name	City	State
n/a

Sponsors (3)

Lead Sponsor	Collaborator
National Taiwan University Hospital	National Taiwan University Hospital, Yun-Lin Branch, National Yunlin University of Science and Technology

References & 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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