Clinical trial NCT05308563: Fall Risk Assessment Using Hybrid Machine Learning and Deep Learning Approaches and a Novel Posturography
Official title: Fall Risk Assessment Using Hybrid Machine Learning and Deep Learning Approaches and a Novel Posturography
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Age Problem
Fall
Keywords: fall risk
posturography
machine learning
fall efficacy
deep learning
Lead sponsor: National Taiwan University Hospital
Sponsor class: OTHER
Collaborators: name: National Taiwan University Hospital, Yun-Lin Branch
name: National Yunlin University of Science and Technology
First posted: 2022-04-04
Enrollment: 500
Sex: ALL
Minimum age (years): 60
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Community-living aged group

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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Number of fall events
timeFrame: 6 months
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
References:
citation: 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.