Validating Wireless Gait Sensor for Elderly Fall Risk Classification

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

51 participants

Study Classification

INTERVENTIONAL

Study Start Date

2023-12-01

Study Completion Date

2024-04-30

Brief Summary

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The walking status of elderly patients over 65 years of age in the hospital will be verified through political analysis and objective fall risk assessment through wireless inertial sensors and diagnostic machine learning models, and based on the results, As investigators, providing a foundation for the objective evaluation of the risk of falling patients by nurses in general wards in the future.

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Detailed Description

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Currently, in the case of general clinical wards in Korea, the evaluator who assesses the risk of falling during the patient's hospitalization changes every time, and the evaluation of fall risk differs for the same patient depending on the subjectivity of the evaluator. Hence, evaluating falls requires assessing the patient's walking based on consistent criteria. Through walking analysis with a wireless small inertial sensor, there is an expectation that the incidence of fall risk will decrease. When analyzing walking to classify fall risk groups, quantitative evaluation should be applied for stride length, gait speed, step width, cadence, and gait cycle, but currently, fall assessments taking this into account are not properly conducted. Therefore, it is necessary to prepare and apply quantitative standards for fall evaluation through walking analysis through wireless small inertial sensors and data machine learning to classify the risk of falling in elderly hospitalized patients.

Conditions

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Elderly Person

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

The subject wears shoes equipped with sensors, and walks for 1 minute, repeating this three times. We plan to machine learn the correlation between walking data and BBS data. Since machine learning becomes more accurate as the number increases, the analysis group was set at 51 people.

Primary Study Purpose

OTHER

Blinding Strategy

NONE

Study Groups

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Gait group

1. Those aged 55 years or older
2. Those who can walk independently for at least 1 minute
3. Those who are not taking medications that affect the ability to maintain balance
4. Those who have not had any orthopedic problems such as lower limb fractures within the past 6 months

Group Type EXPERIMENTAL

Walking analysis sensor

Intervention Type DEVICE

Participant gait analysis with the inertial sensor

Interventions

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Walking analysis sensor

Participant gait analysis with the inertial sensor

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

1. a person over the age of 55
2. Persons who can walk independently for at least one minute
3. Those who do not take drugs that affect their ability to maintain balance
4. A person who does not have an orthopedic problem such as a fracture of the lower extremities within six months

Exclusion Criteria

1. Those who have difficulty understanding the gait analysis program or difficulty expressing symptoms
2. A person deemed unfit for this study by a rehabilitation specialist due to other conditions
3. A person who is unable to apply this walking analysis program due to serious cardiovascular diseases

Minimum Eligible Age

55 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No