Clinical trial NCT06398431: Validating Wireless Gait Sensor for Elderly Fall Risk Classification
Official title: A Study on Validation of Gait Analysis Wireless Small Inertial Sensor and Diagnostic Machine Learning Model for Classification of Elderly Fall Risk Group
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Elderly Person
Keywords: Fall Risk Assessment
Walking analysis
Machine Running
Lead sponsor: Pusan National University Yangsan Hospital
Sponsor class: OTHER
Collaborators: name: Pusan National University
First posted: 2024-05-03
Enrollment: 51
Sex: ALL
Minimum age (years): 55
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: 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 purpose: DEVICE_FEASIBILITY

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: 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

Primary outcomes:
measure: Falls Risk Assessment Scale
timeFrame: Patient gait data is collected continuously throughout the study period, enabling the ongoing measurement of falls risk.
Secondary outcomes:
Other outcomes:
Locations:
city: Yangsan
country: Korea, Republic of
facility: Sungchul Huh, MD
Study officials:
References: