Validating Wireless Gait Sensor for Elderly Fall Risk Classification

NCT06398431 | Completed

• 1 other identifier: 11-2023-001
• Study Type: interventional
• Target: 51
• Locations: 1 country
• Sites: 1

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.

Conditions

Elderly Person

Keywords

Fall Risk Assessment; Walking analysis; Machine Running

Outcome Measures

Primary Outcomes (1)

• Falls Risk Assessment Scale

  A falls risk assessment scale measured through the analysis of patients' gait using wireless inertial sensors and a diagnostic machine learning model.

  Patient gait data is collected continuously throughout the study period, enabling the ongoing measurement of falls risk.

Study Arms (1)

Gait group

EXPERIMENTAL

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

Device: Walking analysis sensor

Interventions

Walking analysis sensor DEVICE

Participant gait analysis with the inertial sensor

Gait group

Eligibility Criteria

• Age: 55 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Pusan National University Yangsan Hospital: lead
• Pusan National University: collaborator

Study Sites (1)

Sungchul Huh, MD

Yangsan, South Korea

Location

Study Officials

• Sungchul Huh, PhD

  Pusan National University Yangsan Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: OTHER
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Model Details: 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.

Study Record Dates

• First Submitted: December 21, 2023
• First Posted: May 3, 2024
• Study Start: December 1, 2023
• Primary Completion: January 5, 2024
• Study Completion: April 30, 2024
• Last Updated: June 22, 2025

Record last verified: 2025-06

Locations

KR (1)