Elderly PersonNCT06398431
Validating Wireless Gait Sensor for Elderly Fall Risk Classification
A study testing Walking analysis sensor in people with Elderly Person, run by Pusan National University Yangsan Hospital at 1 site in South Korea.
Trial 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… Read more about this study
- Who is it for?
- Ages 55 years and older
- Conditions the study is for
- Elderly Person
- What is being tested?
- Walking analysis sensor
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 51 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Dec 2023to 30 Apr 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 55 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
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
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.
Read the 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.
- Conditions
- Elderly Person
- Being tested
- Walking analysis sensor
- Keywords
- Fall Risk Assessment, Walking analysis, Machine Running
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Walking analysis sensorDevice
Participant gait analysis with the inertial sensor
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: Device: Walking analysis sensor |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Patient gait data is collected continuously throughout the study period, enabling the ongoing measurement of falls risk.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 May 2024
- Study start1 Dec 2023
- Primary completion5 Jan 2024
- Study completion30 Apr 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Elderly PersonStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- 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.
- Masking
- None (open label)
- Enrolment
- 51 participants (actual)
Sponsor and investigators
- Lead sponsor
- Pusan National University Yangsan Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Pusan National University
- Responsible party
- Principal investigator, Sungchul Huh, Pusan National University Yangsan Hospital
- Investigators
- Sungchul Huh, PhD, Principal investigator, Pusan National University Yangsan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06398431
- Sponsor's protocol ID
- 11-2023-001
- Status last verified
- Jun 2025
- First submitted
- 21 Dec 2023
- Last updated
- 22 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT06398431
- Acronym / sponsor ID
- 11-2023-001
- Conditions
- Elderly Person
- Keywords
- Fall Risk Assessment, Walking analysis, Machine Running
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 51 participants (actual)
- Lead sponsor
- Pusan National University Yangsan HospitalUniversity, hospital or other
- Collaborators
- Pusan National University
- Study start
- 1 Dec 2023 (actual)
- Primary completion
- 5 Jan 2024 (actual)
- Study completion
- 30 Apr 2024 (actual)
- First posted
- 3 May 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- 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.
| Arm | Type | Receives |
|---|---|---|
| Gait group | Experimental | Device: Walking analysis sensor1. 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 |
Who may qualify
- Ages
- 55 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Walking analysis sensorParticipant gait analysis with the inertial sensor | Device | Gait group |
Primary outcome
Falls Risk Assessment Scale
Time frame: Patient gait data is collected continuously throughout the study period, enabling the ongoing measurement of falls risk.
A falls risk assessment scale measured through the analysis of patients' gait using wireless inertial sensors and a diagnostic machine learning model.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Jun 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Sungchul Huh, PhD principal investigator
Pusan National University Yangsan Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Walking analysis sensor in people with Elderly Person, run by Pusan National University Yangsan Hospital at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 22 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Falls Risk Assessment Scale (time frame: Patient gait data is collected continuously throughout the study period, enabling the ongoing measurement of falls risk.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Jun 2025.