StrokeNCT06380049
Predicting Fall Risk in Stroke Patients Using a Machine Learning Model and Multi-Sensor Data
An observational study of Stroke and Fall, run by Seoul National University Hospital at 1 site in South Korea.
Trial summary
The study assesses a machine learning model developed to predict fall risk among stroke patients using multi-sensor signals. This prospective, multicenter, open-label, sponsor-initiated confirmatory trial aims to validate the safety and efficacy of the model which utilizes electromyography (EMG) signals to categorize… Read more about this study
- Who is it for?
- Ages 19 years and olderHealthy volunteers can take part
- What is being tested?
- EMG Analysis Software
- Type of treatment
- Medical device
- How many people
- 90 participants
- Where
- 1 siteSouth Korea
- Study dates
- 20 May 2024to 28 Apr 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 19 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study aims to enroll approximately 80 stroke patients and 10 healthy adults to facilitate a comprehensive analysis of the EMG-based machine learning model's effectiveness.
Who may be able to join
- 19 years and older
- the onset of the stroke is less than 3months ago
- Lower extremity weakness due to stroke (MMT =< 4 grade)
- Cognitive ability to follow commands
- 19 years and older
- Individuals who fully understand the necessity of the study and have voluntarily consented to participate as subjects
Who cannot join
- stroke recurrence
- other neurological abnormalities (e.g. parkinson's disease).
- severely impaired cognition
- serious and complex medical conditions(e.g. active cancer)
- cardiac pacemaker or other implanted electronic system
- Health Participants
Show all 10Show fewer
- other neurological abnormalities (e.g. parkinson's disease).
- severely impaired cognition
- serious and complex medical conditions(e.g. active cancer)
- cardiac pacemaker or other implanted electronic system
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
South Korea1 site, 1 recruiting
- Recruiting
Seoul National University Hospital
Seoul, Jongno, 03080
junghyun kim, Ph. D., 82+1021740890
About this study
Official title: Development and Validation of a Machine Learning-based Model to Predict a High-risk Group for Falls Using Multi-sensor Signals in Stroke Patients
The study assesses a machine learning model developed to predict fall risk among stroke patients using multi-sensor signals. This prospective, multicenter, open-label, sponsor-initiated confirmatory trial aims to validate the safety and efficacy of the model which utilizes electromyography (EMG) signals to categorize patients into high-risk or low-risk fall categories. The innovative approach hopes to offer a predictive tool that enhances preventative strategies in clinical settings, potentially reducing fall-related injuries in stroke survivors.
Read the detailed description
Objective: The primary objective is to develop and validate a machine learning-based model that uses multi-sensor (EMG) signals to identify stroke patients at high risk of falls. This model aims to improve on traditional fall risk assessments which rely heavily on physical assessments and patient history.
Study Design: This is a prospective, multicenter, open-label, confirmatory clinical trial. It involves collecting EMG data from stroke patients and applying machine learning techniques to predict fall risk. The study will compare the predictive accuracy of the machine learning model against conventional fall risk assessment tools.
Methods:
- Participants:
- Sample Size: 80 stroke patients and 10 healthy adults to establish baseline EMG readings.
- Interventions:
- Participants will undergo EMG signal collection from key lower limb muscles while performing standardized movements.
- Outcome Measures:
- Primary Outcome: Sensitivity and specificity of the machine learning model in predicting high-risk fall patients.
- Secondary Outcomes: Comparison of the machine learning model's predictive performance with traditional fall risk assessment tools (e.g., Berg Balance Scale).
Data Collection:
- EMG sensors will be attached to the patients' muscles of the lower limbs. Sensors will record muscle activity during movement, which will then be analyzed using the machine learning model.
- The predictive model will be trained using features extracted from the EMG signals, and its performance will be validated against actual fall incidents reported during the follow-up period.
Statistical Analysis:
- The machine learning model's efficacy will be measured through its sensitivity (ability to correctly identify high-risk patients) and specificity (ability to correctly identify low-risk patients).
- Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) statistics will be used to assess model performance.
- Being tested
- EMG Analysis Software
- Keywords
- Predict model, Machin leanning, Electromyography
- Medical subject headings
- Stroke
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
EMG Analysis SoftwareDevice
Surface electromyography devices are non-invasive tools that measure electrical activity produced by skeletal muscles through sensors placed on the skin.
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of the Machine Learning Model
The primary outcome measure is the sensitivity of the machine learning model, which refers to its ability to correctly identify patients who are at high risk of falls. Sensitivity is defined as the proportion of actual positives that are correctly identified.
Measured: At the time of the single visit
Secondary outcomes (1)
Specificity of the Machine Learning Model
Specificity measures the proportion of actual negatives that are correctly identified.
Measured: At the time of the single visit
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Apr 2024
- Study start20 May 2024
- Primary completion12 Mar 2025
- Study completion28 Apr 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for StrokeStudies of the same condition, open and under way.
- More trials run by Seoul National University HospitalThe sponsor's other registered studies.
- 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
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 90 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Seoul National University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Ministry of Trade, Industry & Energy, Republic of Korea
- Responsible party
- Sponsor
- Investigators
- Woo Hyung Lee, prof, Principal investigator, Seoul National University HospitalByung-Mo Oh, prof, Study director, Seoul National University HospitalHan Gil Seo, prof, Study director, Seoul National University HospitalSung Eun Hyun, prof, Study director, Seoul National University HospitalHyunmi Oh, prof, Study director, National Traffic Injury Rehabilitation HospitalSumin Oh, B.S., Study director, National Traffic Injury Rehabilitation HospitalSO YEON JEON, B.S., Study director, Seoul National University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06380049
- Sponsor's protocol ID
- 0720242110
- Other IDs
- 20240012366
- Status last verified
- May 2025
- First submitted
- 15 Apr 2024
- Last updated
- 2 Jun 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 2 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Machine Learning-based Model to Predict a High-risk Group for Falls Using Multi-sensor Signals in Stroke Patients
- ClinicalTrials.gov ID
- NCT06380049
- Acronym / sponsor ID
- 0720242110
- Keywords
- Predict model, Machin leanning, Electromyography
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 90 participants (estimated)
- Lead sponsor
- Seoul National University HospitalUniversity, hospital or other
- Collaborators
- Ministry of Trade, Industry & Energy, Republic of Korea
- Study start
- 20 May 2024 (actual)
- Primary completion
- 12 Mar 2025 (actual)
- Study completion
- 28 Apr 2026 (estimated)
- First posted
- 23 Apr 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 study assesses a machine learning model developed to predict fall risk among stroke patients using multi-sensor signals. This prospective, multicenter, open-label, sponsor-initiated confirmatory trial aims to validate the safety and efficacy of the model which utilizes electromyography (EMG) signals to categorize patients into high-risk or low-risk fall categories. The innovative approach hopes to offer a predictive tool that enhances preventative strategies in clinical settings, potentially reducing fall-related injuries in stroke survivors.
Detailed description
Objective: The primary objective is to develop and validate a machine learning-based model that uses multi-sensor (EMG) signals to identify stroke patients at high risk of falls. This model aims to improve on traditional fall risk assessments which rely heavily on physical assessments and patient history.
Study Design: This is a prospective, multicenter, open-label, confirmatory clinical trial. It involves collecting EMG data from stroke patients and applying machine learning techniques to predict fall risk. The study will compare the predictive accuracy of the machine learning model against conventional fall risk assessment tools.
Methods:
- Participants:
- Sample Size: 80 stroke patients and 10 healthy adults to establish baseline EMG readings.
- Interventions:
- Participants will undergo EMG signal collection from key lower limb muscles while performing standardized movements.
- Outcome Measures:
- Primary Outcome: Sensitivity and specificity of the machine learning model in predicting high-risk fall patients.
- Secondary Outcomes: Comparison of the machine learning model's predictive performance with traditional fall risk assessment tools (e.g., Berg Balance Scale).
Data Collection:
- EMG sensors will be attached to the patients' muscles of the lower limbs. Sensors will record muscle activity during movement, which will then be analyzed using the machine learning model.
- The predictive model will be trained using features extracted from the EMG signals, and its performance will be validated against actual fall incidents reported during the follow-up period.
Statistical Analysis:
- The machine learning model's efficacy will be measured through its sensitivity (ability to correctly identify high-risk patients) and specificity (ability to correctly identify low-risk patients).
- Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) statistics will be used to assess model performance.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 19 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study aims to enroll approximately 80 stroke patients and 10 healthy adults to facilitate a comprehensive analysis of the EMG-based machine learning model's effectiveness.
Key inclusion criteria (6 listed)
- 19 years and older
- the onset of the stroke is less than 3months ago
- Lower extremity weakness due to stroke (MMT =< 4 grade)
- Cognitive ability to follow commands
- 19 years and older
Key exclusion criteria (10 listed)
- stroke recurrence
- other neurological abnormalities (e.g. parkinson's disease).
- severely impaired cognition
- serious and complex medical conditions(e.g. active cancer)
- cardiac pacemaker or other implanted electronic system
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 |
|---|---|---|
| EMG Analysis SoftwareSurface electromyography devices are non-invasive tools that measure electrical activity produced by skeletal muscles through sensors placed on the skin. | Device | – |
Primary outcome
Sensitivity of the Machine Learning Model
Time frame: At the time of the single visit
The primary outcome measure is the sensitivity of the machine learning model, which refers to its ability to correctly identify patients who are at high risk of falls. Sensitivity is defined as the proportion of actual positives that are correctly identified.
1 secondary outcome
Specificity of the Machine Learning Model
Time frame: At the time of the single visit
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Jun 2025.
- Individual participant dataSharing planned: No
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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.
Woo Hyung Lee, prof principal investigator
Seoul National University Hospital
Byung-Mo Oh, prof study director
Seoul National University Hospital
Han Gil Seo, prof study director
Seoul National University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Stroke and Fall, run by Seoul National University 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 6 inclusion and 10 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: looking for participants now, as of the sponsor's last update on 2 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact JungHyun Kim, prof (82+1088632341, kiking0@naver.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of the Machine Learning Model (time frame: At the time of the single visit). 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 2 Jun 2025.