[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100545295":3},{"organization":4,"armGroups":7,"interventions":8,"overallOfficials":13,"centralContacts":31,"locations":37,"responsibleParty":54,"collaborators":56,"id":60,"slug":61,"hasResults":62,"nctId":63,"briefTitle":64,"officialTitle":65,"acronym":7,"eligibilityCriteria":66,"healthyVolunteers":67,"sex":68,"minAge":69,"maxAge":7,"enrollmentInfo":70,"targetDuration":7,"studyType":73,"phases":7,"briefSummary":74,"conditions":75,"keywords":78,"overallStatus":39,"whyStopped":7,"lastUpdateSubmitDate":82,"lastUpdatePostDateStruct":83,"startDateStruct":86,"completionDateStruct":88,"leadSponsor":90,"locationsCount":91},{"fullName":5,"class":6},"Seoul National University Hospital","OTHER",null,[9],{"type":10,"name":11,"description":12,"armGroupLabels":7,"otherNames":7},"DEVICE","EMG Analysis Software","Surface electromyography devices are non-invasive tools that measure electrical activity produced by skeletal muscles through sensors placed on the skin.",[14,17,20,22,24,27,29],{"name":15,"affiliation":5,"role":16},"Woo Hyung Lee, prof","PRINCIPAL_INVESTIGATOR",{"name":18,"affiliation":5,"role":19},"Byung-Mo Oh, prof","STUDY_DIRECTOR",{"name":21,"affiliation":5,"role":19},"Han Gil Seo, prof",{"name":23,"affiliation":5,"role":19},"Sung Eun Hyun, prof",{"name":25,"affiliation":26,"role":19},"Hyunmi Oh, prof","National Traffic Injury Rehabilitation Hospital",{"name":28,"affiliation":26,"role":19},"Sumin Oh, B.S.",{"name":30,"affiliation":5,"role":19},"SO YEON JEON, B.S.",[32],{"name":33,"role":34,"phone":35,"phoneExt":7,"email":36},"JungHyun Kim, prof","CONTACT","82+1088632341","kiking0@naver.com",[38],{"facility":5,"status":39,"city":40,"state":41,"zip":42,"country":43,"countryCode":7,"cosmosGeoPoint":44,"geoPoint":49,"contacts":50},"RECRUITING","Seoul","Jongno","03080","South Korea",{"type":45,"coordinates":46},"Point",[47,48],126.9784,37.566,{"lat":48,"lon":47},[51],{"name":52,"role":34,"phone":53,"phoneExt":7,"email":7},"junghyun kim, Ph. D.","82+1021740890",{"type":55,"investigatorFullName":7,"investigatorTitle":7,"investigatorAffiliation":7,"oldNameTitle":7,"oldOrganization":7},"SPONSOR",[57],{"name":58,"class":59},"Ministry of Trade, Industry & Energy, Republic of Korea","OTHER_GOV","100545295","predicting-fall-risk-in-stroke-patients-using-a-machine-learning-model-and-multi-sensor-data-100545295",false,"NCT06380049","Predicting Fall Risk in Stroke Patients Using a Machine Learning Model and Multi-Sensor Data","Development and Validation of a Machine Learning-based Model to Predict a High-risk Group for Falls Using Multi-sensor Signals in Stroke Patients","Stroke Participants\n\nInclusion Criteria:\n\n* 19 years and older\n* the onset of the stroke is less than 3months ago\n* Lower extremity weakness due to stroke (MMT =\\\u003C 4 grade)\n* Cognitive ability to follow commands\n\nExclusion Criteria:\n\n* stroke recurrence\n* other neurological abnormalities (e.g. parkinson's disease).\n* severely impaired cognition\n* serious and complex medical conditions(e.g. active cancer)\n* cardiac pacemaker or other implanted electronic system\n\nHealth Participants\n\nInclusion Criteria:\n\n* 19 years and older\n* Individuals who fully understand the necessity of the study and have voluntarily consented to participate as subjects\n\nExclusion Criteria:\n\n* other neurological abnormalities (e.g. parkinson's disease).\n* severely impaired cognition\n* serious and complex medical conditions(e.g. active cancer)\n* cardiac pacemaker or other implanted electronic system",true,"ALL","19 Years",{"count":71,"type":72},90,"ESTIMATED","OBSERVATIONAL","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.",[76,77],"Stroke","Fall",[79,80,81],"Predict model","Machin leanning","Electromyography","2025-05-30",{"date":84,"type":85},"2025-06-02","ACTUAL",{"date":87,"type":85},"2024-05-20",{"date":89,"type":72},"2026-04-28",{"name":5,"class":6},1]