Ignite Creation Date:
2025-12-25 @ 1:20 AM
Ignite Modification Date:
2026-01-02 @ 10:54 PM
Study NCT ID:
NCT06596993
Status:
RECRUITING
Last Update Posted:
2025-11-19
First Post:
2024-09-11
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Developing a Balance Rehabilitation System for Older Adults, Based on IMU and AI: Personalized Training and Preventive Strategies
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'interventionBrowseModule': {'meshes': [{'id': 'D015444', 'term': 'Exercise'}], 'ancestors': [{'id': 'D009043', 'term': 'Motor Activity'}, {'id': 'D009068', 'term': 'Movement'}, {'id': 'D009142', 'term': 'Musculoskeletal Physiological Phenomena'}, {'id': 'D055687', 'term': 'Musculoskeletal and Neural Physiological Phenomena'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 120}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2023-11-03', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-10', 'completionDateStruct': {'date': '2026-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-11-16', 'studyFirstSubmitDate': '2024-09-11', 'studyFirstSubmitQcDate': '2024-09-11', 'lastUpdatePostDateStruct': {'date': '2025-11-19', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2024-09-19', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-12', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Static Standing Balance Test', 'timeFrame': 'pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)', 'description': 'Balance Assessments'}, {'measure': 'Single Leg Standing Test', 'timeFrame': 'pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)', 'description': 'Balance Assessments'}, {'measure': 'Five Times Sit to Stand Test', 'timeFrame': 'pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)', 'description': 'Functional Tests'}, {'measure': 'Timed Up and Go Test', 'timeFrame': 'pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)', 'description': 'Functional Tests'}, {'measure': 'Six-Minute Walk Test', 'timeFrame': 'pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)', 'description': 'Functional Tests'}, {'measure': 'Over-ground walking', 'timeFrame': 'pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)', 'description': 'Walking test'}, {'measure': 'Walking on a treadmill', 'timeFrame': 'pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)', 'description': 'Walking test'}, {'measure': 'Delsys Trigno EMG analysis system', 'timeFrame': 'pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)', 'description': 'Three-Dimensional Motion Analysis'}, {'measure': 'Vicon Bonita', 'timeFrame': 'pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)', 'description': 'Three-Dimensional Motion Analysis'}, {'measure': 'Force plates', 'timeFrame': 'pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)', 'description': 'Three-Dimensional Motion Analysis'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Inertial Measurement Unit Sensing', 'Artificial Intelligence', 'Balance Rehabilitation', 'Older Adults'], 'conditions': ['Community-dwelling Older Adults']}, 'descriptionModule': {'briefSummary': 'The aging physiological state of the elderly may lead to problems such as unstable gait, balance disorders, and falls. Previous research has confirmed that exercise training can help improve the physical function, quality of life, and reduce the risk of falls in the elderly. In order to achieve effective and continuous intervention training, somatosensory games have become a trend in recent years. Among them, the use of non-immersive virtual reality training methods not only provides training for the elderly, but also reduces the discomfort caused by the virtual environment; however, there are some limitations in clinical rehabilitation training methods, such as the lack of data-based evaluation and personalization. In order to solve the above problems, this research plan will use the inertial measurement unit as a tool for clinical monitoring and human movement assessment, and use artificial intelligence technology to evaluate and adjust the training plan according to its gait characteristics to achieve personalization Training and prevention strategies.', 'detailedDescription': "The development of a balance rehabilitation system for older adults, integrating Inertial Measurement Unit (IMU) sensing and Artificial Intelligence (AI). The key technical components and methodology are as follows:\n\nTechnological Foundation:\n\nIMU sensors will be used to monitor and assess human movement and posture. These sensors detect motion through accelerometers, gyroscopes, and magnetometers, allowing for precise gait analysis.\n\nAI and Generative Adversarial Networks (GAN) will process the data to customize training regimens based on the individual's physiological and movement characteristics.\n\nA Vicon 3D motion capture system will be used in conjunction with IMUs for validating and collecting data during the development phase.\n\nResearch Phases:\n\nYear 1: Developing an AI-based gait training system using IMUs. This involves creating a gait database and balance training protocols using bilateral and unilateral movements.\n\nYear 2: Optimizing the training system using AI and GAN to diversify the data and improve training efficacy.\n\nYear 3: Clinical validation of the system by comparing results between participants undergoing IMU-based training versus standard physical exercises.\n\nTraining Protocols:\n\nExergame Environment: Participants engage in exercises within a virtual environment, which mimics real-world conditions but includes artificial elements to challenge balance and coordination.\n\nBalance Training: Skateboard-based training focuses on unilateral leg movements, monitored by IMUs to provide feedback and adjust difficulty based on performance.\n\nData Analysis:\n\nGait Data: AI and GAN are used to generate personalized gait profiles, which will feed into the training system.\n\nStatistical Analysis: Various statistical tests (e.g., ANOVA) will assess the effectiveness of the system compared to conventional rehabilitation methods.\n\nThis system aims to provide older adults with personalized rehabilitation, reducing fall risk and enhancing their quality of life."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': "Inclusion Criteria:\n\nAged between 18 and 80 years capable of independent walking-\n\nExclusion Criteria:\n\n1. history of lower limb orthopedic surgery, ankylosing spondylitis, rheumatoid arthritis, osteoarthritis, and other medical joint diseases\n2. Those who cannot communicate or follow instructions, and those with severe visual or hearing impairments\n3. the neurological impairment or vestibular disorders, such as stroke, spinal cord injury, Meniere's syndrome."}, 'identificationModule': {'nctId': 'NCT06596993', 'briefTitle': 'Developing a Balance Rehabilitation System for Older Adults, Based on IMU and AI: Personalized Training and Preventive Strategies', 'organization': {'class': 'OTHER', 'fullName': 'National Taiwan University Hospital'}, 'officialTitle': 'Developing a Balance Rehabilitation System for Older Adults, Based on Inertial Measurement Unit Sensing and Artificial Intelligence: Personalized Training and Preventive Strategies', 'orgStudyIdInfo': {'id': '202309084RIND'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'experimental group', 'description': 'IMU-based balance training', 'interventionNames': ['Other: IMU-based balance training']}, {'type': 'OTHER', 'label': 'control group', 'description': 'General health education or exercise training', 'interventionNames': ['Other: general health education or exercise training']}], 'interventions': [{'name': 'IMU-based balance training', 'type': 'OTHER', 'description': 'Leveraging AI technology to identify motion deficiencies, the experimental group will undergo IMU-based balance training', 'armGroupLabels': ['experimental group']}, {'name': 'general health education or exercise training', 'type': 'OTHER', 'description': 'general health education or exercise training', 'armGroupLabels': ['control group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '100', 'city': 'Taipei', 'status': 'RECRUITING', 'country': 'Taiwan', 'contacts': [{'name': 'Hsu Wei-Li, Ph. D', 'role': 'CONTACT', 'email': 'wlhsu@ntu.edu.tw', 'phone': '886-2-33668127'}], 'facility': 'National Taiwan University, College of Medicine, School and Graduate Institute of Physical Therapy', 'geoPoint': {'lat': 25.05306, 'lon': 121.52639}}], 'centralContacts': [{'name': 'Hsu Wei-Li, Ph. D', 'role': 'CONTACT', 'email': 'wlhsu@ntu.edu.tw', 'phone': '886-2-33668127'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'National Taiwan University Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}