Community-dwelling Older AdultsNCT06596993
Developing a Balance Rehabilitation System for Older Adults, Based on IMU and AI: Personalized Training and Preventive Strategies
A study testing IMU-based balance training and general health education or exercise training in people with Community-dwelling Older Adults, run by National Taiwan University Hospital at 1 site in Taiwan.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Community-dwelling Older Adults
- What is being tested?
- IMU-based balance training and general health education or exercise training
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 120 participants
- Where
- 1 siteTaiwan
- Study dates
- 3 Nov 2023to Dec 2026
- Phase
- Interventional
- 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 18 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Aged between 18 and 80 years capable of independent walking-
Who cannot join
- history of lower limb orthopedic surgery, ankylosing spondylitis, rheumatoid arthritis, osteoarthritis, and other medical joint diseases
- Those who cannot communicate or follow instructions, and those with severe visual or hearing impairments
- the neurological impairment or vestibular disorders, such as stroke, spinal cord injury, Meniere's syndrome.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site, 1 recruiting
- Recruiting
National Taiwan University, College of Medicine, School and Graduate Institute of Physical Therapy
Taipei, 100
Hsu Wei-Li, Ph. D, 886-2-33668127, wlhsu@ntu.edu.tw
About this study
Official title: Developing a Balance Rehabilitation System for Older Adults, Based on Inertial Measurement Unit Sensing and Artificial Intelligence: Personalized Training and Preventive Strategies
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.
Read the detailed description
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:
Technological Foundation:
IMU 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.
AI and Generative Adversarial Networks (GAN) will process the data to customize training regimens based on the individual's physiological and movement characteristics.
A Vicon 3D motion capture system will be used in conjunction with IMUs for validating and collecting data during the development phase.
Research Phases:
Year 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.
Year 2: Optimizing the training system using AI and GAN to diversify the data and improve training efficacy.
Year 3: Clinical validation of the system by comparing results between participants undergoing IMU-based training versus standard physical exercises.
Training Protocols:
Exergame Environment: Participants engage in exercises within a virtual environment, which mimics real-world conditions but includes artificial elements to challenge balance and coordination.
Balance Training: Skateboard-based training focuses on unilateral leg movements, monitored by IMUs to provide feedback and adjust difficulty based on performance.
Data Analysis:
Gait Data: AI and GAN are used to generate personalized gait profiles, which will feed into the training system.
Statistical Analysis: Various statistical tests (e.g., ANOVA) will assess the effectiveness of the system compared to conventional rehabilitation methods.
This system aims to provide older adults with personalized rehabilitation, reducing fall risk and enhancing their quality of life.
- Conditions
- Community-dwelling Older Adults
- Being tested
- IMU-based balance training, general health education or exercise training
- Keywords
- Inertial Measurement Unit Sensing, Artificial Intelligence, Balance Rehabilitation, Older Adults
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
IMU-based balance trainingOther
Leveraging AI technology to identify motion deficiencies, the experimental group will undergo IMU-based balance training
general health education or exercise trainingOther
general health education or exercise training
| Study group | Role | What they receive |
|---|---|---|
| experimental group | Experimental | IMU-based balance training Receives: Other: IMU-based balance training |
| control group | Other | General health education or exercise training Receives: Other: general health education or exercise training |
What is being measured
The primary outcome is the main question the study is designed to answer.
Static Standing Balance Test
Balance Assessments
Measured: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Single Leg Standing Test
Balance Assessments
Measured: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Five Times Sit to Stand Test
Functional Tests
Measured: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Timed Up and Go Test
Functional Tests
Measured: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Six-Minute Walk Test
Functional Tests
Measured: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Over-ground walking
Walking test
Measured: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Walking on a treadmill
Walking test
Measured: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Delsys Trigno EMG analysis system
Three-Dimensional Motion Analysis
Measured: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Vicon Bonita
Three-Dimensional Motion Analysis
Measured: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Force plates
Three-Dimensional Motion Analysis
Measured: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Sep 2024
- Study start3 Nov 2023
- Primary completionDec 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Community-dwelling Older AdultsStudies of the same condition, open and under way.
- More trials run by National Taiwan 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06596993
- Sponsor's protocol ID
- 202309084RIND
- Status last verified
- Oct 2025
- First submitted
- 11 Sep 2024
- Last updated
- 19 Nov 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 19 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Developing a Balance Rehabilitation System for Older Adults, Based on Inertial Measurement Unit Sensing and Artificial Intelligence: Personalized Training and Preventive Strategies
- ClinicalTrials.gov ID
- NCT06596993
- Acronym / sponsor ID
- 202309084RIND
- Conditions
- Community-dwelling Older Adults
- Keywords
- Inertial Measurement Unit Sensing, Artificial Intelligence, Balance Rehabilitation, Older Adults
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Study start
- 3 Nov 2023 (actual)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 19 Sep 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 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.
Detailed description
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:
Technological Foundation:
IMU 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.
AI and Generative Adversarial Networks (GAN) will process the data to customize training regimens based on the individual's physiological and movement characteristics.
A Vicon 3D motion capture system will be used in conjunction with IMUs for validating and collecting data during the development phase.
Research Phases:
Year 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.
Year 2: Optimizing the training system using AI and GAN to diversify the data and improve training efficacy.
Year 3: Clinical validation of the system by comparing results between participants undergoing IMU-based training versus standard physical exercises.
Training Protocols:
Exergame Environment: Participants engage in exercises within a virtual environment, which mimics real-world conditions but includes artificial elements to challenge balance and coordination.
Balance Training: Skateboard-based training focuses on unilateral leg movements, monitored by IMUs to provide feedback and adjust difficulty based on performance.
Data Analysis:
Gait Data: AI and GAN are used to generate personalized gait profiles, which will feed into the training system.
Statistical Analysis: Various statistical tests (e.g., ANOVA) will assess the effectiveness of the system compared to conventional rehabilitation methods.
This system aims to provide older adults with personalized rehabilitation, reducing fall risk and enhancing their quality of life.
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| experimental group | Experimental | Other: IMU-based balance trainingIMU-based balance training |
| control group | Other | Other: general health education or exercise trainingGeneral health education or exercise training |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Aged between 18 and 80 years capable of independent walking-
Key exclusion criteria (3 listed)
- history of lower limb orthopedic surgery, ankylosing spondylitis, rheumatoid arthritis, osteoarthritis, and other medical joint diseases
- Those who cannot communicate or follow instructions, and those with severe visual or hearing impairments
- the neurological impairment or vestibular disorders, such as stroke, spinal cord injury, Meniere's syndrome.
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 |
|---|---|---|
| IMU-based balance trainingLeveraging AI technology to identify motion deficiencies, the experimental group will undergo IMU-based balance training | Other | experimental group |
| general health education or exercise traininggeneral health education or exercise training | Other | control group |
Primary outcome
Static Standing Balance Test
Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Balance Assessments
Single Leg Standing Test
Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Balance Assessments
Five Times Sit to Stand Test
Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Functional Tests
Timed Up and Go Test
Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Functional Tests
Six-Minute Walk Test
Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Functional Tests
Over-ground walking
Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Walking test
Walking on a treadmill
Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Walking test
Delsys Trigno EMG analysis system
Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Three-Dimensional Motion Analysis
Vicon Bonita
Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Three-Dimensional Motion Analysis
Force plates
Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Three-Dimensional Motion Analysis
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 19 Nov 2025.
Related trials for Community-dwelling Older Adults
Not yet recruiting
Multicomponent Exercise and Pain Science Education for Fall Prevention in Older Adults With Chronic Musculoskeletal Pain (MovePainFall)
Chronic Pain, Musculoskeletal Pain
Not yet recruiting
Motor Imagery and Turning Practice to Improve Mobility: Pilot Trial
Mobility Impairment, Community Dwelling Older Adults
Not yet recruiting
Effects of the EatWelLog App on Enhancing Diet Management for Older Adults With Sarcopenic Obesity
Sacropenic Obesity
Not yet recruiting
Cost-Effectiveness Analysis of the Healthy Beat Acupunch Regimen on Physical Fitness, Sleep Quality, and Quality of Life Among Community-Dwelling Older Adults
Healthy Aging, Health Promotion
Not yet recruiting
Study of Bedroom Environment Sleep Intervention at Home for Older Adults Living Alone With Memory Concerns
Subjective Cognitive Decline (SCD), Mild Cognitive Impairment (MCI)
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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing IMU-based balance training and general health education or exercise training in people with Community-dwelling Older Adults, run by National Taiwan University Hospital at 1 site in Taiwan.
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 1 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: looking for participants now, as of the sponsor's last update on 19 Nov 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 Taiwan. 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 Hsu Wei-Li, Ph. D (886-2-33668127, wlhsu@ntu.edu.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Static Standing Balance Test (time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)). The study lists 10 primary outcomes in all. 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 19 Nov 2025.