National Taiwan University, College of Medicine, School and Graduate Institute of Physical Therapy
Taipei, 100, Taiwan
Location status: Recruiting
Recruiting
NCT Number: NCT06596993
Developing a Balance Rehabilitation System for Older Adults, Based on IMU and AI: 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.
Recruiting
Interested in participating?
Request InfoKey information
Conditions
Age range
18 year–80 year
Sex eligibility
All sexes
Study type
Interventional
Phase
Not applicable
Primary location
About this study
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.
Who can participate
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Aged between 18 and 80 years capable of independent walking-
Exclusion criteria
- 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.
Treatment and study plan
IMU-based balance training
OtherLeveraging AI technology to identify motion deficiencies, the experimental group will undergo IMU-based balance training
general health education or exercise training
Othergeneral health education or exercise training
Primary outcomes
-
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
Study contacts
Contact information is provided by the study sponsor or research team.
Sponsors and collaborators
Lead sponsor
National Taiwan University Hospital
Other
Registry information
Official study title
Developing a Balance Rehabilitation System for Older Adults, Based on Inertial Measurement Unit Sensing and Artificial Intelligence: Personalized Training and Preventive Strategies
Important dates
- Study start
- 2023
- Primary completion
- 2026
- Study completion
- 2026
- First posted
- Sep 19, 2024
- Registry last updated
- Nov 19, 2025
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
Related clinical trials
Published trials that share one or more normalized conditions with this study.
Recruiting
The Efficacy of a Technology-Assisted Block Training Program on Cognitive Function, Hand Dexterity, and Grip Strength in Community-Dwelling Older Adults: A Mixed-Methods Research
NCT07315724
Aging, Cognition Disorders
Yilan, Taiwan
View Trial Details
Recruiting
OTAGO Exercise Program and Motivational Interviewing
NCT06931067
Behavior, Community-dwelling Older Adults
Portland, Oregon, United States
View Trial Details
Not Yet Recruiting
Intrinsic Capacity, Resilience, and Meaning in Life in Community-Dwelling Midlife and Older Adults
NCT07687563
Community-dwelling Older Adults, Healthy Ageing
Taipei, Nursing Building, No.155, Sec.2, Li-Nong Street,, Taiwan
View Trial Details
Completed
Exercise and Gezaixi Interventions on Community-Dwelling Older Adults
NCT07302568
Behavior, Community-dwelling Older Adults
Taipei, Taitau District, Taiwan
View Trial Details