Community-dwelling Older Adults
Conditions
Keywords
Inertial Measurement Unit Sensing, Artificial Intelligence, Balance Rehabilitation, Older Adults
Brief 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. 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.
Interventions
OTHERIMU-based balance training
Leveraging AI technology to identify motion deficiencies, the experimental group will undergo IMU-based balance training
OTHERgeneral health education or exercise training
general health education or exercise training
Sponsors
National Taiwan University Hospital
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
PREVENTION
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
Aged between 18 and 80 years capable of independent walking-
Exclusion criteria
1. history of lower limb orthopedic surgery, ankylosing spondylitis, rheumatoid arthritis, osteoarthritis, and other medical joint diseases 2. Those who cannot communicate or follow instructions, and those with severe visual or hearing impairments 3. the neurological impairment or vestibular disorders, such as stroke, spinal cord injury, Meniere's syndrome.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Static Standing Balance Test | pre-training, post-training(after 6 weeks), follow-up(after 2 weeks) | Balance Assessments |
| Single Leg Standing Test | pre-training, post-training(after 6 weeks), follow-up(after 2 weeks) | Balance Assessments |
| Five Times Sit to Stand Test | pre-training, post-training(after 6 weeks), follow-up(after 2 weeks) | Functional Tests |
| Timed Up and Go Test | pre-training, post-training(after 6 weeks), follow-up(after 2 weeks) | Functional Tests |
| Six-Minute Walk Test | pre-training, post-training(after 6 weeks), follow-up(after 2 weeks) | Functional Tests |
| Over-ground walking | pre-training, post-training(after 6 weeks), follow-up(after 2 weeks) | Walking test |
| Walking on a treadmill | pre-training, post-training(after 6 weeks), follow-up(after 2 weeks) | Walking test |
| Delsys Trigno EMG analysis system | pre-training, post-training(after 6 weeks), follow-up(after 2 weeks) | Three-Dimensional Motion Analysis |
| Vicon Bonita | pre-training, post-training(after 6 weeks), follow-up(after 2 weeks) | Three-Dimensional Motion Analysis |
| Force plates | pre-training, post-training(after 6 weeks), follow-up(after 2 weeks) | Three-Dimensional Motion Analysis |
Countries
Taiwan
Contacts
Primary ContactHsu Wei-Li, Ph. D
Outcome results
None listed