Developing a Balance Rehabilitation System for Older Adults, Based on Inertial Measurement Unit Sensing and Artificial Intelligence: Personalized Training and Preventive Strategies
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 120
- 试验地点
- 2
- 主要终点
- Static Standing Balance Test
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Prevention
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Aged between 18 and 80 years capable of independent walking-
排除标准
- •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.
结局指标
主要结局
Static Standing Balance Test
时间窗: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Balance Assessments
Single Leg Standing Test
时间窗: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Balance Assessments
Five Times Sit to Stand Test
时间窗: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Functional Tests
Timed Up and Go Test
时间窗: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Functional Tests
Six-Minute Walk Test
时间窗: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Functional Tests
Over-ground walking
时间窗: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Walking test
Walking on a treadmill
时间窗: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Walking test
Delsys Trigno EMG analysis system
时间窗: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Three-Dimensional Motion Analysis
Vicon Bonita
时间窗: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Three-Dimensional Motion Analysis
Force plates
时间窗: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)
Three-Dimensional Motion Analysis
次要结局
未报告次要终点