Ignite Creation Date:
2024-10-26 @ 3:40 PM
Last Modification Date:
2024-10-26 @ 3:40 PM
Study NCT ID:
NCT06596993
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-09-11
Brief Title:
Developing a Balance Rehabilitation System for Older Adults Based on IMU and AI Personalized Training and Preventive Strategies
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Developing a Balance Rehabilitation System for Older Adults Based on Inertial Measurement Unit Sensing and Artificial Intelligence Personalized Training and Preventive Strategies
Status:
RECRUITING
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 individuals 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 eg 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
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 individuals 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 eg 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None