Developing a Balance Rehabilitation System for Older Adults, Based on IMU and AI: Personalized Training and Preventive Strategies

Official title

Developing a Balance Rehabilitation System for Older Adults, Based on Inertial Measurement Unit Sensing and Artificial Intelligence: Personalized Training and Preventive Strategies

Overview

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

• Other IMU-based balance training
  Leveraging AI technology to identify motion deficiencies, the experimental group will undergo IMU-based balance training
• Other general health education or exercise training
  general health education or exercise training

Primary outcome measures

• Static Standing Balance Test [Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)]
• Single Leg Standing Test [Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)]
• Five Times Sit to Stand Test [Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)]
• Timed Up and Go Test [Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)]
• Six-Minute Walk Test [Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)]
• Over-ground walking [Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)]
• Walking on a treadmill [Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)]
• Delsys Trigno EMG analysis system [Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)]
• Vicon Bonita [Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)]
• Force plates [Time frame: pre-training, post-training(after 6 weeks), follow-up(after 2 weeks)]

Eligibility criteria

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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation

Randomized

Model

Parallel assignment

Masking

Open label

Primary purpose

Prevention

Study locations

Taiwan · 1 center

• National Taiwan University, College of Medicine, School and Graduate Institute of Physical — Taipei

Identifiers

NCT: NCT06596993 · 202309084RIND

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗