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This study will integrate wireless wearable sensors, smartphone imaging, and multimodal artificial intelligence (AI) to address the rehabilitation needs of patients with lumbar degeneration. Patients will undergo comprehensive functional assessments, and individualized exercise instruction with real-time feedback will be provided through a smartphone application. The goals of this research are to: (1) develop a multimodal AI-based digital health system combining IMU sensors and smartphone cameras for real-time assessment and interactive rehabilitation training, (2) construct biomechanics- and gait-analysis models to support personalized rehabilitation for patients with lumbar degeneration, and (3) investigate the mechanisms and clinical efficacy of pelvic control exercise training combined with real-time smartphone feedback in improving function and quality of life for aging patients.
The multimodal AI-based smart assessment and rehabilitation training system developed in this study will provide patients with lumbar degeneration a convenient and precise home-based rehabilitation solution. Through the integration of wireless inertial sensors and smartphone imaging, the system can monitor pelvic and lumbar movements in real time, generate a digital twin model, and deliver instant feedback to guide patients in performing correct exercises. This design not only improves patients' self-awareness of posture and movement but also reduces the risk of improper compensatory strategies that often occur in traditional home exercise programs.
The system is particularly suitable for older adults with mobility limitations or those who have difficulties frequently visiting medical institutions. By enabling remote assessment, individualized training, and long-term monitoring, this platform ensures continuity of care and enhances patients' motivation to engage in rehabilitation. The outcomes of this project will establish a tele-rehabilitation system tailored to degenerative lumbar spine disease, support clinicians in delivering precise and effective treatment, and ultimately reduce the healthcare and economic burden on families and society.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI-Based Smart Assessment and Rehabilitation Training | Experimental | The multimodal AI-based smart assessment and rehabilitation training system developed in this study will provide patients with lumbar degeneration a convenient and precise home-based rehabilitation solution. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI-Based Smart Assessment and Rehabilitation Training | Other | Through the integration of wireless inertial sensors and smartphone imaging, the system can monitor pelvic and lumbar movements in real time, generate a digital twin model, and deliver instant feedback to guide patients in performing correct exercises. |
| Measure | Description | Time Frame |
|---|---|---|
| Functional assessment: Walking speed | Functional assessment is a process that allows for the identification of disability. The data from the functional assessment is used to calculate walking speed (unit: m/s). | 6 months |
| Functional assessment: Walking distance | Functional assessment is a process that allows for the identification of disability. The data from the functional assessment is used to calculate walking distance (unit: m). | 6 months |
| Functional assessment: 5 Times Sit to Stand Test | Functional assessment is a process that allows for the identification of disability. The data from the 5 Times Sit to Stand Test is used to calculate the duration it took to complete the test (unit: s). | 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| Kinematic variables: Joint angles | A motion capture system is used to measure the joint kinematics. The data is used to calculate joint angles (unit: degree). | 6 months |
| Kinetic variables | A motion capture system is used to measure the joint kinetics. The data is used to calculate joint moments (unit: Nm) |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Wei-Li Hsu, Ph.D. | Contact | 886-2-3366-8127 | wlhsu@ntu.edu.tw |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| National Taiwan University Hospital | Recruiting | Taipei | 100 | Taiwan |
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| 6 months |