Digital Health for Lumbar Degeneration

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.

Inclusion criteria

• Age between 50-80 years to capture the typical characteristics of lumbar degeneration in this age group.
• No history of low back pain lasting more than one week or severe enough to interrupt work within the past year.
• Normal lumbar functional mobility.
• Ability to walk independently for more than 10 meters.

Exclusion criteria

• Presence of systemic joint diseases such as ankylosing spondylitis, rheumatoid arthritis, or multiple sclerosis, which may significantly affect lumbar mobility and gait patterns.
• Central nervous system disorders (e.g., spinal cord injury, stroke, or Parkinson's disease) that may influence gait and motor control.
• Vestibular system disorders, to avoid balance abnormalities interfering with gait testing.
• History of spinal or lower limb surgery, as postoperative changes may affect the accuracy of gait data.
• Inability to communicate or follow instructions.