Digital Health for Lumbar Degeneration

Overview

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.