Edge-Enabled Real-Time Gait Assessment for Degenerative Spinal Disease Using Wearable Inertial Sensors

Gait analysis is used in the diagnosis, rehabilitation, and longitudinal monitoring of degenerative spinal disease (DSD). However, conventional gait assessment commonly depends on subjective visual observation or laboratory-based motion-capture systems, which restrict accessibility and routine clinical use. This study presents an edge-enabled real-time gait analysis framework for DSD using two ankle-worn inertial measurement units (IMUs). The proposed framework integrates causal gait-event detection with spatiotemporal gait estimation, including stride length, stride height, stride frequency, and swing ratio, across Regular-, Toe-, Heel-, and Tandem-Walk tasks. To improve the stability of wearable gait estimation, the framework incorporates a cycle-wise initial sensor-orientation correction strategy with inter-cycle horizontal velocity continuity, reducing reliance on conventional zero-velocity update (ZUPT) resetting. A percentile-referenced, separability-weighted composite score was also developed to combine average gait performance, step-to-step variability, and gait asymmetry into an interpretable clinical index. Algorithm validation was conducted using an optical motion-capture system as the reference standard. The proposed framework, however, is intended for deployment using ankle-worn IMUs and edge-based computation without requiring optical cameras, reflective markers, or dedicated motion-capture laboratories during routine operation. Experimental results showed centimeter-level spatial estimation accuracy. The composite scoring framework achieved accuracy/F1-scores of 0.971/0.981 for Regular-Walk, 0.934/0.956 for Toe-Walk, 0.955/0.970 for Heel-Walk, and 0.876/0.919 for Tandem-Walk. Feature analysis indicated that stride length, stride frequency, swing ratio, and step-to-step variability provided the greatest discrimination between healthy controls and spinal patients, with stronger group separation observed in Regular-, Toe-, and Heel-Walk tasks. These results suggest that ankle-mounted IMU sensing combined with lightweight edge-based computation and interpretable gait scoring may provide a practical approach for point-of-care gait assessment and remote functional monitoring in DSD. The proposed system operates using only two ankle-mounted IMUs during routine deployment, while optical motion capture was employed exclusively as a laboratory reference for validation.