  Lumbar disc herniation (LDH) is a prevalent orthopedic condition in clinical
practice. Inertial measurement unit sensors (IMUs) are an effective tool for
monitoring and assessing gait impairment in patients with lumbar disc
herniation (LDH). However, the current gait assessment of LDH focuses solely on
single-source acceleration signal data, without considering the diversity of
sensor data. It also overlooks the individual differences in motor function
deterioration between the healthy and affected lower limbs in patients with
LDH. To address this issue, we developed an LDH gait feature model that relies
on multi-source adaptive Kalman data fusion of acceleration and angular
velocity. We utilized an adaptive Kalman data fusion algorithm for acceleration
and angular velocity to estimate the attitude angle and segment the gait phase.
Two Inertial Measurement Units (IMUs) were used to analyze the gait
characteristics of patients with lumbar disc issues and healthy individuals.
This analysis included 12 gait characteristics, such as gait spatiotemporal
parameters, kinematic parameters, and expansibility index numbers. Statistical
methods were employed to analyze the characteristic model and confirm the
biological differences between the healthy affected side of LDH and healthy
subjects. Finally, a classifier based on feature engineering was utilized to
classify the gait patterns of the affected side of patients with lumbar disc
disease and healthy subjects. This approach achieved a classification accuracy
of 95.50%, enhancing the recognition of LDH and healthy gait patterns. It also
provided effective gait feature sets and methods for assessing LDH clinically.
