A Muscle-Driven Lumbar Spine Model for Predicting Vibration-Induced Spinal Loads with Adaptive Control
摘要
Low back pain associated with whole-body vibration (WBV) exposure remains a significant health concern, yet the biomechanical mechanisms linking WBV to spinal loads are incompletely understood. Prior computational studies often relied on simplified assumptions, such as static muscle activation patterns and constrained lumbar joint rotations, limiting the fidelity of dynamic spinal load predictions. To address these gaps, this study aims to establish and validate a muscle-driven lumbar spine model that integrates nonlinear mechanical properties of intervertebral joints and an adaptive feedback control strategy.
MethodsA hybrid inverse-forward dynamics framework, integrated with a robust adaptive proportional-integral-derivative (PID)-based control algorithm providing closed-loop feedback tracking, dynamically allocated muscle excitations to stabilize lumbar posture under vertical vibration without artificial rotational constraints. The effects of muscle activations and vibration frequency on spinal biomechanical loads and biodynamic responses were also investigated.
ResultsValidations against in vivo intradiscal pressure and erector spinae electromyography showed good agreement (r > 0.9). For biodynamic responses, seat-to-head transmissibility was used to set the pelvis–seat interface properties, and apparent mass was predicted with favorable agreement. A preliminary analysis of frequency effects revealed peak spinal loads near resonance. Active muscle control considerably altered resonance frequencies (4.5 Hz vs. 5 Hz in passive models) and reduced vibration transmissibility while increasing lumbar compressive loads at resonance, highlighting a critical trade-off between vibration mitigation and spinal biomechanical stress.
ConclusionBy addressing limitations in resolving dynamic muscle recruitment and joint-level loads, this work provides a validated framework for evaluating vibration-induced spinal biomechanics, offering insights into injury pathways and informing ergonomic interventions.