Back-support exoskeletons (BSEs) mitigate musculoskeletal strain, yet their efficacy depends on precise, context-aware modulation. This paper introduces a user-centric optimization framework and a vision-based adaptive control strategy for industrial BSEs. First, we constructed a multi-metric optimization space, integrating electromyography reduction, perceived discomfort, and user preference, through baseline experiments with 12 subjects. This revealed a non-linear relationship between optimal assistance and payload. Second, we developed a predictive computer vision pipeline using a Vision Transformer (DINOv2) to estimate payloads before lifting, effectively overcoming actuation latency. Validation with 12 subjects confirmed the system's robustness, achieving over 82% estimation accuracy. Crucially, the adaptive controller reduced peak back muscle activation by up to 23% compared to static baselines while optimizing user comfort. These results validate the proposed framework, demonstrating that pre-lift environmental perception and user-centric optimization significantly enhance physical assistance and human-robot interaction in industrial settings.

翻译：背部支撑外骨骼（BSEs）能够缓解肌肉骨骼劳损，但其效能依赖于精确且情境感知的调节。本文针对工业用BSEs，提出了一种以用户为中心的优化框架及一种基于视觉的自适应控制策略。首先，我们通过12名受试者的基线实验，构建了一个集成肌电图活动降低、感知不适度和用户偏好的多指标优化空间。该空间揭示了最优辅助与载荷之间的非线性关系。其次，我们开发了一个预测性计算机视觉流程，利用Vision Transformer（DINOv2）在举升前估计载荷，有效克服了驱动延迟。对12名受试者的验证证实了系统的鲁棒性，实现了超过82%的估计准确率。至关重要的是，与静态基线相比，该自适应控制器在优化用户舒适度的同时，将背部肌肉峰值激活降低了高达23%。这些结果验证了所提出的框架，表明举升前的环境感知和以用户为中心的优化能显著提升工业场景中的物理辅助效果与人机交互水平。