用视觉预判负重，动态调节外骨骼助力，更省力更舒适。
Toward Context-Aware Exoskeleton Assistance: Integrating Computer Vision Payload Estimation with a User-Centric Optimization Space
- 基于视觉识别提前估算负重，解决动作延迟问题。
- 实测负重识别准确率超82%，背肌激活峰值降低23%。
- 兼顾肌肉减负、舒适度与用户偏好，适合工业场景。
背托式外骨骼（BSE）可缓解肌肉骨骼压力，但其效果依赖于精准的上下文感知调节。本文提出一种以用户为中心的优化框架与基于视觉的自适应控制策略。首先，通过12名受试者的基线实验构建多指标优化空间，融合肌电减少、主观不适感与用户偏好，揭示最优助力与负载间存在非线性关系。其次，开发基于视觉变换器（DINOv2）的预测视觉流水线，实现抬举前的负载估计，有效克服执行延迟。在12名受试者上的验证表明，系统具备鲁棒性，负载估计准确率超过82%。关键的是，自适应控制器相比静态基线，将背肌峰值激活降低最多达23%，同时优化用户舒适度。结果验证了该框架的有效性，表明抬举前环境感知与用户中心优化能显著提升工业场景下的物理辅助与人机交互质量。
原文摘要 · Abstract (English)
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.
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