State of the art controllers for back exoskeletons largely rely on body kinematics. This results in control strategies which cannot provide adaptive support under unknown external loads. We developed a neuromechanical model-based controller (NMBC) for a soft back exosuit, wherein assistive forces were proportional to the active component of lumbosacral joint moments, derived from real-time electromyography-driven models. The exosuit provided adaptive assistance forces with no a priori information on the external loading conditions. Across 10 participants, who stoop-lifted 5 and 15 kg boxes, our NMBC was compared to a non-adaptive virtual spring-based control(VSBC), in which exosuit forces were proportional to trunk inclination. Peak cable assistive forces were modulated across weight conditions for NMBC (5kg: 2.13 N/kg; 15kg: 2.82 N/kg) but not for VSBC (5kg: 1.92 N/kg; 15kg: 2.00 N/kg). The proposed NMBC strategy resulted in larger reduction of cumulative compression forces for 5 kg (NMBC: 18.2%; VSBC: 10.7%) and 15 kg conditions (NMBC: 21.3%; VSBC: 10.2%). Our proposed methodology may facilitate the adoption of non-hindering wearable robotics in real-life scenarios.

翻译：当前最先进的背部外骨骼控制器主要依赖身体运动学，导致在未知外部载荷下无法提供自适应支持。我们开发了一种基于神经力学模型的控制器（NMBC），用于柔软背部外衣，其辅助力与实时肌电驱动模型推导的腰骶关节主动力矩分量成正比。该外衣无需预先了解外部负载条件即可提供自适应辅助力。在10名受试者分别搬运5kg和15kg箱子进行弯腰举重的实验中，我们将所提出的NMBC与非自适应虚拟弹簧控制器（VSBC，外衣力与躯干倾斜度成正比）进行了比较。结果显示，NMBC的峰值缆绳辅助力在不同重量条件下可调节（5kg: 2.13 N/kg；15kg: 2.82 N/kg），而VSBC则未调节（5kg: 1.92 N/kg；15kg: 2.00 N/kg）。与VSBC相比，所提出的NMBC策略在5kg（NMBC: 18.2%；VSBC: 10.7%）和15kg（NMBC: 21.3%；VSBC: 10.2%）条件下均实现了更大的累积压缩力减少。我们的方法可能推动非阻碍性可穿戴机器人在现实场景中的应用。