The prevalence of mobility impairments due to conditions such as spinal cord injuries, strokes, and degenerative diseases is on the rise globally. Lower-limb exoskeletons have been increasingly recognized as a viable solution for enhancing mobility and rehabilitation for individuals with such impairments. However, existing exoskeleton control systems often suffer from limitations such as latency, lack of adaptability, and computational inefficiency. To address these challenges, this paper introduces a novel online adversarial learning architecture integrated with edge computing for high-level lower-limb exoskeleton control. In the proposed architecture, sensor data from the user is processed in real-time through edge computing nodes, which then interact with an online adversarial learning model. This model adapts to the user's specific needs and controls the exoskeleton with minimal latency. Experimental evaluations demonstrate significant improvements in control accuracy and adaptability, as well as enhanced quality-of-service (QoS) metrics. These findings indicate that the integration of online adversarial learning with edge computing offers a robust and efficient approach for the next generation of lower-limb exoskeleton control systems.

翻译：全球范围内，因脊髓损伤、中风及退行性疾病等导致的行动障碍发病率正持续上升。下肢外骨骼已被广泛认定为增强此类患者行动能力与康复效果的有效解决方案。然而，现有外骨骼控制系统普遍存在延迟、适应性不足及计算效率低下等局限。为应对这些挑战，本文提出一种集成边缘计算的新型在线对抗学习架构，用于下肢外骨骼的高层级控制。在所提出的架构中，来自用户的传感器数据通过边缘计算节点进行实时处理，进而与在线对抗学习模型交互。该模型能够适应患者的特定需求，并以极低延迟控制外骨骼。实验评估表明，该方案在控制精度与适应性方面取得显著提升，同时改善了服务质量（QoS）指标。这些发现表明，将在线对抗学习与边缘计算相融合，为下一代下肢外骨骼控制系统提供了一种稳健且高效的解决方案。