SRL ASSISTANCE DATASET

In light of global aging and prevalent stroke-related hemiplegia, this study addresses challenges in robot-assisted Sit-to-Stand (STS) movements, a daily activity prone to falls. Supernumerary Robotic Legs (SRL) serve as independent support, enhancing stability and limb movement range. Existing coordination control methods lack personalization for STS assistance, requiring solutions for human intent transmission and rapidly optimize coordination control challenges in the non-coupled human-robot system. The proposed human-SRL coordination control algorithm, grounded in personalized SRL-human coupling models, incorporates surface electromyography (sEMG) signals to design an intent-driven variable stiffness impedance control. The inclusion of incremental learning enables rapid optimization of impedance parameters, facilitating real-time adjustments in SRL assistance for adaptive coupling with users. Practical experiments involving both healthy participants and hemiplegic patients validate the algorithm's effectiveness during STS.

鉴于全球人口老龄化以及中风相关偏瘫的普遍性，本研究针对机器人辅助的坐到站（STS）动作中的挑战进行研究，这一日常活动易导致跌倒。超额机器人腿（SRL）作为独立支撑，增强了稳定性及肢体运动范围。现有的协调控制方法在STS辅助方面缺乏个性化，需要解决人类意图传输以及非耦合的人-机器人系统中快速优化协调控制挑战的解决方案。所提出的人-SRL协调控制算法，基于个性化的SRL-人耦合模型，结合表面肌电图（sEMG）信号设计了一种意图驱动的可变刚度阻抗控制。引入增量学习使得阻抗参数能够快速优化，从而促进SRL辅助的实时调整，以实现与用户的自适应耦合。包括健康参与者和偏瘫患者在内的实际实验验证了该算法在STS过程中的有效性。