Miao Qing, Fu Xueming, Chen Yi-Feng
School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, Hubei, China.
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Front Bioeng Biotechnol. 2024 Nov 15;12:1484265. doi: 10.3389/fbioe.2024.1484265. eCollection 2024.
Robot-assisted mirror therapy has been widely developed to help remodeling of premotor cortex for patients suffering from motor disability of limbs. Nevertheless, it is difficult to achieve real-time adaptive control in robot-assisted mirror rehabilitation training, particularly for patients with varying levels of limb impairment.
This paper proposes an equivalent kinematics control framework based on the Broaden Learning System model for active robotic mirror rehabilitation, where people's bilateral upper limbs actively perform mirror movements to enhance the impaired limb's participation. The framework accommodates a broaden learning model from sensing multi-kinematic features to adjust the robotic damping coefficient in assisting human participants to complete mirror-symmetry training. Besides, in order to adapt to inter-patients' variability with different disability levels, a challenge-level modification interface is also fused for safer training. This model is verified by additional symmetry indicator such as position trajectory error and force.
Experimental results show that the weaker subjects can also maintain mirror movement with the stronger subjects under the help of this model and verify the performance of framework in mirror-symmetry effects and movement smoothness. This leads us to believe that the framework can safely and efficiently assist human participants in completing mirror-symmetry movement.
The framework has the potential to improve outcomes in smoother and safer mirror-symmetry training by sensing multi-kinematic features. Future studies are necessary to involve clinical trials with actual patients.
机器人辅助镜像疗法已得到广泛发展，以帮助肢体运动功能障碍患者重塑运动前区皮质。然而，在机器人辅助镜像康复训练中难以实现实时自适应控制，尤其是对于肢体损伤程度不同的患者。
本文提出了一种基于增广学习系统模型的等效运动学控制框架，用于主动式机器人镜像康复训练，即人们通过双侧上肢主动进行镜像运动，以增强受损肢体的参与度。该框架采用增广学习模型，通过感知多种运动学特征来调整机器人阻尼系数，以协助人类参与者完成镜像对称训练。此外，为了适应不同残疾程度患者之间的差异，还融合了一个挑战级别修改界面，以实现更安全的训练。该模型通过位置轨迹误差和力等额外的对称指标进行了验证。
实验结果表明，在该模型的帮助下，较弱的受试者也能与较强的受试者保持镜像运动，并验证了该框架在镜像对称效果和运动平滑度方面的性能。这使我们相信，该框架能够安全有效地协助人类参与者完成镜像对称运动。
该框架有潜力通过感知多种运动学特征，在更平滑、更安全的镜像对称训练中改善训练效果。未来有必要开展针对实际患者的临床试验研究。
