Xu Datao, Zhou Huiyu, Quan Wenjing, Gusztav Fekete, Baker Julien S, Gu Yaodong
Faculty of Sports Science, Ningbo University, Ningbo 315211, China; Faculty of Engineering, University of Pannonia, Veszprém 8201, Hungary; Savaria Institute of Technology, Eötvös Loránd University, Szombathely 9700, Hungary.
Faculty of Sports Science, Ningbo University, Ningbo 315211, China; School of Health and Life Sciences, University of the West of Scotland, Scotland G72 0LH, United Kingdom.
Comput Methods Programs Biomed. 2023 Dec;242:107848. doi: 10.1016/j.cmpb.2023.107848. Epub 2023 Oct 6.
For patients with movement disorders, the main clinical focus is on exercise rehabilitation to help recover lost motor function, which is achieved by relevant assisted equipment. The basis for seamless control of the assisted equipment is to achieve accurate inference of the user's movement intentions in the human-machine interface. This study proposed a novel movement intention detection technology for estimating lower limb joint continuous kinematic variables following muscle synergy patterns, to develop applications for more efficient assisted rehabilitation training.
This study recruited 16 healthy males and 16 male patients with symptomatic patellar tendinopathy (VISA-P: 59.1 ± 8.7). The surface electromyography of 12 muscles and lower limb joint kinematic and kinetic data from healthy subjects and patients during step-off landings from 30 cm-high stair steps were collected. We subsequently solved the preprocessed data based on the established recursive model of second-order differential equation to obtain the muscle activation matrix, and then imported it into the non-negative matrix factorization model to obtain the muscle synergy matrix. Finally, the lower limb neuromuscular synergy pattern was then imported into the developed adaptive neuro-fuzzy inference system non-linear regression model to estimate the human movement intention during this movement pattern.
Six muscle synergies were determined to construct the muscle synergy pattern driven ANFIS model. Three fuzzy rules were determined in most estimation cases. Combining the results of the four error indicators across the estimated variables indicates that the current model has excellent estimated performance in estimating lower limb joint movement. The estimation errors between the healthy (Angle: R=0.98±0.03; Torque: R=0.96±0.04) and patient (Angle: R=0.98±0.02; Torque: R=0.96±0.03) groups are consistent.
The proposed model of this study can accurately and reliably estimate lower limb joint movements, and the effectiveness will also be radiated to the patient group. This revealed that our models also have certain advantages in the recognition of motor intentions in patients with relevant movement disorders. Future work from this study can be focused on sports rehabilitation in the clinical field by achieving more flexible and precise movement control of the lower limb assisted equipment to help the rehabilitation of patients.
对于运动障碍患者，临床主要关注通过相关辅助设备进行运动康复训练以帮助恢复丧失的运动功能。辅助设备无缝控制的基础是在人机界面中准确推断用户的运动意图。本研究提出一种新颖的运动意图检测技术，用于根据肌肉协同模式估计下肢关节连续运动学变量，以开发更高效辅助康复训练的应用。
本研究招募了16名健康男性和16名有症状的髌腱病男性患者（VISA-P：59.1±8.7）。收集了健康受试者和患者在从30厘米高的楼梯台阶下台阶着地过程中12块肌肉的表面肌电图以及下肢关节运动学和动力学数据。随后，我们基于建立的二阶微分方程递归模型求解预处理后的数据以获得肌肉激活矩阵，然后将其导入非负矩阵分解模型以获得肌肉协同矩阵。最后，将下肢神经肌肉协同模式导入所开发的自适应神经模糊推理系统非线性回归模型，以估计此运动模式期间的人体运动意图。
确定了六种肌肉协同作用以构建由肌肉协同模式驱动的自适应神经模糊推理系统模型。在大多数估计情况下确定了三条模糊规则。综合估计变量的四个误差指标结果表明，当前模型在估计下肢关节运动方面具有出色的估计性能。健康组（角度：R = 0.98±0.03；扭矩：R = 0.96±0.04）和患者组（角度：R = 0.98±0.02；扭矩：R = 0.96±0.03）之间的估计误差一致。
本研究提出的模型能够准确可靠地估计下肢关节运动，并且其有效性也将辐射到患者群体。这表明我们的模型在相关运动障碍患者的运动意图识别方面也具有一定优势。本研究未来的工作可以通过实现下肢辅助设备更灵活精确的运动控制来专注于临床领域的运动康复，以帮助患者康复。
