Lanzani Valentina, Brambilla Cristina, Scano Alessandro
Advanced Methods for Biomedical Signal and Image Processing Laboratory, Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), 20133 Milan, Italy.
Biomimetics (Basel). 2024 Oct 13;9(10):619. doi: 10.3390/biomimetics9100619.
Kinematics, kinetics and biomechanics of human gait are widely investigated fields of research. The biomechanics of locomotion have been described as characterizing muscle activations and synergistic control, i.e., spatial and temporal patterns of coordinated muscle groups and joints. Both kinematic synergies and muscle synergies have been extracted from locomotion data, showing that in healthy people four-five synergies underlie human locomotion; such synergies are, in general, robust across subjects and might be altered by pathological gait, depending on the severity of the impairment. In this work, for the first time, we apply the mixed matrix factorization algorithm to the locomotion data of 15 healthy participants to extract hybrid kinematic-muscle synergies and show that they allow us to directly link task space variables (i.e., kinematics) to the neural structure of muscle synergies. We show that kinematic-muscle synergies can describe the biomechanics of motion to a better extent than muscle synergies or kinematic synergies alone. Moreover, this study shows that at a functional level, modular control of the lower limb during locomotion is based on an increased number of functional synergies with respect to standard muscle synergies and accounts for different biomechanical roles that each synergy may have within the movement. Kinematic-muscular synergies may have impact in future work for a deeper understanding of modular control and neuro-motor recovery in the medical and rehabilitation fields, as they associate neural and task space variables in the same factorization. Applications include the evaluation of post-stroke, Parkinson's disease and cerebral palsy patients, and for the design and development of robotic devices and exoskeletons during walking.
人体步态的运动学、动力学和生物力学是广泛研究的领域。运动生物力学被描述为表征肌肉激活和协同控制，即协调的肌肉群和关节的空间和时间模式。运动学协同和肌肉协同都已从运动数据中提取出来，表明在健康人群中，四到五种协同作用构成了人类运动的基础；一般来说，这些协同作用在不同个体间具有较强的稳定性，并且可能会因病理步态而改变，这取决于损伤的严重程度。在这项工作中，我们首次将混合矩阵分解算法应用于15名健康参与者的运动数据，以提取混合运动学 - 肌肉协同作用，并表明它们使我们能够直接将任务空间变量（即运动学）与肌肉协同作用的神经结构联系起来。我们表明，运动学 - 肌肉协同作用比单独的肌肉协同作用或运动学协同作用能更好地描述运动的生物力学。此外，这项研究表明，在功能层面上，运动过程中下肢的模块化控制基于相对于标准肌肉协同作用而言数量增加的功能协同作用，并考虑了每种协同作用在运动中可能具有的不同生物力学作用。运动学 - 肌肉协同作用可能会对未来的工作产生影响，有助于更深入地理解医学和康复领域中的模块化控制和神经运动恢复，因为它们在同一分解中关联了神经和任务空间变量。其应用包括对中风、帕金森病和脑瘫患者的评估，以及用于步行过程中机器人设备和外骨骼的设计与开发。
