Burns Martin K, Patel Vrajeshri, Florescu Ionut, Pochiraju Kishore V, Vinjamuri Ramana
Sensorimotor Control Laboratory, Department of Biomedical Engineering, Chemistry, and Biological Sciences, Stevens Institute of Technology , Hoboken, NJ , USA.
Front Bioeng Biotechnol. 2017 Feb 10;5:2. doi: 10.3389/fbioe.2017.00002. eCollection 2017.
Kinematic and neuromuscular synergies have been found in numerous aspects of human motion. This study aims to determine how effectively kinematic synergies in bilateral upper arm movements can be used to replicate complex activities of daily living (ADL) tasks using a sparse optimization algorithm. Ten right-handed subjects executed 18 rapid and 11 natural-paced ADL tasks requiring bimanual coordination while sitting at a table. A position tracking system was used to track the subjects' arms in space, and angular velocities over time for shoulder abduction, shoulder flexion, shoulder internal rotation, and elbow flexion for each arm were computed. Principal component analysis (PCA) was used to generate kinematic synergies from the rapid-paced task set for each subject. The first three synergies accounted for 80.3 ± 3.8% of variance, while the first eight accounted for 94.8 ± 0.85%. The first and second synergies appeared to encode symmetric reaching motions which were highly correlated across subjects. The first three synergies were correlated between left and right arms within subjects, whereas synergies four through eight were not, indicating asymmetries between left and right arms in only the higher order synergies. The synergies were then used to reconstruct each natural-paced task using the -norm minimization algorithm. Temporal dilations of the synergies were introduced in order to model the temporal scaling of movement patterns achieved by the cerebellum and basal ganglia as reported previously in the literature. Reconstruction error was reduced by introducing synergy dilations, and cumulative recruitment of several synergies was significantly reduced in the first 10% of training task time by introducing temporal dilations. The outcomes of this work could open new scenarios for the applications of postural synergies to the control of robotic systems, with potential applications in rehabilitation. These synergies not only help in providing near-natural control but also provide simplified strategies for design and control of artificial limbs. Potential applications of these bilateral synergies were discussed and future directions were proposed.
运动学和神经肌肉协同作用已在人类运动的诸多方面被发现。本研究旨在确定如何利用稀疏优化算法，有效地利用双侧上臂运动中的运动学协同作用来复制日常生活活动（ADL）的复杂任务。十名右利手受试者坐在桌前执行18项需要双手协调的快速和11项自然节奏的ADL任务。使用位置跟踪系统在空间中跟踪受试者的手臂，并计算每只手臂的肩外展、肩屈曲、肩内旋和肘屈曲随时间的角速度。主成分分析（PCA）用于从每个受试者的快速任务集中生成运动学协同作用。前三个协同作用占方差的80.3±3.8%，而前八个占94.8±0.85%。第一和第二个协同作用似乎编码了跨受试者高度相关的对称伸展运动。受试者内左臂和右臂之间前三个协同作用是相关的，而第四到第八个协同作用则不相关，这表明仅在高阶协同作用中左右臂之间存在不对称性。然后使用(l_1)-范数最小化算法，利用这些协同作用来重建每个自然节奏任务。引入协同作用的时间扩张，以模拟如先前文献报道的由小脑和基底神经节实现的运动模式的时间缩放。通过引入协同作用扩张，重建误差降低，并且通过引入时间扩张，在训练任务时间的前10%内，几个协同作用的累积募集显著减少。这项工作的成果可能为姿势协同作用在机器人系统控制中的应用开辟新的前景，在康复领域具有潜在应用。这些协同作用不仅有助于提供接近自然的控制，还为假肢的设计和控制提供了简化策略。讨论了这些双侧协同作用的潜在应用并提出了未来的方向。
