Data_Sheet_1_Towards Human-like Walking with Biomechanical and Neuromuscular Control Features: Personalized Attachment Point Optimization Method of Cable-Driven Exoskeleton.PDF

The cable-driven exoskeleton can avoid joint misalignment, and is substantial alterations in the pattern of muscle synergy coordination, which arouse more attention in recent years to facilitate exercise for older adults and improve their overall quality of life. This study leverages principles from neuroscience and biomechanical analysis to select attachment points for cable-driven soft exoskeletons. By extracting key features of human movement, the objective is to develop a subject-specific design methodology that provides precise and personalized support in the attachment points optimization of cable-driven exoskeleton to achieve natural gait, energy efficiency, and muscle coordination controllable in the domain of human mobility and rehabilitation. To achieve this, the study first analyzes human walking experimental data and extracts biomechanical features. These features are then used to generate trajectories, allowing better natural movement under complete cable-driven exoskeleton control. Next, a genetic algorithm-based method is employed to minimize energy consumption and optimize the attachment points of the cable-driven system. This process identifies connections that are better suited for the human model, leading to improved efficiency and natural movement. By comparing the calculated elderly human model driven by exoskeleton with experimental subject in terms of joint angles, joint torques and muscle forces, the human model can successfully replicate subject movement and the cable output forces can mimic human muscle coordination. The optimized cable attachment points facilitate more natural and efficient collaboration between humans and the exoskeleton, making significant contributions to the field of assisting the elderly in rehabilitation.</p

Inter-Hemispheric Correlation of Node Betweenness.

<p>Each circle gives the left and right betweenness value for each node. Each age group shows a rightward assymetry - indicated by the slope values 0.2843, 0.6202, and 0.4738, respectively (1 indicates perfect symmetry). The dashed lines indicate 95% confidence interval. The betweenness centrality value is normalized by division by .</p

Average Lengths of Connection Fibers (mm).

<p>Average Lengths of Connection Fibers (mm).</p

Network Efficiency.

<p>Local and global efficiency of pediatric brain networks of (a) 2-weeks-olds, (b) 1-year-olds and (c) 2-year-olds. All networks exhibit small-world nature, which is characterized by local efficiency greater than comparable random networks, and global efficiency greater than regular lattices <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-Latora1" target="_blank">[5]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-Latora2" target="_blank">[6]</a>. There is a general trend of efficiency increase with age. The neonatal brain network shows significantly lower efficiency compared to the other two age groups.</p

Node Degree Distributions.

<p>Single-scale, scale-free and broad-scale <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-Amaral1" target="_blank">[39]</a> are characterized by Gaussian/exponential decay, power law decay, and truncated power law decay, respectively. The node degree distributions give good indication that the pediatric brain networks are broad-scale in nature. In the double logarithmic plots, the degree distribution decays linearly before a sharp cutoff. The gradient magnitudes of the fitted lines are 3.921, 2.784 and 2.764 for (a), (b) and (c), respectively.</p

Obtaining the Connectivity Matrix.

<p>A schematic digram illustrating the major processes involved in generating the final connectivity maps. Streamline fiber tractography was performed on each diffusion tensor image and a connectivity matrix was computed based on the AAL <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-TzourioMazoyer1" target="_blank">[23]</a> ROIs. The fiber count matrices were constructed by enumerating the number of fibers connecting each region pair. The connectivity matrix, indicating consistent connections, was generated by thresholding the fiber count statistics.</p

Network Communities.

<p>The spring-embedding visualization of networks is implemented with Kamada-Kawai layout algorithm using the Pajek <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-Batagelj1" target="_blank">[57]</a> software package (pajek.imfm.si/doku.php). The nodes and intra-modular connections are colored-coded by the communities detected by the algorithm described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-Clauset1" target="_blank">[16]</a>, while inter-modular connections are colored-coded with light-gray. The sizes of the vertices are weighted by the (logarithmically scaled) node betweenness <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-Freeman1" target="_blank">[29]</a>. Descriptions of the abbreviated region labels can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone-0024678-t001" target="_blank">Table 1</a>. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone-0024678-t003" target="_blank">Table 3</a> for the constituent regions in each community.</p

Betweenness Centrality, Intra-Modular Degree, and Participation Coefficient.

<p>The values are sorted based those of the 2-year-olds. The role of each node, as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-Guimer1" target="_blank">[26]</a>, is specified above the respective bar: (A) non-hub ultra-peripheral node; (B) non-hub peripheral node; (C) non-hub connector nodes; and (D) non-hub kinless nodes; (E) provincial hubs; (F) connector hubs; and (G) kinless hubs. No node was found to satisfy the conditions required by (F) and (G).</p

Constituent Regions in Each Community.

<p>Constituent Regions in Each Community.</p

Nonrandom Modularity.

<p>Comparing the modularities <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-Newman1" target="_blank">[15]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-Clauset1" target="_blank">[16]</a> of the brain networks with comparable random networks indicates non-random network modularity.</p