Low-Dimensional Synergistic Representation of Bilateral Reaching Movements

Abstract

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 l1-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.

Kinematic Synergy of Multi-DoF Movement in Upper Limb and Its Application for Rehabilitation Exoskeleton Motion Planning

“…Then, the spatial position of shoulder, elbow, wrist, and their trajectory of upper limb motion could be recorded during test. Referred as an experimental report (Burns et al, 2017), frame calibrations were performed before the data acquisition as: (1) positive x extended backward away from the subject; (2) positive y extended to the right side of subject, and (3) positive z extended upwards toward the ceiling. The axis x, y, and z were illustrated in Figure 2A.…”

“…Existing evidence suggests that the CNS generates motor command to co-activate multiple muscles working with specific extents, which was generally referred to as “synergy” (Turvey, 2007; Mukta et al, 2015). During motion behaviors, the neural control strategies were modulated to regulate synergistic patterns dynamically to meet the task requirements (Burns et al, 2017). Several pilot studies have employed kinematic synergies to evaluate the inter-joint movement patterns of different human motor approaches (Tresch et al, 2006; Chen and Xiong, 2013).…”

“…Several pilot studies have employed kinematic synergies to evaluate the inter-joint movement patterns of different human motor approaches (Tresch et al, 2006; Chen and Xiong, 2013). Artificial neural network (Devi et al, 2013), linear discriminant analysis (LDA) (Ramana Vinjamuri et al, 2015), and several other algorithms have been introduced to derive synergies; however, principal component analysis (PCA) is most frequently employed for kinematic synergies analysis (Ramana et al, 2010; Chen and Xiong, 2013; Patel et al, 2016; Burns et al, 2017). Burns et al (2017) successfully used PCA method to derive time-varying kinematic synergies of bilateral upper arm reaching movement.…”

“…Artificial neural network (Devi et al, 2013), linear discriminant analysis (LDA) (Ramana Vinjamuri et al, 2015), and several other algorithms have been introduced to derive synergies; however, principal component analysis (PCA) is most frequently employed for kinematic synergies analysis (Ramana et al, 2010; Chen and Xiong, 2013; Patel et al, 2016; Burns et al, 2017). Burns et al (2017) successfully used PCA method to derive time-varying kinematic synergies of bilateral upper arm reaching movement. Bockemühl et al (2010) analyzed the kinematics complexity of human catching movement, and found that the first three principal components captured more than 97% of variance.…”

Synergy Repetition Training versus Task Repetition Training in Acquiring New Skill

“…This skill may be represented in the CNS as motor synergies that encode simultaneous coordination of the many involved joints versus individual control of each joint. Using dimensionality reduction techniques, we (Vinjamuri et al, 2010 ; Patel et al, 2015 ; Burns et al, 2017 ) and others have characterized this synergy subspace at various hierarchical levels including neural (Saleh et al, 2010 ), muscle (d’Avella et al, 2011 ), and kinematic (Santello et al, 2002 ; Vinjamuri et al, 2014 ). If synergies represent motor strategies that have been optimized through experience (involving both explicit and implicit learning mechanisms), they may useful during the learning experience itself.…”