Common kinematic synergies of various human locomotor behaviours

Huang, Bo; Chen, Xiong; Chen, Wenbin; Liang, Jiejunyi; Sun, Bai-Yang; Gong, Xuan

doi:10.1098/rsos.210161

Cited by 23 publications

(11 citation statements)

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“…The human lower limb can achieve diverse movements in daily living. To build a comprehensive and representative categorization for the lower limb movements and explore their similarities and differences, a motion dataset from previous work of the authors ( Huang et al, 2021a ; Huang et al, 2021b ) was analyzed in this study. Nine healthy male subjects (age: 23.0 ± 1.0 years; weight: 64.0 ± 6.1 kg; height: 173.1 ± 4.1 cm; mean ± s.d.)…”

Section: Methodsmentioning

confidence: 99%

Characterization and Categorization of Various Human Lower Limb Movements Based on Kinematic Synergies

Huang

Chen

Liang

et al. 2022

Front. Bioeng. Biotechnol.

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A proper movement categorization reduces the complexity of understanding or reproducing human movements in fields such as physiology, rehabilitation, and robotics, through partitioning a wide variety of human movements into representative sub-motion groups. However, how to establish a categorization (especially a quantitative categorization) for various human lower limb movements is rarely investigated in literature and remains challenging due to the diversity and complexity of the lower limb movements (diverse gait modes and interaction styles with the environment). Here we present a quantitative categorization for the various lower limb movements. To this end, a similarity measure between movements was first built based on limb kinematic synergies that provide a unified and physiologically meaningful framework for evaluating the similarities among different types of movements. Then, a categorization was established via hierarchical cluster analysis for thirty-four lower limb movements, including walking, running, hopping, sitting-down-standing-up, and turning in different environmental conditions. According to the movement similarities, the various movements could be divided into three distinct clusters (cluster 1: walking, running, and sitting-down-standing-up; cluster 2: hopping; cluster 3: turning). In each cluster, cluster-specific movement synergies were required. Besides the uniqueness of each cluster, similarities were also found among part of the synergies employed by these different clusters, perhaps related to common behavioral goals in these clusters. The mix of synergies shared across the clusters and synergies for specific clusters thus suggests the coexistence of the conservation and augmentation of the kinematic synergies underlying the construction of the diverse and complex motor behaviors. Overall, the categorization presented here yields a quantitative and hierarchical representation of the various lower limb movements, which can serve as a basis for the understanding of the formation mechanisms of human locomotion and motor function assessment and reproduction in related fields.

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Section: Methodsmentioning

confidence: 99%

Characterization and Categorization of Various Human Lower Limb Movements Based on Kinematic Synergies

Huang

Chen

Liang

et al. 2022

Front. Bioeng. Biotechnol.

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“…The first PCs had the most variance explained, the second PCs had the second-highest variance explained, and so on (as shown in Table 1 ) [ 71 ]. According to the criterion established by prior studies, the number of PCs (i.e., synergies) was chosen as a plausible definition of synergies when it explained more than 90% of the variance [ 35 , 72 ].…”

Section: Methodsmentioning

confidence: 99%

Age-related differences in gait symmetry obtained from kinematic synergies and muscle synergies of lower limbs during childhood

2022

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The age-related changes of gait symmetry in healthy children concerning individual joint and muscle activation data have previously been widely studied. Extending beyond individual joints or muscles, identifying age-related changes in the coordination of multiple joints or muscles (i.e., muscle synergies and kinematic synergies) could capture more closely the underlying mechanisms responsible for gait symmetry development. To evaluate the effect of age on the symmetry of the coordination of multiple joints or muscles during childhood, we measured gait symmetry by kinematic and EMG data in 39 healthy children from 2 years old to 14 years old, divided into three equal age groups: preschool children (G1; 2.0–5.9 years), children (G2; 6.0–9.9 years), pubertal children (G3; 10.0–13.9 years). Participants walked barefoot at a self-selected walking speed during three-dimensional gait analysis (3DGA). Kinematic synergies and muscle synergies were extracted with principal component analysis (PCA) and non-negative matrix factorization (NNMF), respectively. The synergies extracted from the left and right sides were compared with each other to obtain a symmetry value. Statistical analysis was performed to examine intergroup differences. The results showed that the effect of age was significant on the symmetry values extracted by kinematic synergies, while older children exhibited higher kinematic synergy symmetry values compared to the younger group. However, no significant age-related changes in symmetry values of muscle synergy were observed. It is suggested that kinematic synergy of lower joints can be asymmetric at the onset of independent walking and showed improving symmetry with increasing age, whereas the age-related effect on the symmetry of muscle synergies was not demonstrated. These data provide an age-related framework and normative dataset to distinguish age-related differences from pathology in children with neuromotor disorders.

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“…In any case, the real function of the brain is not the control of movements per se but the organization of purposive actions, identified by a small number of control variables (thus reducing dimensionality) and structured according to the principle of compositionality: this means that humans simplify the generation of various motor behaviors through the re-use of a limited number of basic motor primitives to be combined in an additive manner, rather than developing entirely new modules for each behavior/task. If we consider the etymology of “synergy” (the word derives from two ancient Greek words: συν+εργός, Sun + ergòs, i.e., “working together”) it is not surprising that anybody working in motor neuroscience agrees on its fundamental role in the organization of purposive actions, although it is equally evident that in the literature there is a large variety of synergies (or “zoo of synergies” to quote Mark Latash, 2008 ): kinematic synergies ( Freitas et al, 2006 ; Huang et al, 2021 ), kinetic synergies ( Slomka et al, 2015 ), muscle synergies ( Cheung and Seki 2021 ), to name a few. They all clarify the concept that the DoFs are not independent but are recruited by combining a limited number of adaptable primitives.…”

Section: Spatio-temporal Invariances Of Multi-joint Motor Controlmentioning

confidence: 99%

A Vexing Question in Motor Control: The Degrees of Freedom Problem

Morasso

2022

Front. Bioeng. Biotechnol.

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The human “marionette” is extremely complex and multi-articulated: anatomical redundancy (in terms of Degrees of Freedom: DoFs), kinematic redundancy (movements can have different trajectories, velocities, and accelerations and yet achieve the same goal, according to the principle of Motor Equivalence), and neurophysiological redundancy (many more muscles than DoFs and multiple motor units for each muscle). Although it is quite obvious that such abundance is not noxious at all because, in contrast, it is instrumental for motor learning, allowing the nervous system to “explore” the space of feasible actions before settling on an elegant and possibly optimal solution, the crucial question then boils down to figure out how the nervous system “chooses/selects/recruits/modulates” task-dependent subsets of countless assemblies of DoFs as functional motor synergies. Despite this daunting conceptual riddle, human purposive behavior in daily life activities is a proof of concept that solutions can be found easily and quickly by the embodied brain of the human cognitive agent. The point of view suggested in this essay is to frame the question above in the old-fashioned but still seminal observation by Marr and Poggio that cognitive agents should be regarded as Generalized Information Processing Systems (GIPS) and should be investigated according to three nearly independent but complementary levels of analysis: 1) the computational level, 2) the algorithmic level, and 3) the implementation level. In this framework, we attempt to discriminate as well as aggregate the different hypotheses and solutions proposed so far: the optimal control hypothesis, the muscle synergy hypothesis, the equilibrium point hypothesis, or the uncontrolled manifold hypothesis, to mention the most popular ones. The proposed GIPS follows the strategy of factoring out shaping and timing by adopting a force-field based approach (the Passive Motion Paradigm) that is inspired by the Equilibrium Point Hypothesis, extended in such a way to represent covert as well overt actions. In particular, it is shown how this approach can explain spatio-temporal invariances and, at the same time, solve the Degrees of Freedom Problem.

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Common kinematic synergies of various human locomotor behaviours

Cited by 23 publications

References 50 publications

Characterization and Categorization of Various Human Lower Limb Movements Based on Kinematic Synergies

Characterization and Categorization of Various Human Lower Limb Movements Based on Kinematic Synergies

Age-related differences in gait symmetry obtained from kinematic synergies and muscle synergies of lower limbs during childhood

A Vexing Question in Motor Control: The Degrees of Freedom Problem

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