Linear and Nonlinear Kinematic Synergies in the Grasping Hand

Patel, Vrajeshri; Burns, Martin

doi:10.4172/2155-9538.1000163

Cited by 22 publications

(24 citation statements)

References 35 publications

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“…Statistic methods for dimensional reduction are used to search synergies, Principal Component Analysis (PCA) being the most widely applied since it has provided good results in hand kinematics 17 . PCA allows the original multivariate space of highly correlated variables (DoF) to be transformed into a smaller set of new uncorrelated variables (linear combinations of the original variables called synergies) explaining a high percentage of the original variability.…”

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confidence: 99%

Sharing of hand kinematic synergies across subjects in daily living activities

Gracia-Ibáñez

Sancho-Bru

Jarque-Bou

et al. 2020

Sci Rep

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Roda-Sales the motor system is hypothesised to use kinematic synergies to simplify hand control. Recent studies suggest that there is a large set of synergies, sparse in degrees of freedom, shared across subjects, so that each subject performs each action with a sparse combination of synergies. identifying how synergies are shared across subjects can help in prostheses design, in clinical decision-making or in rehabilitation. Subject-specific synergies of healthy subjects performing a wide number of representative daily living activities were obtained through principal component analysis. to make synergies comparable between subjects and tasks, the hand kinematics data were scaled using normative range of motion data. to obtain synergies sparse in degrees of freedom a rotation method that maximizes the sum of the variances of the squared loadings was applied. Resulting synergies were clustered and each cluster was characterized by a core synergy and different indexes (prevalence, relevance for function and within-cluster synergy similarity), substantiating the sparsity of synergies. The first two core synergies represent finger flexion and were present in all subjects. The remaining core synergies represent coordination of the thumb joints, thumb-index joints, palmar arching or fingers adduction, and were employed by subjects in different combinations, thus revealing different subjectspecific strategies.

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confidence: 99%

Sharing of hand kinematic synergies across subjects in daily living activities

Gracia-Ibáñez

Sancho-Bru

Jarque-Bou

et al. 2020

Sci Rep

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“…The possibility remains that nonlinear dimensionality reduction would reveal that hand kinematics indeed occupy a low-dimensional manifold. However, linear approaches, including PCA, have been previously shown to yield more efficient and reliable manifolds than do non-linear ones [39]. While the informativeness (condition dependence) of low-variance dimensions in non-linear manifolds was not probed in these previous studies, there is no reason to believe that a less efficient manifold in terms of explained variance would yield a more efficient representation in terms of informativeness.…”

Section: Volitional Hand Movements Are High Dimensionalmentioning

confidence: 98%

“…In other words, even though grasp and ASL occupy a different manifold than does typing, those different manifolds should, themselves, be low-dimensional. However, classification based on subsets of principal components revealed that high-rank (e.g., [20][21][22] PCs contain substantial object-and ASL sign-specific information. Indeed, kinematic trajectories projected on high-rank PCs were highly consistent within condition and different across conditions (Supplementary Figure 1).…”

Section: Volitional Hand Movements Are High Dimensionalmentioning

confidence: 99%

“…Indeed, principal component analysis (PCA) of kinematics or muscle activations reveals that a small number of principal components (PCs) account for most of the variance in the movements or activations, typically measured for a single behavior (e.g. grasping, playing piano, typing) [11,12,[19][20][21][22][23]. The more or less explicit assumption underlying the interpretation of this dimensionality reduction is that high-variance principal components (PCs) -the synergies -are under volitional control whereas low-variance PCs reflect motor or measurement noise.…”

Section: Introductionmentioning

confidence: 99%

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Unexpected complexity of everyday manual behaviors

Goodman

Bensmaı̈a

2019

Preprint

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ACKNOWLEDGMENTSWe would like to thank Dalton Moore for help with experimental design and data collection. We would like to thank Matt Kaufman for help with the title. ABSTRACTHow does the brain control an effector as complex and versatile as the hand? One possibility is that the neural control of the hand is simplified by limiting the number of achievable hand postures. Indeed, hand kinematics can be largely accounted for within a small subspace. This oft replicated finding has been interpreted as evidence that hand postures are confined to this subspace and that leaving it volitionally is impossible. A prediction from this hypothesis is that measured hand movements that fall outside of this subspace reflect motor or measurement noise. To address this question, we track hand postures of human participants as they perform three tasks -grasping, typing, and signing in American Sign Language. We then apply a standard dimensionality reduction technique -principal components analysis -and replicate the finding that hand movements can be largely described within a much reduced subspace. However, we show that kinematic dimensions that fall outside of this subspace are highly structured and task dependent, suggesting that they too are under volitional control. Moreover, we find that the hand kinematics of monkeys performing a grasping task also occupy a highdimensional space as do the underlying neuronal representations in primary motor cortex. We conclude that hand control occupies a high dimensional space and propose that the complexity of hand movements is well within the scope of the brain's computational power.

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“…NMF is typically used to derive neuromuscular synergies (Tresch and Jarc, 2009 ), while PCA is frequently used to derive kinematic synergies as in Mason et al ( 2001 ) and Vinjamuri et al ( 2010 ). PCA-derived kinematic synergies have been demonstrated to perform favorably when directly compared to those from other linear and non-linear dimensionality reduction methods when applied to hand grasp reconstruction (Patel et al, 2015a ).…”

Section: Introductionmentioning

confidence: 99%

Low-Dimensional Synergistic Representation of Bilateral Reaching Movements

Burns

Patel

Florescu

et al. 2017

Front. Bioeng. Biotechnol.

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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.

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Linear and Nonlinear Kinematic Synergies in the Grasping Hand

Cited by 22 publications

References 35 publications

Sharing of hand kinematic synergies across subjects in daily living activities

Sharing of hand kinematic synergies across subjects in daily living activities

Unexpected complexity of everyday manual behaviors

Low-Dimensional Synergistic Representation of Bilateral Reaching Movements

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