Temporal Postural Synergies of the Hand in Rapid Grasping Tasks

Vinjamuri, Ramana; Sun, Mingui; Chang, Cheng-Chun; Lee, Heung-No; Sclabassi, R.J.; Mao, Zhi-Hong

doi:10.1109/titb.2009.2038907

Cited by 43 publications

(40 citation statements)

References 19 publications

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“…In order to develop a comprehensive study of human upper limb movements, one of the key features for the generation of a valid dataset is the definition of a set of meaningful actions (Santello et al, 1998;Mason et al, 2001;Todorov and Ghahramani, 2004;Vinjamuri et al, 2010). For this reason, we selected a set of movements driven by the study of grasping taxonomies (Cutkosky, 1989;Feix et al, 2016), and the analysis of human upper limb movement workspace (Lenarcic and Umek, 1994;Abdel-Malek et al, 2004;Perry et al, 2007).…”

Section: A Set Of Daily Living Tasksmentioning

confidence: 99%

Unvealing the Principal Modes of Human Upper Limb Movements through Functional Analysis

et al. 2017

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The rich variety of human upper limb movements requires an extraordinary coordination of different joints according to specific spatio-temporal patterns. However, unvealing these motor schemes is a challenging task. Principal components have been often used for analogous purposes, but such an approach relies on hypothesis of temporal uncorrelation of upper limb poses in time. To overcome these limitations, in this work, we leverage on functional principal component analysis (fPCA). We carried out experiments with 7 subjects performing a set of most significant human actions, selected considering state-of-the-art grasp taxonomies and human kinematic workspace. fPCA results show that human upper limb trajectories can be reconstructed by a linear combination of few principal time-dependent functions, with a first component alone explaining around 60/70% of the observed behaviors. This allows to infer that in daily living activities humans reduce the complexity of movement by modulating their motions through a reduced set of few principal patterns. Finally, we discuss how this approach could be profitably applied in robotics and bioengineering, opening fascinating perspectives to advance the state of the art of artificial systems, as it was the case of hand synergies.

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Section: A Set Of Daily Living Tasksmentioning

confidence: 99%

Unvealing the Principal Modes of Human Upper Limb Movements through Functional Analysis

et al. 2017

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“…The first two principal components accounted for ~84% of the variance over 57 objects grasp postures. Following the research of Santello, hand postures in reach-to-grasp action [20], angular velocities of finger joints [21] [22] [23] [24], hand postures in bimanual manipulation of various objects [25] can also be described by a small number of postural synergies. However, on previous investigations of postural synergies, the tolerance grasping of various objects is not presented.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Hand and Wrist Postural Synergies in Tolerance Grasping of Various Objects

2016

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Human can successfully grasp various objects in different acceptable relative positions between human hand and objects. This grasp functionality can be described as the grasp tolerance of human hand, which is a significant functionality of human grasp. To understand the motor control of human hand completely, an analysis of hand and wrist postural synergies in tolerance grasping of various objects is needed. Ten healthy right-handed subjects were asked to perform the tolerance grasping with right hand using 6 objects of different shapes, sizes and relative positions between human hand and objects. Subjects were wearing CyberGlove attaching motion tracker on right hand, allowing a measurement of the hand and wrist postures. Correlation analysis of joints and inter-joint/inter-finger modules were carried on to explore the coordination between joints or modules. As the correlation between hand and wrist module is not obvious in tolerance grasping, individual analysis of wrist synergies would be more practical. In this case, postural synergies of hand and wrist were then presented separately through principal component analysis (PCA), expressed through the principal component (PC) information transmitted ratio, PC elements distribution and reconstructed angle error of joints. Results on correlation comparison of different module movements can be well explained by the influence factors of the joint movement correlation. Moreover, correlation analysis of joints and modules showed the wrist module had the lowest correlation among all inter-finger and inter-joint modules. Hand and wrist postures were both sufficient to be described by a few principal components. In terms of the PC elements distribution of hand postures, compared with previous investigations, there was a greater proportion of movement in the thumb joints especially the interphalangeal (IP) and opposition rotation (ROT) joint. The research could serve to a complete understanding of hand grasp, and the design, control of the anthropomorphic hand and wrist.

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“…The above classification was already presented in Vinjamuri et al (2010b). In addition to synergies proposed in postures, and kinematics which are of relevance to the current study, synergies were also proposed in muscle activities d 'Avella et al (2006).…”

Section: Synergiesmentioning

confidence: 59%