Analysis of hand synergies in healthy subjects during bimanual manipulation of various objects

Jarrassé, Nathanaël; Ribeiro, Adriano Tacilo; Sahbani, Anis; Bachta, Wael; Roby-Brami, Agnès

doi:10.1186/1743-0003-11-113

Cited by 46 publications

(52 citation statements)

References 40 publications

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“…Derived from the 19-dimensional glove sensor space, the first three PCs of each run explained 75 to 80 percent of the variance, and were thus salient according to the Gutmann Kaiser criterion [33]. This low dimensionality is consistent with the observations of Belic and Faisal; [22], Jarassé et al [43] and…”

Section: Discussionsupporting

confidence: 71%

Sensing form - finger gaiting as key to tactile object exploration: A data glove analysis of a prototypical daily task

Krammer

Missimer

Habegger

et al. 2020

Preprint

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Background Motor hand skill with associated dexterity is an important facility in meeting the challenges of daily activity and an important resource post-stroke. In this context, the present study investigated the finger movements of right-handed subjects during tactile manipulation of a cuboid, a prototypical component of tactile exploration. Methods For both hands of 22 subjects, we acquired the time series of consecutive multifinger cuboid manipulations using a digital data glove consisting of 29 sensors. Of these, 16 recorded the bending of metacarpo-phalangeal (MCP) and proximal interphalangeal (PIP) joints of the fingers, MCP and interphalangeal IP joints of the thumb, palm arch and carpo-metacarpal (CMC) joint of the thumb, and abduction between fingers. Results Using principle component analysis we decomposed the short action into motor patterns related to successive manipulations of the cuboid. The fraction of variance described by the principal components indicated that three components described the salient features of the single motor acts for each hand. Striking in the finger patterns was the prominent and varying roles of the MCP and PIP joints of the fingers, and the CMC joint of the thumb. An important aspect of the three components was their representation of distinct finger configurations within the same motor act. Principal component and graph theory analysis confirmed modular, functionally synchronous action of the involved joints. The computation of finger trajectories in one subject illustrated the workspace of the task, which differed for the right and left hands. Conclusion The study substantiates finger gaiting, described until now only in artificial systems, as the principal mechanism underlying this prototypical task, which is ubiquitous in daily object shape recognition.

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Section: Discussionsupporting

confidence: 71%

Sensing form - finger gaiting as key to tactile object exploration: A data glove analysis of a prototypical daily task

Krammer

Missimer

Habegger

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, it has been proposed that control of human hand movements is organized in a way that comprises coupling of several DOF into functional groups. The opinion that motor synergies lie behind manual actions has been supported by several studies (Santello et al, 1998, 2002; Daffertshofer et al, 2004; d'Avella et al, 2006; Tresch et al, 2006; Ingram et al, 2008; Faisal et al, 2010; Jarrassé et al, 2014). The most common interpretation is in terms of simplifying the strategy that the central nervous system might undertake.…”

Section: Discussionmentioning

confidence: 91%

Decoding of human hand actions to handle missing limbs in neuroprosthetics

Belić

Faisal

2015

Front. Comput. Neurosci.

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The only way we can interact with the world is through movements, and our primary interactions are via the hands, thus any loss of hand function has immediate impact on our quality of life. However, to date it has not been systematically assessed how coordination in the hand's joints affects every day actions. This is important for two fundamental reasons. Firstly, to understand the representations and computations underlying motor control “in-the-wild” situations, and secondly to develop smarter controllers for prosthetic hands that have the same functionality as natural limbs. In this work we exploit the correlation structure of our hand and finger movements in daily-life. The novelty of our idea is that instead of averaging variability out, we take the view that the structure of variability may contain valuable information about the task being performed. We asked seven subjects to interact in 17 daily-life situations, and quantified behavior in a principled manner using CyberGlove body sensor networks that, after accurate calibration, track all major joints of the hand. Our key findings are: (1) We confirmed that hand control in daily-life tasks is very low-dimensional, with four to five dimensions being sufficient to explain 80–90% of the variability in the natural movement data. (2) We established a universally applicable measure of manipulative complexity that allowed us to measure and compare limb movements across tasks. We used Bayesian latent variable models to model the low-dimensional structure of finger joint angles in natural actions. (3) This allowed us to build a naïve classifier that within the first 1000 ms of action initiation (from a flat hand start configuration) predicted which of the 17 actions was going to be executed—enabling us to reliably predict the action intention from very short-time-scale initial data, further revealing the foreseeable nature of hand movements for control of neuroprosthetics and tele operation purposes. (4) Using the Expectation-Maximization algorithm on our latent variable model permitted us to reconstruct with high accuracy (<5–6° MAE) the movement trajectory of missing fingers by simply tracking the remaining fingers. Overall, our results suggest the hypothesis that specific hand actions are orchestrated by the brain in such a way that in the natural tasks of daily-life there is sufficient redundancy and predictability to be directly exploitable for neuroprosthetics.

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“…Therefore, it has been proposed that control of human hand movements is organized in a way that comprises coupling of several DOF into functional groups. The opinion that motor synergies lie behind manual actions has been supported by several studies (Santello et al, 1998; Santello et al, 2002; Daffershofer et al, 2004; d’Avella et al, 2006; Tresch et al, 2006; Ingram et al, 2008; Faisal et al, 2010; Jarrasse et al, 2014). The most common interpretation is in terms of simplifying the strategy that the central nervous system might undertake.…”

Section: Discussionmentioning

confidence: 90%

Decoding of human hand actions to handle missing limbs in Neuroprosthetics

Belić

Faisal

2015

Preprint

View full text Add to dashboard Cite

The only way we can interact with the world is through movements, and our primary interactions are via the hands, thus any loss of hand function has immediate impact on our quality of life. However, to date it has not been systematically assessed how coordination in the hand's joints affects every day actions. This is important for two fundamental reasons. Firstly, to understand the representations and computations underlying motor control "in-the-wild" situations, and secondly to develop smarter controllers for prosthetic hands that have the same functionality as natural limbs. In this work we exploit the correlation structure of our hand and finger movements in daily-life. The novelty of our idea is that instead of averaging variability out, we take the view that the structure of variability may contain valuable information about the task being performed. We asked seven subjects to interact in 17 daily-life situations, and quantified behavior in a principled manner using CyberGlove body sensor networks that, after accurate calibration, track all major joints of the hand. Our key findings are: (1) We confirmed that hand control in daily-life tasks is very low-dimensional, with four to five dimensions being sufficient to explain 80-90% of the variability in the natural movement data. (2) We established a universally applicable measure of manipulative complexity that allowed us to measure and compare limb movements across tasks. We used Bayesian latent variable models to model the low-dimensional structure of finger joint angles in natural actions. (3) This allowed us to build a naïve classifier that within the first 1000 ms of action initiation (from a flat hand start configuration) predicted which of the 17 actions was going to be executed-enabling us to reliably predict the action intention from very short-time-scale initial data, further revealing the foreseeable nature of hand movements for control of neuroprosthetics and tele operation purposes. (4) Using the Expectation-Maximization algorithm on our latent variable model permitted us to reconstruct with high accuracy (<5-6 • MAE) the movement trajectory of missing fingers by simply tracking the remaining fingers. Overall, our results suggest the hypothesis that specific hand actions are orchestrated by the brain in such a way that in the natural tasks of daily-life there is sufficient redundancy and predictability to be directly exploitable for neuroprosthetics.

show abstract

Analysis of hand synergies in healthy subjects during bimanual manipulation of various objects

Cited by 46 publications

References 40 publications

Sensing form - finger gaiting as key to tactile object exploration: A data glove analysis of a prototypical daily task

Sensing form - finger gaiting as key to tactile object exploration: A data glove analysis of a prototypical daily task

Decoding of human hand actions to handle missing limbs in neuroprosthetics

Decoding of human hand actions to handle missing limbs in Neuroprosthetics

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