The Role of the Thumb: Study of Finger Motion in Grasping and Reachability Space in Human and Robotic Hands

Cotugno, Giuseppe; Althoefer, Kaspar; Nanayakkara, Thrishantha

doi:10.1109/tsmc.2016.2531679

Cited by 36 publications

(13 citation statements)

References 33 publications

(39 reference statements)

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“…Our results also found that most of the finger preshaping is performed in this phase. This finding is in line with what is observed by [49] and it complements our previous work [58] which describes how fingers are displacing for grasping. It is possible to observe that the precise approaching to grasp strategy is decided by the end of this phase.…”

Section: Discussionsupporting

confidence: 93%

Modelling the structure of object-independent human affordances of approaching to grasp for robotic hands

et al. 2018

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Grasp affordances in robotics represent different ways to grasp an object involving a variety of factors from vision to hand control. A model of grasp affordances that is able to scale across different objects, features and domains is needed to provide robots with advanced manipulation skills. The existing frameworks, however, can be difficult to extend towards a more general and domain independent approach. This work is the first step towards a modular implementation of grasp affordances that can be separated into two stages: approach to grasp and grasp execution. In this study, human experiments of approaching to grasp are analysed, and object-independent patterns of motion are defined and modelled analytically from the data. Human subjects performed a specific action (hammering) using objects of different geometry, size and weight. Motion capture data relating the hand-object approach distance was used for the analysis. The results showed that approach to grasp can be structured in four distinct phases that are best represented by non-linear models, independent from the objects being handled. This suggests that approaching to grasp patterns are following an intentionally planned control strategy, rather than implementing a reactive execution.

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Section: Discussionsupporting

confidence: 93%

Modelling the structure of object-independent human affordances of approaching to grasp for robotic hands

et al. 2018

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“…While thumb used for searching the object, where the position of placing an object is not disclosed and results for algorithm as follows. In this case, object found using box-based method [19]. Figure 14, 15 understands about the finger movement in a 3D space with total iterations; which helps determining whether object has been found or not and also shows where it has been checking for the object.…”

Section: Resultsmentioning

confidence: 99%

“…The process of grasp [19] is necessary to ensure the object safely held, neither too loose, nor too tight. This process may require all the capacitive sensors to be activated or may depend on the type of grasp coded into the machine based on the shape detected.…”

Section: Graspmentioning

confidence: 99%

“…In detail, F a is the maximum force module and α i,j ∈ (0, 1] is a strictly decreasing sequence of scale factors defined as α i,j (k + 1) = εα i,j (k) (13) α i,j (0) = 1 (14) where ε ∈ (0, 1) and a new value of the sequence is computed every time point z i,j comes out from V. Collecting some terms in the previous dynamic equation of the system, a more compact expression is I,j + k(4 ) = (15) Where, in the expressions, the points with subscript coincides with those with subscripts, respectively. (16) The two poles have to be treated distinctly, due to topological differences, np + (17) For the north pole (np), and sp + (18) For the south pole (sp), (19) Where, is the force vector, is the projection of force , and is the normal unit vector to the surface at point p i . During the action of grasp, the contact points do not always belong to the same plane.…”

Section: Az≤d (7)mentioning

confidence: 99%

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Robotic Pick and Place using Two-Finger Touch Sensing

Kondaveti¹,

Venugopal²,

Rai³

2019

IJITEE

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Pick and place robots are gaining popularity in the current era due to their ability to handle objects of varying sizes and dimensions. Research to improve the efficiency and design of these manipulators is currently the need of the hour. In this, a new and novel mechanism has been introduced; it is called Blind man Mechanism that is to find the object and to find shape of it. Shape Recognition algorithm to improve the hold on the object. It involves analysis of working of the human thumb and shape of the object. The Blind man mechanism allows object to be found, even when it is not in its usual position. It includes details about the thumb analysis to find an object, which further includes shape recognition of different objects in multiple scenarios also results for whether the object has been found or not. If found so verify for a pick place robot using MATLAB results

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“…Kong et al [11] and Prasad and Manivannan [12] reported that the perception of handgrip force differed according to the handedness. Therefore, while substantial research has already been conducted on grip strength, very few studies investigated thumb force exertion levels and self-reporting estimates, even though the thumb plays a major role in various manual tasks [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Gender-Based Differences in Actual Thumb Force Exertions at Various Target Force Levels

et al. 2019

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Measurement of thumb force in the industrial filed is important to quantify the risk of manual tasks. However, few studies have assessed the thumb force. Therefore, the purpose of this study was to investigate the relationship between actual thumb force and perceived force according to the force levels and gender. A total of 31 healthy participants (23 males and 8 females) were asked to exert a maximum thumb force (MTF) and random levels of target thumb force (10, 30, 50, 70, and 90% MTF). Results indicated that females exerted approximately 73.6% (100.6 N) of the strength exerted by males (136.7 N). Based on the differences between actual and perceived thumb exertions, the highest accuracy was achieved at 50% MTF, designated as “50%-phenomenon”. At 10% and 30% MTF levels, participants tended to exert greater strength (over-exertion) than the perceived exertions, whereas participants showed less strength (under-exertion) than the perceived exertions at 70% and 90% MTFs. Participants generally exerted greater and lesser thumb force than the perceived exertions at levels lower and higher than 50% MTF, respectively. Thus, the relationship between the actual and perceived force of exertion displayed a nonlinear S-shaped curve.

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The Role of the Thumb: Study of Finger Motion in Grasping and Reachability Space in Human and Robotic Hands

Cited by 36 publications

References 33 publications

Modelling the structure of object-independent human affordances of approaching to grasp for robotic hands

Modelling the structure of object-independent human affordances of approaching to grasp for robotic hands

Robotic Pick and Place using Two-Finger Touch Sensing

Gender-Based Differences in Actual Thumb Force Exertions at Various Target Force Levels

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