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Thumb-finger opposition effects. Left: automatic opposition by oblique finger flexion caused by inclined flexion-extension axes in the MCP, PIP and DIP joints. Right: hollowing of the palm by movement of the metacarpal heads in the CMC joints, with respect to the frontal plane (anteriorly) and slightly to the side (laterally).
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For the purpose of ergonomic human-machine interaction and geometrical design of hand held haptic devices, a kinematic model that represents the functional anatomy of different human hands is desired. It is the goal of this paper to present a kinematic hand model that is based on human physiology and that is easily adaptable to represent various re...
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... opposition: During flexion, the fingers are di- rected towards the same point (the radial pulse) as shown in the left half of Fig. 2. This effect presents the pulp of the fingers to that of the thumb and to the object to grasp. The result is an increased contact surface contributing to the strengthening of the ...
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... effects of inward finger flexion and oblique finger segment flexion increase from the index finger to the little finger, as also shown in the left half of Fig. ...
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... hollowing of the palm: The heads of the metacarpal bones located in the CMC joints move with respect to the frontal plane (anteriorly) and slightly to the side (laterally). As illustrated in the right half of Fig. 2, this effect increases from the index finger (where it is negligible) to the little finger, causing hollowing of the ...
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... MCP axes inclination angles φ mcp are assumed such that all fingers are directed towards the radial pulse when flexed (left half of Fig. 2). The MCP axis inclination angles are assumed: -6.8 • , 3.6 • , 13.8 • , 23.9 • for the index-to the little finger, as proposed in [7]. The CMC axes inclination angles φ cmc of the ring-and little finger are assumed equal to their φ mcp ...
Citations
... According to the analysis of the human anatomy [1], the thumb has the most independent muscle groups compared to the other four digits which cause different features of kinematics and dynamics. The function of the thumb evaluated by Hart et al. [2] also shows that the loss of the thumb corresponds to a loss of 40% function. ...
Although various advanced anthropomorphic hands have been proposed, the design and fabrication of thumb joints to achieve dexterity function in a limited space is still a great challenge. To promote the dexterity of the anthropomorphic thumb joints in the cramped space of the palm, we present a fabric-based flexible actuator used for thumb joints to realize two degrees of freedom (DOFs): circumduction and adduction. First, we introduce the design concept inspired by muscle groups of the human palm. A kind of pneumatic small-scale actuators that relies on fabric-crease to produce deformation is proposed. Then the properties of flexible lightweight actuators are characterized. The actuators just need ± 10 kPa pressure to realize a flexion range of 90°. Finally, we modularly assemble the actuators to construct a compact two DOFs thumb joints. Experimental results show that the applied fabric-based thumb joints can achieve dexterous manipulation like sliding the smartphone screen.KeywordsFabric-based flexible actuatorSoft
anthropomorphic handsthumb jointsBio-inspired designSmall-scaleDexterous manipulation
... Tremendous grasp functionality is one of the critical characteristics of humans [1][2][3][4]. More than 20 degrees of freedom (DoFs) [5][6][7] are coordinately actuated by multiple extrinsic and intrinsic muscles [8] and controlled by a huge amount of neural resources [9]. Any bone, muscle, and nerve damage often leads to impairment of hand movement, which will seriously affect the quality of life [10], such as stroke, Parkinson's disease (PD), or physical injury. ...
Modeling and understanding human grasp functionality are fundamental in prosthetics, robotics, medicine, and rehabilitation, since they contribute to exploring motor control mechanism, evaluating grasp function, and designing and controlling prosthetic hands or exoskeletons. However, there are still limitations in providing a comprehensive and quantitative understanding of hand grasp functionality. After simultaneously considering three significant and essential influence factors in daily grasping contained relative position, object shape, and size, this paper presents the tolerance grasping to provide a more comprehensive understanding of human grasp functionality. The results of joint angle distribution and variance explained by PCs supported that tolerance grasping can represent hand grasp functionality more comprehensively. Four synergies are found and account for 93%±1.5% of the overall variance. The ANOVA confirmed that there was no significant individual difference in the first four postural synergies. The common patterns of grasping behavior were found and characterized by the mean value of postural synergy across 10 subjects. The independence analysis demonstrates that the tolerance grasping results highly correlate with unstructured natural grasping and more accurately correspond to cortical representation size of finger movement. The potential for exploring the neuromuscular control mechanism of human grasping is discussed. The analysis of hand grasp characteristics that contained joint angle distribution, correlation, independence, and postural synergies, presented here, should be more representative to provide a more comprehensive understanding of hand grasp functionality.
... e thumb only consists of proximal and distal phalanges; it does not have a medial phalange. e remaining eight hand bones are the carpals that are located in the wrist [17]. e name of each joint is based on the bones they linked. ...
... Previous studies discovered that neurophysiological phenomena called event-related desynchronization (ERD) or synchronization (ERS) are detectable from EEG signals when motor imagery is performed [29]. ERD or ERS is also a high-frequency band-specific [30] but can be observed from mu rhythms (µ) (8)(9)(10)(11)(12) or beta rhythms (ß) (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) of the EEG signals [31]. e amplitude of the EEG rhythms is about 100 µV when measured on the scalp and about 1-2 mV when measured from the surface of the human brain. ...
More than one billion people face disabilities worldwide, according to the World Health Organization (WHO). In Sri Lanka, there are thousands of people suffering from a variety of disabilities, especially hand disabilities, due to the civil war in the country. e Ministry of Health of Sri Lanka reports that by 2025, the number of people with disabilities in Sri Lanka will grow by 24.2%. In the field of robotics, new technologies for handicapped people are now being built to make their lives simple and effective. e aim of this research is to develop a 3-finger anatomical robot hand model for handicapped people and control (flexion and extension) the robot hand using motor imagery. Eight EEG electrodes were used to extract EEG signals from the primary motor cortex. Data collection and testing were performed for a period of 42 s timespan. According to the test results, eight EEG electrodes were sufficient to acquire the motor imagery for flexion and extension of finger movements. e overall accuracy of the experiments was found at 89.34% (mean = 22.32) at the 0.894 precision. We also observed that the proposed design provided promising results for the performance of the task (grab, hold, and release activities) of hand-disabled persons.
... The virtual hand consisted of a graphical model from the Dexmo SDK and a skeleton that included joint limits. Bio-mechanical models of the hand are available [9,15,52], but for this project we calibrated ours by eye as the skeleton itself was not physically correct. The normalised parameters from the Dexmo sensors directly interpolated joint rotations between their limits. ...
Grounded haptic devices can provide a variety of forces but have limited working volumes. Wearable haptic devices operate over a large volume but are relatively restricted in the types of stimuli they can generate. We propose the concept of docking haptics, in which different types of haptic devices are dynamically docked at run time. This creates a hybrid system, where the potential feedback depends on the user's location. We show a prototype docking haptic workspace, combining a grounded six degree-of-freedom force feedback arm with a hand exoskeleton. We are able to create the sensation of weight on the hand when it is within reach of the grounded device, but away from the grounded device, hand-referenced force feedback is still available. A user study demonstrates that users can successfully discriminate weight when using docking haptics, but not with the exoskeleton alone. Such hybrid systems would be able to change configuration further, for example docking two grounded devices to a hand in order to deliver twice the force, or extend the working volume. We suggest that the docking haptics concept can thus extend the practical utility of haptics in user interfaces.
... Most hand modelling approaches consider the hand as a mechanism formed by independent rigid bodies connected to the palm, which represents an extra rigid link [2]. The palm itself is a flexible body comprised of a set of connected rigid bodies [3] and has a crucial role in finger and thumb motion. The current literature does not consider a potential of detecting palm to improve accuracy of hand tracking. ...
... Thumb, however, which plays a crucial role in human manipulation capability, consists of two phalanges followed by a Metacarpal bone attached to Carpus [18]. Various approaches have been put forward to model hand/finger kinematics [26,27]. In this study, we are mainly interested to model middle finger, index finger and thumb, therefore we utilize simple model proposed in [27], which can be seen in Fig. 1. ...
... Having all the required joint angles allow calculation of fingertip positions for the two fingers and the thumb using forward kinematics [26]. Denavit-Hartenberg (DH) parameters of the fingers and the thumb and forward kinematics calculations are used according to SynGrasp toolbox [28]. ...
Robot Assisted Surgery is attracting increasing amount of attention as it offers numerous benefits to patients as well as surgeons. Heart surgery requires a high level of precision and dexterity, in contrast to other surgical specialties. Robot assisted heart surgery is not as widely performed due to numerous reasons including a lack of appropriate and intuitive surgical interfaces to control minimally invasive surgical tools. In this paper, finger motion of the surgeon is analyzed during cardiac surgery tasks on an ex-vivo animal model with the purpose of designing a more intuitive master console. First, a custom finger tracking system is developed using IMU sensors, which is lightweight and comfortable enough to allow free movement of the surgeon’s fingers/hands while using instruments. The proposed system tracks finger joint angles and fingertip positions for three involved fingers (thumb, index, middle). Accuracy of the IMU sensors has been evaluated using an optical tracking system (Polaris, NDI). Finger motion of the cardiac surgeon while using a Castroviejo instrument is studied in suturing and knotting scenarios. The results show that PIP and MCP joints have larger Range Of Motion (ROM), and faster rate of change compared to other finger/thumb joints, while thumb has the largest Fingertip WorkSpace (FWS) of all three digits.
... Some characteristics can be modelled reliably, while for others (most commonly the Carpometacarpal (CMC) projections), no models exist and there are only exemplary measurements. We could not find a model of the human hand, parameterised entirely by DH-parameters from the wrist, so we created one based on a number of models presented in the literature [33] [34] [31]. The complete model is provided in the supplementary materials and our code 1 . ...
... [35] [36] [30] [37]). For example, the human palm is capable of hollowing [33]. We have included the joints necessary for this in our virtual hand. ...
The Dexmo glove is a haptic exoskeleton that provides kinesthetic feedback in Virtual Reality. Unlike many other gloves based on string-pulleys, the Dexmo uses a free-hinged link-bar to transfer forces from a crank to the fingertips. It also uses an admittance-based controller parameterised by position, as opposed to an impedance-based controller parameterised by force. When setting the controller's target position, developers must use its native angular coordinate system. The Dexmo has a number of uninstrumented degrees-of-freedom. Mature forward models can reliably predict the hand pose, even with these unknowns. When it comes to computing angular controller parameters from a target pose in Cartesian space however, things become more difficult. Complex models that provide attractive visuals from a small number of sensors can be non-trivial or even impossible to invert. In this paper we suggest side-stepping this issue. We sample the forward model in order to build a lookup table. This is embedded in 3D space as a curve, on which traditional queries against world geometry can be performed. Controller parameters are stored as attributes of the sample points. To compute the driver parameters for a target position, the application constrains the position to the geometry, and interpolates them. This technique is generalisable, stable, simple, and fast. We validate our approach by implementing it in Unity 2017.3 and integrating it with a Dexmo glove.
... The range of motion represents the active motion range of each joint of thumb and fingers according to Ref. [38]. After a thorough comparison of the kinematic models proposed in the literature [41][42][43][44][45][46][47][48][49] , inspired by Bullock's critical review [49] we finally utilized the Hollister's the thumb kinematic model [41,42] and Stillfried's palm and finger kinematic model [43] as our rotation axis and DOF configuration evaluation criteria. For coupling speed ratio, we proposed the joint coupling speed ratio based on the Kamper's research throughout the grasp trials with a Cyber-Glove (Immersion Corp., San Jose, CA) [50] . ...
... We put each index of Table 3 in a 21-dimensional vector, with satisfied elements setting to 1 (otherwise 0). After a thorough comparison of the kinematic models proposed in the literature [41][42][43][44][45][46][47][48][49] , inspired by Bullock's critical review [49] we finally utilized the Hollister's the thumb kinematic model [41,42] and Stillfried's palm and finger kinematic model [43] as our DOF configuration evaluation criterion. Thus, in our DOF configuration evaluation, the thumb contains five DOFs (each of carpometacarpal (CMC) and MCP joint has two, DIP joint has one); each finger contains four DOFs (MCP joint has two, each of PIP and DIP joint has one). ...
The mimic of aesthetics, function, and rehabilitation application makes the prosthetic hand design an interdisciplinary, synthetic work. Prosthetic hands should be designed in a comprehensive consideration with a synthetic framework from multiple areas. In this case, a synthetic framework containing 12 anthropomorphism indexes is established and utilized to understand the human hand characteristic and quantifiably evaluate the anthropomorphism of a prosthetic hand. Our quantified evaluation results show that a Global Anthropomorphic Score (GAS) of the current commercial prosthetic hands is only 45.2%. The compliance, coupling speed ratio and configuration of the Degrees Of Freedom (DOF) are found to be the lowest three anthropomorphism evaluation indexes in all 12 indexes. In addition, a design priority is proposed based on the quantified evaluation results and contributes to a prosthetic hand design. Moreover, our correlation analysis results between each index and GAS show that, compared with the conventional evaluation index-grasp gesture, the rotation axis distribution index has a stronger distinguishing capability to the hand performance. Finally, a flowchart of prosthetic hand design was presented for a designer to design a prosthetic hand with a high anthropomorphism.
... Generally, the design and dimensions of hand prostheses are not sufficiently individualized; only the design of the socket depends on the level of amputation of the patient. The size of the phalanges is scaled according to hand length and breadth [26], [27], based on the assumption that normal hands maintain anatomical structure and dimensional proportions, regardless of their physical size. The proposal consists in extracting all the parameters to fill the Table 1 (dimensions, positions and orientations), from two x-ray images of the patient's healthy hand (upper and lateral views). ...
Commercially available prostheses do not meet patients' expectations regarding dimensions, shape and aesthetic reasons. This paper presents the design of an anthropomorphic and anthropometric prosthesis based on dimensions and shape extracted from a digitized radiograph of the healthy hand of the patient. The mobility of the hand is guaranteed by making a design of phalanges, joints, ligaments and tendons, as closest as possible to a real hand. The kinematic model is presented and validated. A proposal of the actuation system is also described.
... They suggest further studies to see whether this behavior happens due to neural control or as a result of finger positions. According to van der Hulst et al. (2012), finger joints' coordinated flexion motion toward the palm causes automatic opposition and cupping of the palm against the thumb. Experimental results on the contribution of palmar arches in doing six common daily activities in Richards et al. (2015) show that the oblique arch (articular formation of thumb MCP and little finger MCP) contributes the most. ...
... This model is not sufficient to predict kinematics of the thumb in power grasp due to combined F-E and A-A motions at the TM joint as pointed out in Section 3.1. In contrast, MCP joint has three DOFs (A-A, F-E, and axial rotation around the thumb proximal phalanx), while TM has 2-DOFs (orthogonal and non-intersecting) and IP has a single DOF in van der Hulst et al. (2012). A kinematic thumb model is developed in Cui et al. (2011) to match the sensor positions of the Cyberglove 1 with the human thumb joints for teleoperation applications. ...
The unique musculoskeletal structure of the human hand brings in wider dexterous capabilities to grasp and manipulate a repertoire of objects than the non-human primates. It has been widely accepted that the orientation and the position of the thumb plays an important role in this characteristic behavior. There have been numerous attempts to develop anthropomorphic robotic hands with varying levels of success. Nevertheless, manipulation ability in those hands is to be ameliorated even though they can grasp objects successfully. An appropriate model of the thumb is important to manipulate the objects against the fingers and to maintain the stability. Modeling these complex interactions about the mechanical axes of the joints and how to incorporate these joints in robotic thumbs is a challenging task. This article presents a review of the biomechanics of the human thumb and the robotic thumb designs to identify opportunities for future anthropomorphic robotic hands.
