Contact points during multidigit grasping of geometric objects

Eye and hand movements disrupt attentional control

Voluntary attentional control is the ability to selectively focus on a subset of visual information in the presence of other competing stimuli–a marker of cognitive control enabling flexible, goal-driven behavior. To test its robustness, we contrasted attentional control with the most common source of attentional orienting in daily life: attention shifts prior to goal-directed eye and hand movements. In a multi-tasking paradigm, human participants attended at a location while planning eye or hand movements elsewhere. Voluntary attentional control suffered with every simultaneous action plan, even under reduced task difficulty and memory load–factors known to interfere with attentional control. Furthermore, the performance cost was limited to voluntary attention: We observed simultaneous attention benefits at two movement targets without attentional competition between them. This demonstrates that the visual system allows for the concurrent representation of multiple attentional foci. Since attentional control is extremely fragile and dominated by premotor attention shifts, we propose that action-driven selection plays the superordinate role for visual selection.

Hand pose selection in a bimanual fine-manipulation task

Many daily tasks involve the collaboration of both hands. Humans dexterously adjust hand poses and modulate the forces exerted by fingers in response to task demands. Hand pose selection has been intensively studied in unimanual tasks, but little work has investigated bimanual tasks. This work examines hand poses selection in a bimanual high-precision-screwing task taken from watchmaking. Twenty right-handed subjects dismounted a screw on the watch face with a screwdriver in two conditions. Results showed that although subjects used similar hand poses across steps within the same experimental conditions, the hand poses differed significantly in the two conditions. In the free-base condition, subjects needed to stabilize the watch face on the table. The role distribution across hands was strongly influenced by hand dominance: the dominant hand manipulated the tool, whereas the nondominant hand controlled the additional degrees of freedom that might impair performance. In contrast, in the fixed-base condition, the watch face was stationary. Subjects used both hands even though single hand would have been sufficient. Importantly, hand poses decoupled the control of task-demanded force and torque across hands through virtual fingers that grouped multiple fingers into functional units. This preference for bimanual over unimanual control strategy could be an effort to reduce variability caused by mechanical couplings and to alleviate intrinsic sensorimotor processing burdens. To afford analysis of this variety of observations, a novel graphical matrix-based representation of the distribution of hand pose combinations was developed. Atypical hand poses that are not documented in extant hand taxonomies are also included.NEW & NOTEWORTHY We study hand poses selection in bimanual fine motor skills. To understand how roles and control variables are distributed across the hands and fingers, we compared two conditions when unscrewing a screw from a watch face. When the watch face needed positioning, role distribution was strongly influenced by hand dominance; when the watch face was stationary, a variety of hand pose combinations emerged. Control of independent task demands is distributed either across hands or across distinct groups of fingers.

Effect of Number of Digits on Human Precision Manipulation Workspaces

Precision manipulation, or moving small objects held in the fingertips, is likely the most heavily utilized class of dexterous within-hand manipulation and adds greatly to the capabilities of the human hand. This article focuses on studying the effects of varying the number of digits used on the resulting manipulation abilities, in terms of translational workspaces and rotational ranges, by manipulating two circular objects, 50 mm and 80 mm in diameter. In general, as the number of digits in contact with the object increases, the results show a significant reduction in precision manipulation workspace range for four of the six translation and rotation directions and no significant change in the other two, suggesting that for these particular metrics, more fingers result in a reduction in performance. Furthermore, while two digits results in the largest workspaces for five of the six translation and rotation axes, the lack of ability to control rotation in the distal-proximal direction suggests that three digits may be more desirable for overall precision manipulation dexterity.

Two hands are better than one: Perceptual benefits by bimanual movements

Before looking at or reaching for an object, the focus of attention is first allocated to the movement object. Here we investigated whether the strength of these pre-motor shifts of attention cumulates if an object is targeted by multiple effectors (eyes and hands). A total of 29 participants were tested on a visuomotor task. They were cued to move gaze, the left hand, right hand, or both (one to three effectors) to a common object or to different peripheral objects. Before the movements, eight possible objects briefly changed form, of which one was a distinct probe. Results showed that the average recognition of the probe's identity change increased as more effectors targeted this object. For example, performance was higher when two hands as compared to one hand were moved to the probe. This effect remained evident despite the detrimental effect on performance of the increase in motor task complexity of moving two hands as compared to one hand. The accumulation of recognition improvements as a function of the number of effectors that successfully target the probe points at parallel and presumably independent mechanisms for hand- and eye-coordination that evoke pre-motor shifts of attention.

Predicting precision grip grasp locations on three-dimensional objects

We rarely experience difficulty picking up objects, yet of all potential contact points on the surface, only a small proportion yield effective grasps. Here, we present extensive behavioral data alongside a normative model that correctly predicts human precision grasping of unfamiliar 3D objects. We tracked participants' forefinger and thumb as they picked up objects of 10 wood and brass cubes configured to tease apart effects of shape, weight, orientation, and mass distribution. Grasps were highly systematic and consistent across repetitions and participants. We employed these data to construct a model which combines five cost functions related to force closure, torque, natural grasp axis, grasp aperture, and visibility. Even without free parameters, the model predicts individual grasps almost as well as different individuals predict one another's, but fitting weights reveals the relative importance of the different constraints. The model also accurately predicts human grasps on novel 3D-printed objects with more naturalistic geometries and is robust to perturbations in its key parameters. Together, the findings provide a unified account of how we successfully grasp objects of different 3D shape, orientation, mass, and mass distribution.

Hands Holding Clues for Object Recognition in Teachable Machines

Camera manipulation confounds the use of object recognition applications by blind people. This is exacerbated when photos from this population are also used to train models, as with teachable machines, where out-of-frame or partially included objects against cluttered backgrounds degrade performance. Leveraging prior evidence on the ability of blind people to coordinate hand movements using proprioception, we propose a deep learning system that jointly models hand segmentation and object localization for object classification. We investigate the utility of hands as a natural interface for including and indicating the object of interest in the camera frame. We confirm the potential of this approach by analyzing existing datasets from people with visual impairments for object recognition. With a new publicly available egocentric dataset and an extensive error analysis, we provide insights into this approach in the context of teachable recognizers.

Effect of Object and Task Properties on Bimanual Transport

The use of both hands simultaneously when manipulating objects is fairly commonplace, but it is not known what factors encourage people to use two hands as opposed to one during simple tasks such as transport. In particular, we are interested in three possible transport strategies: unimanual transport, handing off between hands, and symmetric bimanual transport. In this study, we investigate the effect of object size, weight, and starting and ending position (configuration) as well as the need to balance the object on the use of these three strategies in a bowl-moving task. We find that configuration and balance have a strong effect on choice of strategy, and size and weight have a weaker effect. Hand-offs are most often used when the task requires moving an object from left to right and vice versa, while the unimanual strategy was frequently used when passing front to back. The bimanual strategy is only weakly affected by configuration. The need to balance an object causes subjects to favor unimanual and bimanual strategies over the hand-off. In addition, an analysis of transport duration and body rotation suggests that strategy choice may be driven by the desire to minimize body rotation.

Human precision manipulation workspace: Effects of object size and number of fingers used

Precision manipulation, or moving small objects in the fingertips, is important for daily tasks such as writing and key insertion, as well as medically relevant tasks such as scalpel cuts and surgical teleoperation. While fingertip force coordination has been studied in some detail, few previous works have experimentally studied the kinematics of human precision manipulation with real objects. The present work focuses on studying the effects of varying object size and the number of fingers used on the resulting manipulation workspace, or range of motions that the object can be moved through. To study object size effects, seven bar-shaped objects ranging from 20 to 80 mm length were tested; after scaling object length to the equivalent for a 17.5 cm hand, the peak volume was obtained for 48-59 mm object length range (23% above average), and the minimum volume was obtained for the smallest 17-27 mm range (72% of average). 50 mm and 80 mm circular objects were used to study the effect of using different numbers of fingers; the five-finger manipulation volume dropped to less than half the two-finger volume (p<;0.001). We anticipate these results will be useful in designing devices such as hand held tools, as well as in designing protocols for effectively testing and rehabilitating hand function. Finally, the results can provide a benchmark for the manipulation capability of prosthetic hands.

Rotational ranges of human precision manipulation when grasping objects with two to five digits

The ability to move and manipulate objects within the hand is important for the overall performance of the human hand. Such movements are key for many tasks, including writing, using precision tools, turning knobs, and operating various haptic interfaces. In this work we analyze the ability of 17 unimpaired subjects to rotate objects 50 and 80 mm in diameter using 2 to 5 digits, while maintaining the initial finger-object contact locations. Subjects were asked to rotate the object with a particular number of fingers around one of three orthogonal hand axes for 30 seconds and explore their rotational range. The average rotational range achieved over all conditions was 47 degrees, with the largest rotation of 82 degrees for the 3 digit case around a distal-proximal axis. The rotations around the palmar-dorsal and the ulnar-radial axes showed similar trends, where the smaller object resulted in 1.3 and 1.2 times larger rotation workspaces than the larger object (p <; 0.001), respectively. The rotation around the distal-proximal axis has a different trend, where the difference in rotation amplitude between different number of finger conditions is over 50% (p <; 0.003), but the difference in object size conditions is only 10%. The results highlight that the orientation of the rotation axis has significant influence on the rotation capabilities of the human hand. In designing handheld tools and haptic devices one should carefully consider around which axes a rotation is required.

Workspace Shape and Characteristics for Human Two- and Three-Fingered Precision Manipulation

GOAL

To study precision manipulation, which involves repositioning an object in the fingertips and is used in everyday tasks such as writing and key insertion, and also for domain-specific tasks such as small scalpel cuts, using tweezers, and hand soldering.

METHODS

In this study, the range of positions (workspace) through which 19 participants manipulated a 3.3-4.1 cm-diameter object are measured with a magnetic tracker. Each participant performed two conditions: a two-finger thumb-index finger condition and a three-finger thumb-index-middle finger condition.

RESULTS

The observed workspaces, normalized to a 17.5 cm hand length, are small compared to free-finger trajectories; for the two-finger trials, 68% of points are within 1.05 cm of the centroid and 95% are within 2.31 cm, while the three-finger case shows a narrower distribution, with 68% of points within 0.94 cm of the centroid and 95% of points within 2.19 cm. The longest axis is a long thin arc in the proximal-palmar plane. Analysis of fingertip workspaces shows that the index fingertip workspace volume is the most linear predictor of object workspace (R(2) = 0.98).

CONCLUSION

Precision manipulation workspace size and shape is shown, along with how the fingers are used during the manipulation.

SIGNIFICANCE

The results have many applications, including normative data for rehabilitation, guidelines for ergonomic device design, and benchmarking prosthetic and robotic hands.

Learned manipulation at unconstrained contacts does not transfer across hands

Recent studies about sensorimotor control of the human hand have focused on how dexterous manipulation is learned and generalized. Here we address this question by testing the extent to which learned manipulation can be transferred when the contralateral hand is used and/or object orientation is reversed. We asked subjects to use a precision grip to lift a grip device with an asymmetrical mass distribution while minimizing object roll during lifting by generating a compensatory torque. Subjects were allowed to grasp anywhere on the object’s vertical surfaces, and were therefore able to modulate both digit positions and forces. After every block of eight trials performed in one manipulation context (i.e., using the right hand and at a given object orientation), subjects had to lift the same object in the second context for one trial (transfer trial). Context changes were made by asking subjects to switch the hand used to lift the object and/or rotate the object 180° about a vertical axis. Therefore, three transfer conditions, hand switch (HS), object rotation (OR), and both hand switch and object rotation (HS+OR), were tested and compared with hand matched control groups who did not experience context changes. We found that subjects in all transfer conditions adapted digit positions across multiple transfer trials similar to the learning of control groups, regardless of different changes of contexts. Moreover, subjects in both HS and HS+OR group also adapted digit forces similar to the control group, suggesting independent learning of the left hand. In contrast, the OR group showed significant negative transfer of the compensatory torque due to an inability to adapt digit forces. Our results indicate that internal representations of dexterous manipulation tasks may be primarily built through the hand used for learning and cannot be transferred across hands.

Analysis of human grasping behavior: object characteristics and grasp type

This paper is the first of a two-part series analyzing human grasping behavior during a wide range of unstructured tasks. The results help clarify overall characteristics of human hand to inform many domains, such as the design of robotic manipulators, targeting rehabilitation toward important hand functionality, and designing haptic devices for use by the hand. It investigates the properties of objects grasped by two housekeepers and two machinists during the course of almost 10,000 grasp instances and correlates the grasp types used to the properties of the object. We establish an object classification that assigns each object properties from a set of seven classes, including mass, shape and size of the grasp location, grasped dimension, rigidity, and roundness. The results showed that 55 percent of grasped objects had at least one dimension larger than 15 cm, suggesting that more than half of objects cannot physically be grasped using their largest axis. Ninety-two percent of objects had a mass of 500 g or less, implying that a high payload capacity may be unnecessary to accomplish a large subset of human grasping behavior. In terms of grasps, 96 percent of grasp locations were 7 cm or less in width, which can help to define requirements for hand rehabilitation and defines a reasonable grasp aperture size for a robotic hand. Subjects grasped the smallest overall major dimension of the object in 94 percent of the instances. This suggests that grasping the smallest axis of an object could be a reliable default behavior to implement in grasp planners.

Haptic-motor transformations for the control of finger position

Dexterous manipulation relies on modulation of digit forces as a function of digit placement. However, little is known about the sense of position of the vertical distance between finger pads relative to each other. We quantified subjects' ability to match perceived vertical distance between the thumb and index finger pads (d_y) of the right hand (“reference” hand) using the same or opposite hand (“test” hand) after a 10-second delay without vision of the hands. The reference hand digits were passively placed non-collinearly so that the thumb was higher or lower than the index finger (d_y = 30 or –30 mm, respectively) or collinearly (d_y = 0 mm). Subjects reproduced reference hand d_y by using a congruent or inverse test hand posture while exerting negligible digit forces onto a handle. We hypothesized that matching error (reference hand d_y minus test hand d_y) would be greater (a) for collinear than non-collinear d_ys, (b) when reference and test hand postures were not congruent, and (c) when subjects reproduced d_y using the opposite hand. Our results confirmed our hypotheses. Under-estimation errors were produced when the postures of reference and test hand were not congruent, and when test hand was the opposite hand. These findings indicate that perceived finger pad distance is reproduced less accurately (1) with the opposite than the same hand and (2) when higher-level processing of the somatosensory feedback is required for non-congruent hand postures. We propose that erroneous sensing of finger pad distance, if not compensated for during contact and onset of manipulation, might lead to manipulation performance errors as digit forces have to be modulated to perceived digit placement.

Neural bases of hand synergies

The human hand has so many degrees of freedom that it may seem impossible to control. A potential solution to this problem is “synergy control” which combines dimensionality reduction with great flexibility. With applicability to a wide range of tasks, this has become a very popular concept. In this review, we describe the evolution of the modern concept using studies of kinematic and force synergies in human hand control, neurophysiology of cortical and spinal neurons, and electromyographic (EMG) activity of hand muscles. We go beyond the often purely descriptive usage of synergy by reviewing the organization of the underlying neuronal circuitry in order to propose mechanistic explanations for various observed synergy phenomena. Finally, we propose a theoretical framework to reconcile important and still debated concepts such as the definitions of “fixed” vs. “flexible” synergies and mechanisms underlying the combination of synergies for hand control.