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.

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.

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.

Analysis of human grasping behavior: correlating tasks, objects and grasps.

This paper is the second in a two-part series analyzing human grasping behavior during a wide range of unstructured tasks. It investigates the tasks performed during the daily work of two housekeepers and two machinists and correlates grasp type and object properties with the attributes of the tasks being performed. The task or activity is classified according to the force required, the degrees of freedom, and the functional task type. We found that 46 percent of tasks are constrained, where the manipulated object is not allowed to move in a full six degrees of freedom. Analyzing the interrelationships between the grasp, object, and task data show that the best predictors of the grasp type are object size, task constraints, and object mass. Using these attributes, the grasp type can be predicted with 47 percent accuracy. Those parameters likely make useful heuristics for grasp planning systems. The results further suggest the common sub-categorization of grasps into power, intermediate, and precision categories may not be appropriate, indicating that grasps are generally more multi-functional than previously thought. We find large and heavy objects are grasped with a power grasp, but small and lightweight objects are not necessarily grasped with precision grasps-even with grasped object size less than 2 cm and mass less than 20 g, precision grasps are only used 61 percent of the time. These results have important implications for robotic hand design and grasp planners, since it appears while power grasps are frequently used for heavy objects, they can still be quite practical for small, lightweight objects.

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.

Grasp frequency and usage in daily household and machine shop tasks.

In this paper, we present results from a study of prehensile human hand use during the daily work activities of four subjects: two housekeepers and two machinists. Subjects wore a head-mounted camera that recorded their hand usage during their daily work activities in their typical place of work. For each subject, 7.45 hours of video was analyzed, recording the type of grasp being used and its duration. From this data, we extracted overall grasp frequency, duration distributions for each grasp, and common transitions between grasps. The results show that for 80 percent of the study duration the housekeepers used just five grasps and the machinists used 10. The grasping patterns for the different subjects were compared, and the overall top 10 grasps are discussed in detail. The results of this study not only lend insight into how people use their hands during daily tasks, but can also inform the design of effective robotic and prosthetic hands.

Classifying human manipulation behavior.

This paper presents a taxonomy for detailed classification of human and anthropomorphic manipulation behavior. This hand-centric, motion-centric taxonomy differentiates tasks based on criteria such as object contact, prehension, and the nature of object motion relative to a hand frame. A sub-classification of the most dexterous categories, within-hand manipulation, is also presented, based on the principal axis of object rotation or translation in the hand frame. Principles for categorizing complex, multi-faceted tasks are also presented, along with illustrative examples. We hope that the proposed taxonomy will both establish a standard language around human and anthropomorphic manipulation as well as enable improved understanding of the differences in hand use for a wide variety of behavior. Although designed for human and anthropomorphic hands, the taxonomy might easily be extended to a wide range of robot manipulators and end-effectors.