A 3D biomechanical model of the hand for power grip

A three-dimensional scalable biomechanical model of the four fingers of the hand to evaluate power grip is proposed. The model has been validated by means of reproducing an experiment in which the subjects exerted the maximal voluntary grasping force over cylinders of different diameters. The model is used to simulate the cylinder grip for two hand sizes and for five different handle diameters. The reduction of the muscle forces using different handle diameters has been studied. The model can be applied to the design and evaluation of handles for power grip and to the study of power grasp for normal and abnormal hands.     

A conductive polymer sensor for measuring external finger forces.

This paper describes the construction and use of a durable and thin force sensor that can be attached to the palmar surface of the fingers and hands for studying the biomechanics of grasp and for use in hand injury rehabilitation. These force sensors were constructed using a modified commercially available conductive polymer pressure sensing element and installing an epoxy dome for directing applied forces through a 12 mm diameter active sensing area. The installation of an epoxy dome was effective for making the sensors insensitive to contact surfaces varying from 25 to 1100 mm2 and a 16 mm radius surface curved convex towards the finger. The completed sensors were only 1.8 mm thick and capable of being taped to the distal phalangeal finger pads. They were calibrated on the hand by pinching a strain gage dynamometer. The useful range was between 0 and 30 N with an accuracy of 1 N for both static loading and normal dynamic grasp activities. The sensor time constant was 0.54 ms for a step force input. Because of varying offset voltages every time the sensors were attached, these sensors should be calibrated on the hand before each use. The sensors were used for measuring finger forces during controlled pinching and lifting tasks, and during ordinary grasping activities, such as picking up a book or a box, where the useful force range and response for these sensors were adequate.     

One Digit Interruption: The Altered Force Patterns during Functionally Cylindrical Grasping Tasks in Patients with Trigger Digits

Most trigger digit (TD) patients complain that they have problems using their hand in daily or occupational tasks due to single or multiple digits being affected. Unfortunately, clinicians do not know much about how this disease affects the subtle force coordination among digits during manipulation. Thus, this study examined the differences in force patterns during cylindrical grasp between TD and healthy subjects. Forty-two TD patients with single digit involvement were included and sorted into four groups based on the involved digits, including thumb, index, middle and ring fingers. Twelve healthy subjects volunteered as healthy controls. Two testing tasks, holding and drinking, were performed by natural grasping with minimal forces. The relations between the force of the thumb and each finger were examined by Pearson correlation coefficients. The force amount and contribution of each digit were compared between healthy controls and each TD group by the independent t test. The results showed all TD groups demonstrated altered correlation patterns of the thumb relative to each finger. Larger forces and higher contributions of the index finger were found during holding by patients with index finger involved, and also during drinking by patients with affected thumb and with affected middle finger. Although no triggering symptom occurred during grasping, the patients showed altered force patterns which may be related to the role of the affected digit in natural grasping function. In conclusion, even if only one digit was affected, the subtle force coordination of all the digits was altered during simple tasks among the TD patients. This study provides the information for the future studies to further comprehend the possible injuries secondary to the altered finger coordination and also to adopt suitable treatment strategies.     

Grasp modelling with a biomechanical model of the hand

The use of a biomechanical model for human grasp modelling is presented. A previously validated biomechanical model of the hand has been used. The equilibrium of the grasped object was added to the model through the consideration of a soft contact model. A grasping posture generation algorithm was also incorporated into the model. All the geometry was represented using a spherical extension of polytopes (s-topes) for efficient collision detection. The model was used to simulate an experiment in which a subject was asked to grasp two cylinders of different diameters and weights. Different objective functions were checked to solve the indeterminate problem. The normal finger forces estimated by the model were compared to those experimentally measured. The popular objective function sum of the squared muscle stresses was shown not suitable for the grasping simulation, requiring at least being complemented by task-dependent grasp quality measures.     

The Force Synergy of Human Digits in Static and Dynamic Cylindrical Grasps

This study explores the force synergy of human digits in both static and dynamic cylindrical grasping conditions. The patterns of digit force distribution, error compensation, and the relationships among digit forces are examined to quantify the synergetic patterns and coordination of multi-finger movements. This study recruited 24 healthy participants to perform cylindrical grasps using a glass simulator under normal grasping and one-finger restricted conditions. Parameters such as the grasping force, patterns of digit force distribution, and the force coefficient of variation are determined. Correlation coefficients and principal component analysis (PCA) are used to estimate the synergy strength under the dynamic grasping condition. Specific distribution patterns of digit forces are identified for various conditions. The compensation of adjacent fingers for the force in the normal direction of an absent finger agrees with the principle of error compensation. For digit forces in anti-gravity directions, the distribution patterns vary significantly by participant. The forces exerted by the thumb are closely related to those exerted by other fingers under all conditions. The index-middle and middle-ring finger pairs demonstrate a significant relationship. The PCA results show that the normal forces of digits are highly coordinated. This study reveals that normal force synergy exists under both static and dynamic cylindrical grasping conditions.     

Grasping behavior in tufted capuchin monkeys (Cebus apella): grip types and manual laterality for picking up a small food item

This study investigates prehension in 20 tufted capuchins (Cebus apella) in a reaching task requiring individuals to grasp a small food item fixed to a tray. The aim was twofold: 1) to describe capuchins' grasping techniques in detail, focusing on digit movements and on different areas of contact between the grasping fingers; and 2) to assess the relationship between grip types and manual laterality in this species. Capuchins picked up small food items using a wide variety of grips. In particular, 16 precision grip variants and 4 power grip variants were identified. The most frequently used precision grip involved the distal lateral areas of the thumb and the index finger, while the most preferred kind of power grip involved the thumb and the palm, with the thumb being enclosed by the other fingers. Immature capuchins picked up small food items using power grips more often than precision grips, while adult individuals exhibited no significant preference for either grip type. The analysis performed on the time capuchins took to grasp the food and withdraw it from the tray hole revealed that 1) precision grips were as efficient as power grips; 2) for precision grips, the left hand was faster than the right hand; and 3) for power grips, both hands were equally quick. Hand preference analysis, based on the frequency for the use of either hand for grasping actions, revealed no significant hand bias at group level. Likewise, there was no significant relationship between grip type and hand preference.     

Hand function: peripheral and central constraints on performance.

The hand is one of the most fascinating and sophisticated biological motor systems. The complex biomechanical and neural architecture of the hand poses challenging questions for understanding the control strategies that underlie the coordination of finger movements and forces required for a wide variety of behavioral tasks, ranging from multidigit grasping to the individuated movements of single digits. Hence, a number of experimental approaches, from studies of finger movement kinematics to the recording of electromyographic and cortical activities, have been used to extend our knowledge of neural control of the hand. Experimental evidence indicates that the simultaneous motion and force of the fingers are characterized by coordination patterns that reduce the number of independent degrees of freedom to be controlled. Peripheral and central constraints in the neuromuscular apparatus have been identified that may in part underlie these coordination patterns, simplifying the control of multi-digit grasping while placing certain limitations on individuation of finger movements. We review this evidence, with a particular emphasis on how these constraints extend through the neuromuscular system from the behavioral aspects of finger movements and forces to the control of the hand from the motor cortex.     

A kinematic model of the human hand to evaluate its prehensile capabilities.

A kinematic model has been developed for simulation and prediction of the prehensile capabilities of the human hand. The kinematic skeleton of the hand is characterized by ideal joints and simple segments. Finger-joint angulation is characterized by yaw (abduction-adduction), pitch (flexion-extension) and roll (axial rotation) angles. The model is based on an algorithm that determines contact between two ellipsoids, which are used to approximate the geometry of the cutaneous surface of the hand segments. The model predicts the hand posture (joint angles) for power grasp of ellipsoidal objects by 'wrapping' the fingers around the object. Algorithms for two grip types are included: (1) a transverse volar grasp, which has the thumb abducted for added power; and (2) a diagonal volar grasp, which has the thumb adducted for an element of precision. Coefficients for estimating anthropometric parameters from hand length and breadth are incorporated in the model. Graphics procedures are included for visual display of the model. In an effort to validate the predictive capabilities of the model, joint angles were measured on six subjects grasping circular cylinders of various diameters and these measured joint angles were compared with angles predicted by the model. Sensitivity of the model to the various input parameters was also determined. On an average, the model predicted joint flexion angles that were 5.3% or 2.8 degrees +/- 12.2 degrees larger than the measured angles. Good agreement was found for the MCP and PIP joints, but results for DIP were more variable because of its dependence on the predictions for the proximal joints.     

Finger force vectors in multi-finger prehension

In a majority of studies on grasp, only normal forces were measured and only when a zero torque was exerted on a hand-held object. This study concerns finger force vectors during the torque production tasks. Subjects (n=8) stabilized a handle with an attachment that allowed for change of external torque from -1.5 to 1.5 Nm. Forces and moments exerted by the digit tips on the object were recorded. At the large (>-0.375 Nm) supination torques the index/middle and ring/little pairs of fingers generated oppositely directed tangential forces. The index and middle finger produced forces in a downward direction and therefore did not support the load. At a zero torque and pronation torques, the middle, ring and little fingers produced forces along nearly the same direction. The vector of the index finger force was always directed differently from the vectors of other finger forces, the angles ranged from 19 degrees 30' to 47 degrees 40'. The points of force application were systematically displaced with the torque, with the exception of the little finger. Tangential finger forces contributed substantially to the total torque exerted on the hand-held object.     

Virtual human hand: model and kinematics

The human hand plays an important role in daily life. It is the interface between the human and the exterior world by positioning, orienting, touching and grasping objects. The human hand has multiple degrees of freedom (DOFs) to enable mobility and dexterity. A virtual human hand model can be inserted into CAD (Computer Aided Design) models to assess the manipulation capabilities in the early design stage to reduce design time and cost. Joystick assessment is one of the important design cases. This study is a first step towards a comprehensive hand simulation tool to simulate the manipulation and grasping of objects. This paper presents a novel 25 DOFs' hand skeletal model based on hand anatomy and hand kinematics: (1) joint range of motion, (2) Denavit–Hartenberg method to define the joint relationship and (3) finger workspace determination. Novelty for this hand model includes arching the palm with the four DOFs added in the carpometacarpal and wrist joints for the ring and small fingers.     

A technique for conditioning and calibrating force-sensing resistors for repeatable and reliable measurement of compressive force

Miniature sensors that could measure forces applied by the fingers and hand without interfering with manual dexterity or range of motion would have considerable practical value in ergonomics and rehabilitation. In this study, techniques have been developed to use inexpensive pressure-sensing resistors (FSRs) to accurately measure compression force. The FSRs are converted from pressure-sensing to force-sensing devices. The effects of nonlinear response properties and dependence on loading history are compensated by signal conditioning and calibration. A fourth-order polynomial relating the applied force to the current voltage output and a linearly weighted sum of prior outputs corrects for sensor hysteresis and drift. It was found that prolonged (>20 h) shear force loading caused sensor gain to change by approximately 100%. Shear loading also had the effect of eliminating shear force effects on sensor output, albeit only in the direction of shear loading. By applying prolonged shear loading in two orthogonal directions, the sensors were converted into pure compression sensors. Such preloading of the sensor is, therefore, required prior to calibration. The error in compression force after prolonged shear loading and calibration was consistently <5% from 0 to 30 N and <10% from 30 to 40 N. This novel method of calibrating FSRs for measuring compression force provides an inexpensive tool for biomedical and industrial design applications where measurements of finger and hand force are needed.     

Force and torque production in static multifinger prehension: biomechanics and control. II. Control

 The coordination of digits during combined force/torque production tasks was further studied using the data presented in the companion paper [Zatsiorsky et al. Biol Cybern this issue, Part I]. Optimization was performed using as criteria the cubic norms of (a) finger forces, (b) finger forces normalized with respect to the maximal forces measured in single-finger tasks, (c) finger forces normalized with respect to the maximal forces measured in a four-finger task, and (d) finger forces normalized with respect to the maximal moments that can be generated by the fingers. All four criteria failed to predict antagonist finger moments when these moments were not imposed by the task mechanics. Reconstruction of neural commands: The vector of neural commands c was reconstructed from the equation c=W ⁻¹ F, where W is the finger interconnection weight matrix and F is the vector of finger forces. The neural commands ranged from zero (no voluntary force production) to one (maximal voluntary contraction). For fingers producing moments counteracting the external torque (`agonist' fingers), the intensity of the neural commands was well correlated with the relative finger forces normalized to the maximal forces in a four-finger task. When fingers produced moments in the direction of the external torque (`antagonist' fingers), the relative finger forces were always larger than those expected from the intensity of the corresponding neural commands. The individual finger forces were decomposed into forces due to `direct' commands and forces induced by enslaving effects. Optimization of the neural commands resulted in the best correspondence between actual and predicted finger forces. The antagonist moments are, at least in part, due to enslaving effects: strong commands to agonist fingers also activated antagonist fingers. Received: 8 August 2001 / Accepted in revised form: 7 February 2002     

Indices of nonlinearity in finger force interaction

Experiments with force production by subsets of fingers within the human hand have shown that finger interaction may be significantly nonlinear. In particular, this nonlinearity is reflected in the phenomenon of force deficit, a drop of the peak force of a finger when several fingers act simultaneously. We describe nonlinear effects in force relations within finger pairs, triplets, etc. Finger forces are represented as the sums of components resulting from force interactions within all subsets of the explicitly involved (master) fingers. The values of these components computed at extreme values of control signals, zero and unity, are taken as indices of such elementary force interactions. Indices of the first order reflect purposeful force production by a single master finger and its effects on forces produced by other fingers (enslaving). Indices of the second order reflect additional influences from pairs of simultaneously recruited master fingers, etc. Force interaction indices were computed based on finger forces measured in earlier experiments. Signs of indices alternated with their order, being positive for the indices of the first order (enslaving), negative for the indices of the second order (force deficit), positive for the indices of the third order, and mostly negative for the indices of the fourth order. Indices of the third and fourth orders reflect phenomena of force interaction not reported earlier. The study emphasizes the importance of nonlinear interactions among finger forces and introduces a set of independent indices that can be used to quantify such interactions in different subpopulations and their possible changes with practice and/or rehabilitation of the hand function.     

Force and torque production in static multifinger prehension: biomechanics and control. I. Biomechanics

 We studied the coordinated action of fingers during static tasks involving exertion of force and torque on a handheld object. Subjects were asked to keep a handle with an attachment that allowed for independent change of the suspended load (0.5–2.0 kg) and external torque (0.375–1.5 N m) in a vertical position while applying minimal effort. Normal and shear forces were measured from the thumb; normal forces only were measured from the four fingers. Experimental results: (1) the thumb shear force increased during supination efforts and decreased during pronation efforts; (2) the total moment of the normal finger forces only counterbalanced approximately 50% of the external torque, hence shear forces accounted for approximately one-half of the total torque exerted on the object; (3) the total normal force increased with external torque, and the total force magnitude did not depend on the torque direction; (4) the forces of the `peripheral' (index and little) fingers depended mainly on the torque while the forces exerted by the `central' (middle and ring) fingers depended both on the load and torque; (5) there was a monotonic relationship between the mechanical advantage of a finger (i.e., its moment arm during torque production) and the force produced by that finger; and (6) antagonist finger moments acting opposite to the intended direction of the total moment were always observed – at low torques the antagonist moments were as high as 40–60% of the agonist moments. Modeling: A three-zone model of coordinated finger action is suggested. In the first zone of load/torque combinations, activation of antagonist fingers (i.e., fingers that generate antagonist moments) is necessary to prevent slipping. In the second zone, the activity of agonist fingers is sufficient for preventing slips. In the third zone, the performer has freedom to choose between either activating the antagonist fingers or redistributing activities amongst the agonist fingers. The findings of this study provide the foundation for neural network and optimization modeling described in the companion paper [Zatsiorsky et al. (2002) Biol Cybern DOI 10.1007/s00422-002-0320-7]. Received: 8 August 2001 / Accepted in revised form: 7 February 2002     

On the Evolution of Handedness: Evidence for Feeding Biases

Many theories have been put forward to explain the origins of right-handedness in humans. Here we present evidence that this preference may stem in part from a right hand advantage in grasping for feeding. Thirteen participants were asked to reach-to-grasp food items of 3 different sizes: SMALL (Cheerios®), MEDIUM (Froot Loops®), and LARGE (Oatmeal Squares®). Participants used both their right- and left-hands in separate blocks (50 trials each, starting order counterbalanced) to grasp the items. After each grasp, participants either a) ate the food item, or b) placed it inside a bib worn beneath his/her chin (25 trials each, blocked design, counterbalanced). The conditions were designed such that the outward and inward movement trajectories were similar, differing only in the final step of placing it in the mouth or bib. Participants wore Plato liquid crystal goggles that blocked vision between trials. All trials were conducted in closed-loop with 5000 ms of vision. Hand kinematics were recorded by an Optotrak Certus, which tracked the position of three infrared diodes attached separately to the index finger, thumb, and wrist. We found a task (EAT/PLACE) by hand (LEFT/RIGHT) interaction on maximum grip aperture (MGA; the maximum distance between the index finger and thumb achieved during grasp pre-shaping). MGAs were smaller during right-handed movements, but only when grasping with intent to eat. Follow-up tests show that the RIGHT-HAND/EAT MGA was significantly smaller than all other hand/task conditions. Because smaller grip apertures are typically associated with greater precision, our results demonstrate a right-hand advantage for the grasp-to-eat movement. From an evolutionary perspective, early humans may have preferred the hand that could grasp food with more precision, thereby maximizing the likelihood of retrieval, consumption, and consequently, survival.     

Coordination of contact forces during multifinger static prehension

This study investigated the effects of modifying contact finger forces in one direction-normal or tangential-on the entire set of the contact forces, while statically holding an object. Subjects grasped a handle instrumented with finger force-moment sensors, maintained it at rest in the air, and then slowly: (1) increased the grasping force, (2) tried to spread fingers apart, and (3) tried to squeeze fingers together. Analysis was mostly performed at the virtual finger (VF) level (the VF is an imaginable finger that generates the same force and moment as the four fingers combined). For all three tasks there were statistically significant changes in the VF normal and tangential forces. For finger spreading/squeezing the tangential force neutral point was located between the index and middle fingers. We conclude that the internal forces are regulated as a whole, including adjustments in both normal and tangential force, instead of only a subset of forces (normal or tangential). The effects of such factors as EFFORT and TORQUE were additive; their interaction was not statistically significant, thus supporting the principle of superposition in human prehension.     

From Single Motor Unit Activity to Multiple Grip Forces: Mini-review of Multi-digit Grasping

This paper is a mini review of kinetic and kinematic evidenceon the control of the hand with emphasis on grasping. It isnot meant to be an exhaustive review, rather it summarizes currentresearch examining the mechanisms through which specific patternsof coordination are elicited and observed during reach to graspmovements and static grasping. These coordination patterns includethe spatial and temporal covariation of the rotation at multiplejoints during reach to grasp movements. A basic coordinationbetween grip forces produced by multiple digits also occursduring whole hand grasping such that normal forces tend to beproduced in a synchronous fashion across pairs of digits. Finally,we address current research that suggests that motor unit synchronyacross hand muscles and muscle compartments might be one ofthe neural mechanisms underlying the control of grasping.     

The grasping side of odours

Background

Research on multisensory integration during natural tasks such as reach-to-grasp is still in its infancy. Crossmodal links between vision, proprioception and audition have been identified, but how olfaction contributes to plan and control reach-to-grasp movements has not been decisively shown. We used kinematics to explicitly test the influence of olfactory stimuli on reach-to-grasp movements.

Methodology/Principal Findings

Subjects were requested to reach towards and grasp a small or a large visual target (i.e., precision grip, involving the opposition of index finger and thumb for a small size target and a power grip, involving the flexion of all digits around the object for a large target) in the absence or in the presence of an odour evoking either a small or a large object that if grasped would require a precision grip and a whole hand grasp, respectively. When the type of grasp evoked by the odour did not coincide with that for the visual target, interference effects were evident on the kinematics of hand shaping and the level of synergies amongst fingers decreased. When the visual target and the object evoked by the odour required the same type of grasp, facilitation emerged and the intrinsic relations amongst individual fingers were maintained.

Conclusions/Significance

This study demonstrates that olfactory information contains highly detailed information able to elicit the planning for a reach-to-grasp movement suited to interact with the evoked object. The findings offer a substantial contribution to the current debate about the multisensory nature of the sensorimotor transformations underlying grasping.     

Tangential finger forces use mechanical advantage during static grasping

When grasping and manipulating objects, the central controller utilizes the mechanical advantage of the normal forces of the fingers for torque production. Whether the same is valid for tangential forces is unknown. The main purpose of this study was to determine the patterns of finger tangential forces and the use of mechanical advantage as a control mechanism when dealing with objects of nonuniform finger positioning. A complementary goal was to explore the interaction of mechanical advantage (moment arm) and the role a finger has as a torque agonist/antagonist with respect to external torques (±0.4 N m). Five 6-df force/torque transducers measured finger forces while subjects held a prism handle (6 cm width × 9 cm height) with and without a single finger displaced 2 cm (handle width). The effect of increasing the tangential moment arm was significant (p < .01) for increasing tangential forces (in >70% of trials) and hence creating greater moments. Thus, the data provides evidence that the grasping system as a rule utilizes mechanical advantage for generating tangential forces. The increase in tangential force was independent of whether the finger was acting as a torque agonist or antagonist, revealing their effects to be additive.     

Analysis of Finger Muscular Forces using a Wearable Hand Exoskeleton System

In this paper,the finger muscular forces were estimated and analyzed through the application of inverse dynamics-based static optimization,and a hand exoskeleton system was designed to pull the fingers and measure the dynamics of the hand.To solve the static optimization,a muscular model of the hand flexors was derived.The experimental protocol was devised to analyze finger flexors in order to evaluate spasticity of the clenched fingers;muscular forces were estimated while the flexed fingers were extended by the exoskeleton with external loads applied.To measure the finger joint angles,the hand exoskeleton system was designed using four-bar linkage structure and potentiometers.In addition,the external loads to the fingertips were generated by cable driven actuators and simultaneously measured by loadcells which were located at each phalanx.The experiments were performed with a normal person and the muscular forces estimation results were discussed with reference to the physical phenomena.