Multifinger prehension: an overview

Two aspects of feed-forward control of action stability: effects of action speed and unexpected events

We explored two types of anticipatory synergy adjustments (ASA) during accurate four-finger total force production task. The first type is a change in the index of force-stabilizing synergy during a steady state when a person is expecting a signal to produce a quick force change, which is seen even when the signal does not come (steady-state ASA). The other type is the drop in in the synergy index prior to a planned force change starting at a known time (transient ASA). The subjects performed a task of steady force production at 10% of maximal voluntary contraction (MVC) followed by a ramp to 20% MVC over 1 s, 3 s, and as a step function (0 s). In another task, in 50% of the trials during the steady-state phase, an unexpected signal could come requiring a quick force pulse to 20% MVC (0-surprise). Inter-trial variance in the finger force space was used to quantify the index of force-stabilizing synergy within the uncontrolled manifold hypothesis. We observed significantly lower synergy index values during the steady state in the 0-ramp trials compared to the 1-ramp and 3-ramp trials. There was also larger transient ASA during the 0-ramp trials. In the 0-surprise condition, the synergy index was significantly higher compared to the 0-ramp condition whereas the transient ASA was significantly larger. The finding of transient ASA scaling is of importance for clinical studies, which commonly involve populations with slower actions, which can by itself be associated with smaller ASAs. The participants varied the sharing pattern of total force across the fingers more in the task with "surprises". This was coupled to more attention to precision of performance, i.e., inter-trial deviations from the target as reflected in smaller variance affecting total force, possibly reflecting higher concentration on the task, which the participants perceived as more challenging compared to a similar task without surprise targets.

Evolution, biomechanics, and neurobiology converge to explain selective finger motor control

Humans use their fingers to perform a variety of tasks, from simple grasping to manipulating objects, to typing and playing musical instruments, a variety wider than any other species. The more sophisticated the task, the more it involves individuated finger movements, those in which one or more selected fingers perform an intended action while the motion of other digits is constrained. Here we review the neurobiology of such individuated finger movements. We consider their evolutionary origins, the extent to which finger movements are in fact individuated, and the evolved features of neuromuscular control that both enable and limit individuation. We go on to discuss other features of motor control that combine with individuation to create dexterity, the impairment of individuation by disease, and the broad extent of capabilities that individuation confers on humans. We comment on the challenges facing the development of a truly dexterous bionic hand. We conclude by identifying topics for future investigation that will advance our understanding of how neural networks interact across multiple regions of the central nervous system to create individuated movements for the skills humans use to express their cognitive activity.

Dexterity in the Acute Phase of Stroke: Impairments and Neural Substrates

BACKGROUND

Stroke can impair manual dexterity, leading to loss of independence following incomplete recovery. Enhancing our understanding of dexterity impairment may improve neurorehabilitation.

OBJECTIVES

The study aimed to measure dexterity components in acute stroke patients with and without hand motor deficits, compare them to those of healthy controls (HC), and to explore the neural substrates involved in specific components of dexterity.

METHODS

We used the Dextrain Manipulandum to quantify fine finger force control, finger selection accuracy, coactivation, and reaction time (RT). Dexterity was evaluated twice (2 days apart) in 74 patients and 14 HC. Voxel-Lesion-Symptom-Mapping (VLSM) was used to analyze the relationship between tissue damage and dexterity. Results. Due to severe paresis or fatigue, 24 patients could not perform these tasks. In 50 patients (included 4.6 ± 3.3 days post-stroke), finger force control improved (P < .001), as it did in HC (P = .03) who performed better than patients on both evaluations. Accuracy of finger selection did not improve significantly in any group, but the HC performed better on both evaluations. Unexpectedly, coactivation was better in patients than in HC at D3 (P = .03). There were no between-group differences in RT. VLSM showed that damage to the superior temporal gyrus (STG) impaired finger force control while damage to the posterior limb of the internal capsule (PLIC) impaired finger selectivity.

CONCLUSIONS

Acute stroke affecting the STG or PLIC impaired selective components of dexterity. Patients with mild to moderate impairment showed better finger force control and accuracy selection within 48 hours, suggesting the feasibility of detecting early dexterity improvements.

Force Control Strategy of Five-Digit Precision Grasping With Aligned and Unaligned Configurations

OBJECTIVE

To investigate the digit force control during a five-digit precision grasp in aligned (AG) and unaligned grasping (UG) configurations.

BACKGROUND

The effects of various cylindrical handles for tools on power grasp performance have been previously investigated. However, there is little information on force control strategy of precision grasp to fit various grasping configurations.

METHOD

Twenty healthy young adults were recruited to perform a lift-hold-lower task. The AG and UG configurations on a cylindrical simulator with force transducers were adjusted for each individual. The applied force and moment, the force variability during holding, and force correlations between thumb and each finger were measured.

RESULT

No differences in applied force, force correlation, repeatability, and variability were found between configurations. However, the moments applied in UG were significantly larger than those in AG.

CONCLUSION

The force control during precision grasp did not change significantly across AG and UG except for the digit moment. The simulator is controlled efficiently with large moment during UG, which is thus the optimal configuration for precision grasping with a cylindrical handle. Further research should consider the effects of task type and handle design on force control, especially for individuals with hand disorders.

APPLICATION

To design the handle of specific tool, one should consider the appropriate configuration according to the task requirements of precision grasping to reduce the risk of accumulating extra loads on digits with a cylindrical handle.

Spatiotemporal Modeling of Grip Forces Captures Proficiency in Manual Robot Control

New technologies for monitoring grip forces during hand and finger movements in non-standard task contexts have provided unprecedented functional insights into somatosensory cognition. Somatosensory cognition is the basis of our ability to manipulate and transform objects of the physical world and to grasp them with the right amount of force. In previous work, the wireless tracking of grip-force signals recorded from biosensors in the palm of the human hand has permitted us to unravel some of the functional synergies that underlie perceptual and motor learning under conditions of non-standard and essentially unreliable sensory input. This paper builds on this previous work and discusses further, functionally motivated, analyses of individual grip-force data in manual robot control. Grip forces were recorded from various loci in the dominant and non-dominant hands of individuals with wearable wireless sensor technology. Statistical analyses bring to the fore skill-specific temporal variations in thousands of grip forces of a complete novice and a highly proficient expert in manual robot control. A brain-inspired neural network model that uses the output metric of a self-organizing pap with unsupervised winner-take-all learning was run on the sensor output from both hands of each user. The neural network metric expresses the difference between an input representation and its model representation at any given moment in time and reliably captures the differences between novice and expert performance in terms of grip-force variability.Functionally motivated spatiotemporal analysis of individual average grip forces, computed for time windows of constant size in the output of a restricted amount of task-relevant sensors in the dominant (preferred) hand, reveal finger-specific synergies reflecting robotic task skill. The analyses lead the way towards grip-force monitoring in real time. This will permit tracking task skill evolution in trainees, or identify individual proficiency levels in human robot-interaction, which represents unprecedented challenges for perceptual and motor adaptation in environmental contexts of high sensory uncertainty. Cross-disciplinary insights from systems neuroscience and cognitive behavioral science, and the predictive modeling of operator skills using parsimonious Artificial Intelligence (AI), will contribute towards improving the outcome of new types of surgery, in particular the single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic Surgery) and SILS (Single-Incision Laparoscopic Surgery).

Grip force as a functional window to somatosensory cognition

Analysis of grip force signals tailored to hand and finger movement evolution and changes in grip force control during task execution provide unprecedented functional insight into somatosensory cognition. Somatosensory cognition is the basis of our ability to act upon and to transform the physical world around us, to recognize objects on the basis of touch alone, and to grasp them with the right amount of force for lifting and manipulating them. Recent technology has permitted the wireless monitoring of grip force signals recorded from biosensors in the palm of the human hand to track and trace human grip forces deployed in cognitive tasks executed under conditions of variable sensory (visual, auditory) input. Non-invasive multi-finger grip force sensor technology can be exploited to explore functional interactions between somatosensory brain mechanisms and motor control, in particular during learning a cognitive task where the planning and strategic execution of hand movements is essential. Sensorial and cognitive processes underlying manual skills and/or hand-specific (dominant versus non-dominant hand) behaviors can be studied in a variety of contexts by probing selected measurement loci in the fingers and palm of the human hand. Thousands of sensor data recorded from multiple spatial locations can be approached statistically to breathe functional sense into the forces measured under specific task constraints. Grip force patterns in individual performance profiling may reveal the evolution of grip force control as a direct result of cognitive changes during task learning. Grip forces can be functionally mapped to from-global-to-local coding principles in brain networks governing somatosensory processes for motor control in cognitive tasks leading to a specific task expertise or skill. Under the light of a comprehensive overview of recent discoveries into the functional significance of human grip force variations, perspectives for future studies in cognition, in particular the cognitive control of strategic and task relevant hand movements in complex real-world precision task, are pointed out.

Hierarchical and synergistic organization of control variables during the multi-digit grasp of a free and an externally fixed object

In the referent control theory, grip force emerges by designating the referent aperture (R_a) as a threshold position inside the object. This study quantified R_a and investigated whether the synergistic control of digit referent coordinate (RC) and apparent stiffness (k) depend on the external mechanical constraints on the hand-held object. Subjects held a motorized handle capable of adjusting the grip width and performed static multi-digit prehension tasks in which the handle was free and externally fixed in different conditions. The RC and k of individual digits were reconstructed from the changes in digit normal forces and the positions as the grip width was modulated. RCs of the thumb and virtual finger were used to calculate the width and midpoint of R_a, and synergy indices quantifying the task-specific covariation in the space of the digit normal forces and {RC, k} variables were computed. We found that the k and width of the R_a were larger when holding a free handle than the fixed handle. The higher stiffness in the free condition could be a strategy to ensure grip stability. The midpoint of R_a was skewed toward the virtual finger, reflecting different magnitudes of k for the two digits. Further, the normal forces and control variables {RC, k} displayed synergistic covariation for stabilization of the total grasping force. Finally, the synergies were weaker when the handle was externally fixed, demonstrating the dependence of synergies on external constraints. These results add to the current literature by demonstrating that grasp control involves modulation of digit apparent stiffness in addition to the referent coordinate and by identifying the synergistic organization of the control variables during static grasp.

Neuroenhancement of a dexterous motor task with Anodal tDCS

Motor skill learning can cause structural and functional changes in the primary motor cortex (M1) leading to cortical plasticity that can be associated with the performance change during the motor skill that is practiced. Similarly, anodal transcranial direct current stimulation (a-tDCS) has been shown to facilitate and enhance plasticity in M1, causing even greater motor skill improvement. By using a fine motor task (O'Connor Tweezer Dexterity Task) in combination with a-tDCS we theorized that a-tDCS could increase the speed of skill acquisition. Forty subjects were recruited and randomized into either a-tDCS or SHAM groups. Subjects completed a single session performing the O'Connor Tweezer Dexterity Task with their non-dominant hand while receiving either a-tDCS stimulation or SHAM stimulation of the hand region of M1. The time it took to place 50- pins was assessed before and after 20 minutes of practice with a-tDCS or SHAM. We found that both groups had similar pre-test performance (P=0.94) and they both had a similar amount of practice pins placed (P=0.69). However, the a-tDCS group had a greater improvement than the SHAM group (p=0.028) for overall learning from pretest to posttest. These results suggest that a-tDCS improved the rate of motor learning and fine motor task performance. These results are in line with previous research and demonstrate that a-tDCS applied to M1 can increase manual precision and steadiness needed for delicate tasks and could have implications in the advancement of surgical training as well as in athletic, military, and other occupational settings.

Effects of hand muscle function and dominance on intra-muscle synergies

The goal of the study was to explore the effects of hand dominance and muscle function (prime mover vs. supporting muscle) on recently discovered intra-muscle synergies as potential windows into their neural origin. Healthy right-handed subjects performed accurate cyclical force production tasks while pressing with the middle phalanges and distal phalanges of the fingers of the dominant and non-dominant hand. Surface electromyography was used to identify individual motor unit action potentials in two muscles, flexor digitorum superficialis (FDS) and extensor digitorum communis (EDC). Stable motor unit groups (MU-modes) were defined in each muscle and in both muscles together. The composition of the MU-modes allowed linking them to the reciprocal and co-activation command. Force-stabilizing synergies were quantified in each hand and during force production at both sites using the framework of the uncontrolled manifold hypothesis. Force-stabilizing synergies were seen in the spaces of MU-modes from FDS and EDC separately, but not of MU-modes defined for both muscles together. Synergy indices were similar for both hands and both sites of force application. In contrast, force-stabilizing synergies in the space of finger forces were present in the non-dominant hand and absent in the dominant hand. The data suggest existence of distributed mechanisms of synergic control. Finger force synergies are likely to reflect functioning of subcortical loops involving the basal ganglia and cerebellum, while MU-mode synergies are likely to reflect spinal circuitry. Studies of both force-based and motor-unit-based synergies may be clinically valuable for distinguishing effects of spinal and supraspinal disorders.

A Method for Measuring Contact Points in Human–Object Interaction Utilizing Infrared Cameras

This article presents a novel method for measuring contact points in human–object interaction. Research in multiple prehension-related fields, e.g., action planning, affordance, motor function, ergonomics, and robotic grasping, benefits from accurate and precise measurements of contact points between a subject’s hands and objects. During interaction, the subject’s hands occlude the contact points, which poses a major challenge for direct optical measurement methods. Our method solves the occlusion problem by exploiting thermal energy transfer from the subject’s hand to the object surface during interaction. After the interaction, we measure the heat emitted by the object surface with four high-resolution infrared cameras surrounding the object. A computer-vision algorithm detects the areas in the infrared images where the subject’s fingers have touched the object. A structured light 3D scanner produces a point cloud of the scene, which enables the localization of the object in relation to the infrared cameras. We then use the localization result to project the detected contact points from the infrared camera images to the surface of the 3D model of the object. Data collection with this method is fast, unobtrusive, contactless, markerless, and automated. The method enables accurate measurement of contact points in non-trivially complex objects. Furthermore, the method is extendable to measuring surface contact areas, or patches, instead of contact points. In this article, we present the method and sample grasp measurement results with publicly available objects.

Unintentional force drifts across the human fingers: implications for the neural control of finger tasks

We explored the unintentional force drift across the four fingers of the dominant hand during accurate force production in isometric conditions caused by turning the visual feedback on force off. Our hypotheses were that the Index finger would show smallest drifts and best ability to eliminate the drifts with knowledge of performance in previous trials. Young healthy subjects produced force at 20% of the maximal force level by one finger at a time. There was no significant difference among the fingers in the root mean square error of force during performance with visual feedback. Turning visual feedback off caused force drift to lower magnitudes. The magnitude of force drift was the largest during tasks performed by the Index finger. After each block of twelve trials, the subjects were given feedback on the drift magnitude in that block and used it to correct performance in future trials. There was a total of six blocks. The magnitude of drift correction between consecutive blocks correlated with the magnitude of drift in the earlier block only after the second and fourth blocks. The Index finger failed to improve its performance more than other fingers and demonstrated significant residual drifts to lower force magnitudes in the sixth block of trials. These findings falsified both our hypotheses. Taken together with earlier studies showing advantage of the Index finger across a variety of tasks that require quick and accurate changes in performance, our results suggest that effector specialization along the stability-agility continuum is not limited to the phenomenon of cortical arm/hand dominance but can also be seen across fingers of a hand controlled by the same hemisphere, possibly reflecting the differences in the finger role in prehensile tasks.

Synergies at the level of motor units in single-finger and multi-finger tasks

We explored the organization of motor units recorded in the flexor digitorum superficialis into stable groups (MU-modes) and force-stabilizing synergies in spaces of MU-modes. Young, healthy participants performed one-finger and three-finger accurate cyclical force production tasks. Two wireless sensor arrays (Trigno Galileo, Delsys, Inc.) were placed over the proximal and distal portions of the muscle for surface recording and identification of motor unit action potentials. Principal component analysis with Varimax rotation and factor extraction was used to identify MU-modes. The framework of the uncontrolled manifold hypothesis was used to analyze inter-cycle variance in the space of MU-modes and compute the index of force-stabilizing synergy. Multiple linear regression between the first MU-mode in the three-finger task and the first MU-modes in the three single-finger tasks showed no differences between the data recorded by the two electrodes suggesting that MU-modes were unlikely to be synonymous with muscle compartments. Multi-MU-mode synergies stabilizing task force were documented across all tasks. In contrast, there were no force-stabilizing synergies in the three-finger task analyzed in the space of individual finger forces. Our results confirm the synergic organization of motor units in single-finger tasks and, for the first time, expand this result to multi-finger tasks. We offer an interpretation of the findings within the theoretical scheme of control with spatial referent coordinates expanded to the analysis of individual motor units. The results confirm trade-offs between synergies at different hierarchical levels and expand this notion to intra-muscle synergies.

Efference copy in kinesthetic perception: a copy of what is it?

A number of notions in the fields of motor control and kinesthetic perception have been used without clear definitions. In this review, we consider definitions for efference copy, percept, and sense of effort based on recent studies within the physical approach, which assumes that the neural control of movement is based on principles of parametric control and involves defining time-varying profiles of spatial referent coordinates for the effectors. The apparent redundancy in both motor and perceptual processes is reconsidered based on the principle of abundance. Abundance of efferent and afferent signals is viewed as the means of stabilizing both salient action characteristics and salient percepts formalized as stable manifolds in high-dimensional spaces of relevant elemental variables. This theoretical scheme has led recently to a number of novel predictions and findings. These include, in particular, lower accuracy in perception of variables produced by elements involved in a multielement task compared with the same elements in single-element tasks, dissociation between motor and perceptual effects of muscle coactivation, force illusions induced by muscle vibration, and errors in perception of unintentional drifts in performance. Taken together, these results suggest that participation of efferent signals in perception frequently involves distorted copies of actual neural commands, particularly those to antagonist muscles. Sense of effort is associated with such distorted efferent signals. Distortions in efference copy happen spontaneously and can also be caused by changes in sensory signals, e.g., those produced by muscle vibration.

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.

Back to reality: differences in learning strategy in a simplified virtual and a real throwing task

Virtual environments have been widely used in motor neuroscience and rehabilitation, as they afford tight control of sensorimotor conditions and readily afford visual and haptic manipulations. However, typically, studies have only examined performance in the virtual testbeds, without asking how the simplified and controlled movement in the virtual environment compares to behavior in the real world. To test whether performance in the virtual environment was a valid representation of corresponding behavior in the real world, this study compared throwing in a virtual set-up with realistic throwing, where the task parameters were precisely matched. Even though the virtual task only required a horizontal single-joint arm movement, similar to many simplified movement assays in motor neuroscience, throwing accuracy and precision were significantly worse than in the real task that involved all degrees of freedom of the arm; only after 3 practice days did success rate and error reach similar levels. To gain more insight into the structure of the learning process, movement variability was decomposed into deterministic and stochastic contributions. Using the tolerance-noise-covariation decomposition method, distinct stages of learning were revealed: While tolerance was optimized first in both environments, it was higher in the virtual environment, suggesting that more familiarization and exploration was needed in the virtual task. Covariation and noise showed more contributions in the real task, indicating that subjects reached the stage of fine-tuning of variability only in the real task. These results showed that while the tasks were precisely matched, the simplified movements in the virtual environment required more time to become successful. These findings resonate with the reported problems in transfer of therapeutic benefits from virtual to real environments and alert that the use of virtual environments in research and rehabilitation needs more caution.NEW & NOTEWORTHY This study compared human performance of the same throwing task in a real and a matched virtual environment. With 3 days' practice, subjects improved significantly faster in the real task, even though the arm and hand movements were more complex. Decomposing variability revealed that performance in the virtual environment, despite its simplified hand movements, required more exploration. Additionally, due to fewer constraints in the real task, subjects could modify the geometry of the solution manifold, by shifting the release position, and thereby simplify the task.

Correlating Grip Force Signals from Multiple Sensors Highlights Prehensile Control Strategies in a Complex Task-User System

Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is generally wireless and enables the non-invasive monitoring of signals to track and trace user behaviors in real time. Examples include signals relative to hand and finger movements or force control reflected by individual grip force data. As will be shown here, these signals directly translate into task, skill, and hand-specific (dominant versus non-dominant hand) grip force profiles for different measurement loci in the fingers and palm of the hand. The present study draws from thousands of such sensor data recorded from multiple spatial locations. The individual grip force profiles of a highly proficient left-hander (expert), a right-handed dominant-hand-trained user, and a right-handed novice performing an image-guided, robot-assisted precision task with the dominant or the non-dominant hand are analyzed. The step-by-step statistical approach follows Tukey's "detective work" principle, guided by explicit functional assumptions relating to somatosensory receptive field organization in the human brain. Correlation analyses (Person's product moment) reveal skill-specific differences in co-variation patterns in the individual grip force profiles. These can be functionally mapped to from-global-to-local coding principles in the brain networks that govern grip force control and its optimization with a specific task expertise. Implications for the real-time monitoring of grip forces and performance training in complex task-user systems are brought forward.

On the origin of finger enslaving: control with referent coordinates and effects of visual feedback

When a person tries to press with a finger, other fingers of the hand produce force unintentionally. We explored this phenomenon of enslaving during unintentional force drifts in the course of continuous force production by pairs of fingers of a hand. Healthy subjects performed accurate force production tasks by finger pairs Index-Middle, Middle-Ring, and Ring-Little with continuous visual feedback on the combined force of the instructed (master) fingers or of the noninstructed (enslaved) fingers. The feedback scale was adjusted to ensure that the subjects did not know the difference between these two, randomly presented, conditions. Across all finger pairs, enslaved force showed a drift upward under feedback on the master finger force, and master force showed a drift downward under feedback on the enslaved finger force. The subjects were unaware of the force drifts, which could reach over 50% of the initial force magnitude over 15 s. Across all conditions, the index of enslaving increased by ∼50% over the trial duration. The initial moment of force magnitude in pronation-supination was not a consistent predictor of the force drift magnitude. These results falsify the hypothesis that the counter-directional force drifts reflected drifts in the moment of force. They suggest that during continuous force production, enslaving increases with time, possibly due to the spread of excitation over cortical finger representations or other mechanisms, such as increased synchronization of firing of α-motoneurons innervating different compartments of extrinsic flexors. These changes in enslaving, interpreted at the level of control with referent coordinates for the fingers, can contribute to a variety of phenomena, including unintentional force drifts.NEW & NOTEWORTHY We report a consistent slow increase in finger enslaving (force production by noninstructed fingers) when visual feedback was presented on the force produced by either two instructed fingers or two noninstructed fingers of the hand. In contrast, force drifts could be in opposite directions depending on the visual feedback. We interpret enslaving and its drifts at the level of control with referent coordinates for the involved muscles, possibly reflecting spread of cortical excitation.

Do Tangential Finger Forces Utilize Mechanical Advantage During Moment of Force production?

This study investigated the beneficial effects of the utilization of mechanical advantage (MA) of finger tangential forces during the moment production. Subjects produced the resistive moment of force against the external torque while the moment arms of the tangential forces were systematically changed. We observed a relatively large contribution to the net moment by the tangential forces with the increased moment arms, whereas the vector sum of normal and tangential forces decreased. The indices of multi-finger coordination for the stabilization of the moment of forces and force direction increased with the moment arms. The current results provide evidence that the utilization of MA is associated with both the efficiency of force production and the stabilization of performance variables.

Effect of Motor Development Levels on Kinematic Synergies During Two-Hand Catching in Children

The ability to coordinate different body parts under different constraints that are imposed by organism, environment, and tasks during motor development might be different in children. The aim of this study was to examine whether children with different motor development levels are different with regard to multijoint coordination during two-hand catching. Eighty-four children (age: 6.05 ±0.67 years) who were assessed on object control skills were recruited voluntarily. The biomechanical model was defined from 20 movements of seven segments (shoulders, elbows, wrists, and torso), and the principal component analysis was used to quantify the multijoint coordination and kinematic synergies during catching. The results showed that the redundancy of joints in two-hand catching is controlled by three kinematic synergies that defined the majority of the variance. The participants who were grouped based on their development levels did not show differences in the number and strength of synergies; however, they were different in the utilization of the kinematic synergies for successful catching. In conclusion, the number and the strength of the kinematic synergies during two-hand catching are not affected by the developmental levels and are related to the nature of the task.

Seven Properties of Self-Organization in the Human Brain

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: (1) modular connectivity, (2) unsupervised learning, (3) adaptive ability, (4) functional resiliency, (5) functional plasticity, (6) from-local-to-global functional organization, and (7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward.

The Visual Perception of Material Properties Affects Motor Planning in Prehension: An Analysis of Temporal and Spatial Components of Lifting Cups

The current study examined the role of visually perceived material properties in motor planning, where we analyzed the temporal and spatial components of motor movements during a seated reaching task. We recorded hand movements of 14 participants in three dimensions while they lifted and transported paper cups that differed in weight and glossiness. Kinematic- and spatial analysis revealed speed-accuracy trade-offs to depend on visual material properties of the objects, in which participants reached slower and grabbed closer to the center of mass for stimuli that required to be handled with greater precision. We found grasp-preparation during the first encounters with the cups was not only governed by the anticipated weight of the cups, but also by their visual material properties, namely glossiness. After a series of object lifting, the execution of reaching, the grip position, and the transportation of the cups from one location to another were preeminently guided by the object weight. We also found the planning phase in reaching to be guided by the expectation of hardness and surface gloss. The findings promote the role of general knowledge of material properties in reach-to-grasp movements, in which visual material properties are incorporated in the spatio-temporal components.

Case Studies in Neuroscience: The central and somatosensory contributions to finger interdependence and coordination: lessons from a study of a "deafferented person"

We tested finger force interdependence and multifinger force-stabilizing synergies in a patient with large-fiber peripheral neuropathy ("deafferented person"). The subject performed a range of tasks involving accurate force production with one finger and with four fingers. In one-finger tasks, nontask fingers showed unintentional force production (enslaving) with an atypical pattern: very large indices for the lateral (index and little) fingers and relatively small indices for the central (middle and ring) fingers. Indices of multifinger synergies stabilizing total force and of anticipatory synergy adjustments in preparation to quick force pulses were similar to those in age-matched control females. During constant force production, removing visual feedback led to a slow force drift to lower values (by ~25% over 15 s). The results support the idea of a neural origin of enslaving and suggest that the patterns observed in the deafferented person were reorganized based on everyday manipulation tasks. The lack of significant changes in the synergy index shows that synergic control can be organized in the absence of somatosensory feedback. We discuss the control of the hand in deafferented persons within the α-model of the equilibrium-point hypothesis and suggest that force drift results from an unintentional drift of the control variables to muscles toward zero values. NEW & NOTEWORTHY We demonstrate atypical patterns of finger enslaving and unchanged force-stabilizing synergies in a person with large-fiber peripheral neuropathy. The results speak strongly in favor of central origin of enslaving and its reorganization based on everyday manipulation tasks. The data show that synergic control can be implemented in the absence of somatosensory feedback. We discuss the control of the hand in deafferented persons within the α-model of the equilibrium-point hypothesis.

Thumbs up: movements made by the thumb are smoother and larger than fingers in finger-thumb opposition tasks

Background

In humans, the thumb plays a crucial role in producing finger opposition movements. These movements form the basis of several activities of the hand. Hence these movements have been used to study phenomena like prehension, motor control, motor learning, etc. Although such tasks have been studied extensively, the relative contribution of the thumb vis-à-vis the fingers in finger opposition tasks is not well understood. In this study, we investigated the kinematics of thumb and fingers in a simple finger opposition task. Further, we quantified the relative contribution and the movement smoothness aspects and compared these between fingers and thumb.

Methods

Eight, young healthy participants (four males and four females) were asked to perform a full finger to thumb opposition movement, where they were required to reach for different phalanges of the fingers. Position (X, Y and Z) of individual segments of the four fingers and the thumb were measured with reference to the wrist by a 16-sensor kinematics measurement system. Displacements and velocities were computed. An index, displacement ratio, that quantifies the relative contribution of thumb and fingers was computed from displacement data. Velocity data was used to quantify the smoothness of movement of thumb and fingers.

Results

The Displacement Ratio showed that contribution of the thumb is higher than contribution of any other target finger or target phalanges, except for the distal phalanx of the index and middle fingers. Smoothness of movement of the thumb was higher than all the finger phalanges in all cases.

Conclusion

We conclude that in the task considered (thumb opposition movements to different targets within the hand & fingers), the thumb made a greater relative contribution in terms of displacement ratio and also produced smoother movements. However, smoothness of thumb did not vary depending on the target. This suggests that the traditional notion of the thumb being a special digit when compared to other fingers is true at least for the opposition movements considered in this study.

COMbined Physical and somatoSEnsory training after stroke: Development and description of a novel intervention to improve upper limb function

BACKGROUND AND PURPOSE

After stroke, reach-to-grasp goal-directed movements are disrupted as a result of both residual motor and somatosensory impairments. This report describes the rationale and development of a new upper limb stroke rehabilitation intervention known as COMPoSE: "COMbined Physical and somatoSEnsory training," designed to improve somatosensory and motor deficits in the upper limb after stroke. A standardized training matrix has been developed to facilitate intervention delivery.

METHODS

The COMPoSE intervention was developed through the following stages: (a) Definition and operationalization of somatosensory and motor variables used in training sensation and movement after stroke; (b) development of methods to give feedback to enhance skill acquisition; and (c) Combination of somatosensory and motor variables, and feedback, into a standardized training matrix. The reporting of the COMPoSE intervention adheres to the recommendations of the Template for Intervention Description and Replication checklist to facilitate replication of the intervention in the future.

RESULTS

The essential features of COMPoSE include combined somatosensory-motor training variables (grasp pressure, distance, object size, crushability, surface texture, and friction), feedback, and calibration using a haptic device providing measures of grasp pressure, use of anticipation trials, and high-dose repetitive task practice. Ten treatment sessions are delivered over 3 weeks, using a standardized matrix for treatment delivery.

CONCLUSION

COMPoSE is a new intervention that combines somatosensory and movement training, delivered synchronously, within the same intervention, and within the same task.

Stability of steady hand force production explored across spaces and methods of analysis

We used the framework of the uncontrolled manifold (UCM) hypothesis and explored the reliability of several outcome variables across different spaces of analysis during a very simple four-finger accurate force production task. Fourteen healthy, young adults performed the accurate force production task with each hand on 3 days. Small spatial finger perturbations were generated by the "inverse piano" device three times per trial (lifting the fingers 1 cm/0.5 s and lowering them). The data were analyzed using the following main methods: (1) computation of indices of the structure of inter-trial variance and motor equivalence in the space of finger forces and finger modes, and (2) analysis of referent coordinates and apparent stiffness values for the hand. Maximal voluntary force and the index of enslaving (unintentional finger force production) showed good to excellent reliability. Strong synergies stabilizing total force were reflected in both structure of variance and motor equivalence indices. Variance within the UCM and the index of motor equivalent motion dropped over the trial duration and showed good to excellent reliability. Variance orthogonal to the UCM and the index of non-motor equivalent motion dropped over the 3 days and showed poor to moderate reliability. Referent coordinate and apparent stiffness indices co-varied strongly and both showed good reliability. In contrast, the computed index of force stabilization showed poor reliability. The findings are interpreted within the scheme of neural control with referent coordinates involving the hierarchy of two basic commands, the r-command and c-command. The data suggest natural drifts in the finger force space, particularly within the UCM. We interpret these drifts as reflections of a trade-off between stability and optimization of action. The implications of these findings for the UCM framework and future clinical applications are explored in the discussion. Indices of the structure of variance and motor equivalence show good reliability and can be recommended for applied studies.

Multi-finger synergies and the muscular apparatus of the hand

We explored whether the synergic control of the hand during multi-finger force production tasks depends on the hand muscles involved. Healthy subjects performed accurate force production tasks and targeted force pulses while pressing against loops positioned at the level of fingertips, middle phalanges, and proximal phalanges. This varied the involvement of the extrinsic and intrinsic finger flexors. The framework of the uncontrolled manifold (UCM) hypothesis was used to analyze the structure of inter-trial variance, motor equivalence, and anticipatory synergy adjustments prior to the force pulse in the spaces of finger forces and finger modes (hypothetical finger-specific control signals). Subjects showed larger maximal force magnitudes at the proximal site of force production. There were synergies stabilizing total force during steady-state phases across all three sites of force production; no differences were seen across the sites in indices of structure of variance, motor equivalence, or anticipatory synergy adjustments. Indices of variance, which did not affect the task (within the UCM), correlated with motor equivalent motion between the steady states prior to and after the force pulse; in contrast, variance affecting task performance did not correlate with non-motor equivalent motion. The observations are discussed within the framework of hierarchical control with referent coordinates for salient effectors at each level. The findings suggest that multi-finger synergies are defined at the level of abundant transformation between the low-dimensional hand level and higher dimensional finger level while being relatively immune to transformations between the finger level and muscle level. The results also support the scheme of control with two classes of neural variables that define referent coordinates and gains in back-coupling loops between hierarchical control levels.

Stability of hand force production. I. Hand level control variables and multifinger synergies

We combined the theory of neural control of movement with referent coordinates and the uncontrolled manifold hypothesis to explore synergies stabilizing the hand action in accurate four-finger pressing tasks. In particular, we tested a hypothesis on two classes of synergies, those among the four fingers and those within a pair of control variables, stabilizing hand action under visual feedback and disappearing without visual feedback. Subjects performed four-finger total force and moment production tasks under visual feedback; the feedback was later partially or completely removed. The "inverse piano" device was used to lift and lower the fingers smoothly at the beginning and at the end of each trial. These data were used to compute pairs of hypothetical control variables. Intertrial analysis of variance within the finger force space was used to quantify multifinger synergies stabilizing both force and moment. A data permutation method was used to quantify synergies among control variables. Under visual feedback, synergies in the spaces of finger forces and hypothetical control variables were found to stabilize total force. Without visual feedback, the subjects showed a force drift to lower magnitudes and a moment drift toward pronation. This was accompanied by disappearance of the four-finger synergies and strong attenuation of the control variable synergies. The indexes of the two types of synergies correlated with each other. The findings are interpreted within the scheme with multiple levels of abundant variables.NEW & NOTEWORTHY We extended the idea of hierarchical control with referent spatial coordinates for the effectors and explored two types of synergies stabilizing multifinger force production tasks. We observed synergies among finger forces and synergies between hypothetical control variables that stabilized performance under visual feedback but failed to stabilize it after visual feedback had been removed. Indexes of two types of synergies correlated with each other. The data suggest the existence of multiple mechanisms stabilizing motor actions.

Multi-Finger Interaction and Synergies in Finger Flexion and Extension Force Production

The aim of this study was to discover finger interaction indices during single-finger ramp tasks and multi-finger coordination during a steady state force production in two directions, flexion, and extension. Furthermore, the indices of anticipatory adjustment of elemental variables (i.e., finger forces) prior to a quick pulse force production were quantified. It is currently unknown whether the organization and anticipatory modulation of stability properties are affected by force directions and strengths of in multi-finger actions. We expected to observe a smaller finger independency and larger indices of multi-finger coordination during extension than during flexion due to both neural and peripheral differences between the finger flexion and extension actions. We also examined the indices of the anticipatory adjustment between different force direction conditions. The anticipatory adjustment could be a neural process, which may be affected by the properties of the muscles and by the direction of the motions. The maximal voluntary contraction (MVC) force was larger for flexion than for extension, which confirmed the fact that the strength of finger flexor muscles (e.g., flexor digitorum profundus) was larger than that of finger extensor (e.g., extensor digitorum). The analysis within the uncontrolled manifold (UCM) hypothesis was used to quantify the motor synergy of elemental variables by decomposing two sources of variances across repetitive trials, which identifies the variances in the uncontrolled manifold (V_UCM) and that are orthogonal to the UCM (V_ORT). The presence of motor synergy and its strength were quantified by the relative amount of V_UCM and V_ORT. The strength of motor synergies at the steady state was larger in the extension condition, which suggests that the stability property (i.e., multi-finger synergies) may be a direction specific quantity. However, the results for the existence of anticipatory adjustment; however, no difference between the directional conditions suggests that feed-forward synergy adjustment (changes in the stability property) may be at least independent of the magnitude of the task-specific apparent performance variables and its direction (e.g., flexion and extension forces).

Taxonomy based analysis of force exchanges during object grasping and manipulation

The flexibility of the human hand in object manipulation is essential for daily life activities, but remains relatively little explored with quantitative methods. On the one hand, recent taxonomies describe qualitatively the classes of hand postures for object grasping and manipulation. On the other hand, the quantitative analysis of hand function has been generally restricted to precision grip (with thumb and index opposition) during lifting tasks. The aim of the present study is to fill the gap between these two kinds of descriptions, by investigating quantitatively the forces exerted by the hand on an instrumented object in a set of representative manipulation tasks. The object was a parallelepiped object able to measure the force exerted on the six faces and its acceleration. The grasping force was estimated from the lateral force and the unloading force from the bottom force. The protocol included eleven tasks with complementary constraints inspired by recent taxonomies: four tasks corresponding to lifting and holding the object with different grasp configurations, and seven to manipulating the object (rotation around each of its axis and translation). The grasping and unloading forces and object rotations were measured during the five phases of the actions: unloading, lifting, holding or manipulation, preparation to deposit, and deposit. The results confirm the tight regulation between grasping and unloading forces during lifting, and extend this to the deposit phase. In addition, they provide a precise description of the regulation of force exchanges during various manipulation tasks spanning representative actions of daily life. The timing of manipulation showed both sequential and overlapping organization of the different sub-actions, and micro-errors could be detected. This phenomenological study confirms the feasibility of using an instrumented object to investigate complex manipulative behavior in humans. This protocol will be used in the future to investigate upper-limb dexterity in patients with sensory-motor impairments.

Multidigit force control during unconstrained grasping in response to object perturbations

Because of the complex anatomy of the human hand, in the absence of external constraints, a large number of postures and force combinations can be used to attain a stable grasp. Motor synergies provide a viable strategy to solve this problem of motor redundancy. In this study, we exploited the technical advantages of an innovative sensorized object to study unconstrained hand grasping within the theoretical framework of motor synergies. Participants were required to grasp, lift, and hold the sensorized object. During the holding phase, we repetitively applied external disturbance forces and torques and recorded the spatiotemporal distribution of grip forces produced by each digit. We found that the time to reach the maximum grip force during each perturbation was roughly equal across fingers, consistent with a synchronous, synergistic stiffening across digits. We further evaluated this hypothesis by comparing the force distribution of human grasping vs. robotic grasping, where the control strategy was set by the experimenter. We controlled the global hand stiffness of the robotic hand and found that this control algorithm produced a force pattern qualitatively similar to human grasping performance. Our results suggest that the nervous system uses a default whole hand synergistic control to maintain a stable grasp regardless of the number of digits involved in the task, their position on the objects, and the type and frequency of external perturbations.NEW & NOTEWORTHY We studied hand grasping using a sensorized object allowing unconstrained finger placement. During object perturbation, the time to reach the peak force was roughly equal across fingers, consistently with a synergistic stiffening across fingers. Force distribution of a robotic grasping hand, where the control algorithm is based on global hand stiffness, was qualitatively similar to human grasping. This suggests that the central nervous system uses a default whole hand synergistic control to maintain a stable grasp.

Parietal area BA7 integrates motor programs for reaching, grasping, and bimanual coordination

Skillful interaction with the world requires that the brain uses a multitude of sensorimotor programs and subroutines, such as for reaching, grasping, and the coordination of the two body halves. However, it is unclear how these programs operate together. Networks for reaching, grasping, and bimanual coordination might converge in common brain areas. For example, Brodmann area 7 (BA7) is known to activate in disparate tasks involving the three types of movements separately. Here, we asked whether BA7 plays a key role in integrating coordinated reach-to-grasp movements for both arms together. To test this, we applied transcranial magnetic stimulation (TMS) to disrupt BA7 activity in the left and right hemispheres, while human participants performed a bimanual size-perturbation grasping task using the index and middle fingers of both hands to grasp a rectangular object whose orientation (and thus grasp-relevant width dimension) might or might not change. We found that TMS of the right BA7 during object perturbation disrupted the bimanual grasp and transport/coordination components, and TMS over the left BA7 disrupted unimanual grasps. These results show that right BA7 is causally involved in the integration of reach-to-grasp movements of the two arms.

NEW & NOTEWORTHY

Our manuscript describes a role of human Brodmann area 7 (BA7) in the integration of multiple visuomotor programs for reaching, grasping, and bimanual coordination. Our results are the first to suggest that right BA7 is critically involved in the coordination of reach-to-grasp movements of the two arms. The results complement previous reports of right-hemisphere lateralization for bimanual grasps.

Digit Position and Forces Covary during Anticipatory Control of Whole-Hand Manipulation

Theoretical perspectives on anticipatory planning of object manipulation have traditionally been informed by studies that have investigated kinematics (hand shaping and digit position) and kinetics (forces) in isolation. This poses limitations on our understanding of the integration of such domains, which have recently been shown to be strongly interdependent. Specifically, recent studies revealed strong covariation of digit position and load force during the loading phase of two-digit grasping. Here, we determined whether such digit force-position covariation is a general feature of grasping. We investigated the coordination of digit position and forces during five-digit whole-hand manipulation of an object with a variable mass distribution. Subjects were instructed to prevent object roll during the lift. As found in precision grasping, there was strong trial-to-trial covariation of digit position and force. This suggests that the natural variation of digit position that is compensated for by trial-to-trial variation in digit forces is a fundamental feature of grasp control, and not only specific to precision grasp. However, a main difference with precision grasping was that modulation of digit position to the object's mass distribution was driven predominantly by the thumb, with little to no modulation of finger position. Modulation of thumb position rather than fingers is likely due to its greater range of motion and therefore adaptability to object properties. Our results underscore the flexibility of the central nervous system in implementing a range of solutions along the digit force-to-position continuum for dexterous manipulation.

The synergic control of multi-finger force production: stability of explicit and implicit task components

Manipulating objects with the hands requires the accurate production of resultant forces including shear forces; effective control of these shear forces also requires the production of internal forces normal to the surface of the object(s) being manipulated. In the present study, we investigated multi-finger synergies stabilizing shear and normal components of force, as well as drifts in both components of force, during isometric pressing tasks requiring a specific magnitude of shear force production. We hypothesized that shear and normal forces would evolve similarly in time and also show similar stability properties as assessed by the decomposition of inter-trial variance within the uncontrolled manifold hypothesis. Healthy subjects were required to accurately produce total shear and total normal forces with four fingers of the hand during a steady-state force task (with and without visual feedback) and a self-paced force pulse task. The two force components showed similar time profiles during both shear force pulse production and unintentional drift induced by turning the visual feedback off. Only the explicitly instructed components of force, however, were stabilized with multi-finger synergies. No force-stabilizing synergies and no anticipatory synergy adjustments were seen for the normal force in shear force production trials. These unexpected qualitative differences in the control of the two force components-which are produced by some of the same muscles and show high degree of temporal coupling-are interpreted within the theory of control with referent coordinates for salient variables. These observations suggest the existence of two classes of neural variables: one that translates into shifts of referent coordinates and defines changes in magnitude of salient variables, and the other controlling gains in back-coupling loops that define stability of the salient variables. Only the former are shared between the explicit and implicit task components.

Contribution of tactile dysfunction to manual motor dysfunction in type II diabetes

INTRODUCTION

Changes in sensory and motor functions of the hand in type II diabetes (T2D) patients have been reported; there is speculation that these changes are driven by tactile dysfunction. The purpose of this study was to evaluate the effects of tactile feedback on manual function in T2D patients.

METHODS

T2D patients and healthy controls underwent median nerve blocks at the wrist and elbow. All participants underwent traditional timed motor evaluations, force dynamometry, laboratory-based kinetic evaluations, and sensory evaluation.

RESULTS

Tactile sensation in the T2D group at baseline was found to be equivalent to tactile function of the control group after median nerve block. Traditional timed evaluation results were negatively impacted by anesthesia, but more sensitive kinetic measures were not impacted.

CONCLUSIONS

These data suggest that mechanisms outside of tactile dysfunction play a significant role in motor dysfunction in T2D. Muscle Nerve 54: 895-902, 2016.

Analytical Inverse Optimization in Two-Hand Prehensile Tasks

The authors explored application of analytical inverse optimization (ANIO) method to the normal finger forces in unimanual and bimanual prehensile tasks with discrete and continuously changing constraints. The subjects held an instrumented handle vertically with one or two hands. The external torque and grip force changed across trials or within a trial continuously. Principal component analysis showed similar percentages of variance accounted for by the first two principal components across tasks and conditions. Compared to unimanual tasks, bimanual tasks showed significantly more frequent inability to find a cost function leading to a stable solution. In cases of stable solutions, similar second-order polynomials were computed as cost functions across tasks and condition. The bimanual tasks, however, showed significantly worse goodness-of-fit index values. The authors show that ANIO can be used in tasks with slowly changing constraints making it an attractive tool to study optimality of performance in special populations. They also show that ANIO can fail in multifinger tasks, likely due to irreproducible behavior across trials, more likely to happen in bimanual tasks compared to unimanual tasks.

Interpersonal synergies: static prehension tasks performed by two actors

We investigated multidigit synergies stabilizing components of the resultant force vector during joint performance of a static prehension task by two persons as compared to similar tasks performed by a single person using both hands. Subjects transferred the instrumented handle from the right hand to the left hand (one-person condition) or passed that handle to another person (two-person condition) while keeping the handle's position and orientation stationary. Only three digits were involved per hand, the thumb, the index finger, and the middle finger; the forces and moments produced by the digits were measured by six-component sensors. We estimated the performance-stabilizing synergies within the uncontrolled manifold framework by quantifying the intertrial variance structure of digit forces and moments. The analysis was performed at three levels: between hands, between virtual finger and virtual thumb (imagined digits producing the same mechanical variables as the corresponding actual digits combined) produced by the two hands (in both interpersonal and intrapersonal conditions), and between the thumb and virtual finger for one hand only. Additionally, we performed correlation and phase synchronization analyses of resultant tangential forces and internal normal forces. Overall, the one-person conditions were characterized by higher amount of intertrial variance that did not affect resultant normal force components, higher internal components of normal forces, and stronger synchronization of the normal forces generated by the hands. Our observations suggest that in two-person tasks, when participants try to achieve a common mechanical outcome, the performance-stabilizing synergies depend on non-visual information exchange, possibly via the haptic and proprioceptive systems. Therefore, synergies quantified in tasks using visual feedback only may not be generalizable to more natural tasks.

Impaired synergic control of posture in Parkinson's patients without postural instability

BACKGROUND

Postural instability is one of most disabling motor symptoms in Parkinson's disease. Indices of multi-muscle synergies are new measurements of movement and postural stability.

OBJECTIVES

Multi-muscle synergies stabilizing vertical posture were studied in Parkinson's disease patients without clinical symptoms of postural instability (Hoehn-Yahr ≤ II) and age-matched controls. We tested the hypothesis that both synergy indices during quiet standing and synergy adjustments to self-triggered postural perturbations would be reduced in patients.

METHODS

Eleven Parkinson's disease patients and 11 controls performed whole-body tasks while standing. Surface electromyography was used to quantify synergy indices stabilizing center of pressure shifts in the anterior-posterior direction during a load-release task.

RESULTS

Parkinson's disease patients showed a significantly lower percentage of variance in the muscle activation space accounted for by the first four principal components, significantly reduced synergy indices during steady state, and significantly reduced anticipatory synergy adjustments (a drop in the synergy index prior to the self-triggered unloading).

CONCLUSIONS

The study demonstrates for the first time that impaired synergic control in Parkinson's disease can be quantified in postural tasks, even in patients without clinical manifestations of postural instability. Synergy measurements may provide a biomarker sensitive for early problems with postural stability in Parkinson's disease.

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.

ThimbleSense: A Fingertip-Wearable Tactile Sensor for Grasp Analysis

Accurate measurement of contact forces between hand and grasped objects is crucial to study sensorimotor control during grasp and manipulation. In this work, we introduce ThimbleSense, a prototype of individual-digit wearable force/torque sensor based on the principle of intrinsic tactile sensing. By exploiting the integration of this approach with an active marker-based motion capture system, the proposed device simultaneously measures absolute position and orientation of the fingertip, which in turn yields measurements of contacts and force components expressed in a global reference frame. The main advantage of this approach with respect to more conventional solutions is its versatility. Specifically, ThimbleSense can be used to study grasping and manipulation of a wide variety of objects, while still retaining complete force/torque measurements. Nevertheless, validation of the proposed device is a necessary step before it can be used for experimental purposes. In this work, we present the results of a series of experiments designed to validate the accuracy of ThimbleSense measurements and evaluate the effects of distortion of tactile afferent inputs caused by the device's rigid shells on grasp forces.

Control of Precision Grip Force in Lifting and Holding of Low-Mass Objects

Few studies have investigated the control of grip force when manipulating an object with an extremely small mass using a precision grip, although some related information has been provided by studies conducted in an unusual microgravity environment. Grip-load force coordination was examined while healthy adults (N = 17) held a moveable instrumented apparatus with its mass changed between 6 g and 200 g in 14 steps, with its grip surface set as either sandpaper or rayon. Additional measurements of grip-force-dependent finger-surface contact area and finger skin indentation, as well as a test of weight discrimination, were also performed. For each surface condition, the static grip force was modulated in parallel with load force while holding the object of a mass above 30 g. For objects with mass smaller than 30 g, on the other hand, the parallel relationship was changed, resulting in a progressive increase in grip-to-load force (GF/LF) ratio. The rayon had a higher GF/LF force ratio across all mass levels. The proportion of safety margin in the static grip force and normalized moment-to-moment variability of the static grip force were also elevated towards the lower end of the object mass for both surfaces. These findings indicate that the strategy of grip force control for holding objects with an extremely small mass differs from that with a mass above 30 g. The data for the contact area, skin indentation, and weight discrimination suggest that a decreased level of cutaneous feedback signals from the finger pads could have played some role in a cost function in efficient grip force control with low-mass objects. The elevated grip force variability associated with signal-dependent and internal noises, and anticipated inertial force on the held object due to acceleration of the arm and hand, could also have contributed to the cost function.

A novel method for the quantification of key components of manual dexterity after stroke

BACKGROUND

A high degree of manual dexterity is a central feature of the human upper limb. A rich interplay of sensory and motor components in the hand and fingers allows for independent control of fingers in terms of timing, kinematics and force. Stroke often leads to impaired hand function and decreased manual dexterity, limiting activities of daily living and impacting quality of life. Clinically, there is a lack of quantitative multi-dimensional measures of manual dexterity. We therefore developed the Finger Force Manipulandum (FFM), which allows quantification of key components of manual dexterity. The purpose of this study was (i) to test the feasibility of using the FFM to measure key components of manual dexterity in hemiparetic stroke patients, (ii) to compare differences in dexterity components between stroke patients and controls, and (iii) to describe individual profiles of dexterity components in stroke patients.

METHODS

10 stroke patients with mild-to-moderate hemiparesis and 10 healthy subjects were recruited. Clinical measures of hand function included the Action Research Arm Test and the Moberg Pick-Up Test. Four FFM tasks were used: (1) Finger Force Tracking to measure force control, (2) Sequential Finger Tapping to measure the ability to perform motor sequences, (3) Single Finger Tapping to measure timing effects, and (4) Multi-Finger Tapping to measure the ability to selectively move fingers in specified combinations (independence of finger movements).

RESULTS

Most stroke patients could perform the tracking task, as well as the single and multi-finger tapping tasks. However, only four patients performed the sequence task. Patients showed less accurate force control, reduced tapping rate, and reduced independence of finger movements compared to controls. Unwanted (erroneous) finger taps and overflow to non-tapping fingers were increased in patients. Dexterity components were not systematically related among each other, resulting in individually different profiles of deficient dexterity. Some of the FFM measures correlated with clinical scores.

CONCLUSIONS

Quantifying some of the key components of manual dexterity with the FFM is feasible in moderately affected hemiparetic patients. The FFM can detect group differences and individual profiles of deficient dexterity. The FFM is a promising tool for the measurement of key components of manual dexterity after stroke and could allow improved targeting of motor rehabilitation.

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.

Prediction of muscle activity during loaded movements of the upper limb

Background

Accurate prediction of electromyographic (EMG) signals associated with a variety of motor behaviors could, in theory, serve as activity templates needed to evoke movements in paralyzed individuals using functional electrical stimulation. Such predictions should encompass complex multi-joint movements and include interactions with objects in the environment.

Methods

Here we tested the ability of different artificial neural networks (ANNs) to predict EMG activities of 12 arm muscles while human subjects made free movements of the arm or grasped and moved objects of different weights and dimensions. Inputs to the trained ANNs included hand position, hand orientation, and thumb grip force.

Results

The ability of ANNs to predict EMG was equally as good for tasks involving interactions with external loads as for unloaded movements. The ANN that yielded the best predictions was a feed-forward network consisting of a single hidden layer of 30 neural elements. For this network, the average coefficient of determination (R² value) between predicted and actual EMG signals across all nine subjects and 12 muscles during movements that involved episodes of moving objects was 0.43.

Conclusion

This reasonable accuracy suggests that ANNs could be used to provide an initial estimate of the complex patterns of muscle stimulation needed to produce a wide array of movements, including those involving object interaction, in paralyzed individuals.

Control of finger forces during fast, slow and moderate rotational hand movements

The goal of this study was to investigate the effect of speed on patterns of grip forces during twisting movement involving forearm supination against a torsional load (combined elastic and inertial load). For slow and moderate speed rotations, the grip force increased linearly with load torque. However, for fast rotations in which the contribution of the inertia to load torque was significantly greater than slower movements, the grip force-load torque relationship could be segmented into two phases: a linear ascending phase corresponding to the acceleration part of the movement followed by a plateau during deceleration. That is, during the acceleration phase, the grip force accurately tracked the combined elastic and inertial load. However, the coupling between grip force and load torque was not consistent during the deceleration phase of the movement. In addition, as speed increased, both the position and the force profiles became smoother. No differences in the baseline grip force, safety margin to secure the grasp during hold phase or the overall change in grip force were observed across different speeds.

Analysis of grasping strategies and function in hemiparetic patients using an instrumented object

This paper validates a novel instrumented object, the iBox, dedicated to the analysis of grasping and manipulation. This instrumented box can be grasped and manipulated, is fitted with an Inertial Measurement Unit (IMU) and can sense the force applied on each side and transmits measured force, acceleration and orientation data wirelessly in real time. The iBox also provides simple access to data for analysing human motor control features such as the coordination between grasping and lifting forces and complex manipulation patterns. A set of grasping and manipulation experiments was conducted with 6 hemiparetic patients and 5 healthy control subjects. Measures made of the forces, kinematics and dynamics are developed, which can be used to analyse grasping and contribute to assessment in patients. Quantitative measurements provided by the iBox reveal numerous characteristics of the grasping strategies and function in patients: variations in the completion time, changes in the force distribution on the object and grasping force levels, difficulties to adjust the level of applied forces to the task and to maintain it, along with movement smoothness decrease and pathological tremor.

Prehension synergies and hand function in early-stage Parkinson's disease

We explored the multi-digit synergies and hand performance in object manipulations and pressing tasks in patients with early-stage Parkinson's disease (PD) and healthy controls. Synergies were defined as inter-trials co-variation patterns among forces/moments produced by individual digits that stabilized a resultant mechanical variable. The subjects performed three main tasks: pressing (steady-state force production followed by a force pulse into the target), prehension (manipulation of a handheld instrumented handle imitating the action of taking a sip from a glass), and functional object manipulation (moving a glass with water as quickly and accurately as possible along a chain of targets). The PD patients were slower compared to controls in all three tasks. Patients showed smaller synergy indices in the pressing and prehension tasks. In the prehension tasks, patients showed elevated grip force at steady states with smaller grip force modulation during the handle motion. PD patients showed smaller feed-forward synergy adjustments in preparation to the quick action in the pressing and (to a smaller degree) prehension tasks. Synergy indices correlated with the time index of performance in the functional glass-with-water task, whereas none of the indices correlated with the Unified PD Rating Scale part III-motor scores. We interpret the results as pointing at an important role of subcortical structures in motor synergies and their feed-forward adjustments to action.