Relation between object properties and EMG during reaching to grasp

In order to stably grasp an object with an artificial hand, a priori knowledge of the object’s properties is a major advantage, especially to ensure subsequent manipulation of the object held by the hand. This is also true for hand prostheses: pre-shaping of the hand while approaching the object, similar to able-bodied, allows the wearer for a much faster and more intuitive way of handling and grasping an object. For hand prostheses, it would be advantageous to obtain this information about object properties from a surface electromyography (sEMG) signal, which is already present and used to control the active prosthetic hand.We describe experiments in which human subjects grasp different objects at different positions while their muscular activity is recorded through eight sEMG electrodes placed on the forearm. Results show that sEMG data, gathered before the hand is in contact with the object, can be used to obtain relevant information on object properties such as size and weight.     

Modelling natural and artificial hands with synergies

We report on recent work in modelling the process of grasping and active touch by natural and artificial hands. Starting from observations made in human hands about the correlation of degrees of freedom in patterns of more frequent use (postural synergies), we consider the implications of a geometrical model accounting for such data, which is applicable to the pre-grasping phase occurring when shaping the hand before actual contact with the grasped object. To extend applicability of the synergy model to study force distribution in the actual grasp, we introduce a modified model including the mechanical compliance of the hand's musculotendinous system. Numerical results obtained by this model indicate that the same principal synergies observed from pre-grasp postural data are also fundamental in achieving proper grasp force distribution. To illustrate the concept of synergies in the dual domain of haptic sensing, we provide a review of models of how the complexity and heterogeneity of sensory information from touch can be harnessed in simplified, tractable abstractions. These abstractions are amenable to fast processing to enable quick reflexes as well as elaboration of high-level percepts. Applications of the synergy model to the design and control of artificial hands and tactile sensors are illustrated.     

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

Background  

Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.     

Altered EMG patterns in diabetic neuropathic and not neuropathic patients during step ascending and descending

Diabetic peripheral neuropathy (DPN) causes motor control alterations during daily life activities. Tripping during walking or stair climbing is the predominant cause of falls in the elderly subjects with DPN and without (NoDPN). Surface Electromyography (sEMG) has been shown to be a valid tool for detecting alterations of motor functions in subjects with DPN. This study aims at investigating the presence of functional alterations in diabetic subjects during stair climbing and at exploring the relationship between altered muscle activation and temporal parameter. Lower limb muscle activities, temporal parameters and speed were evaluated in 50 subjects (10 controls, 20 with DPN, 20 without DPN), while climbing up and down a stair, using sEMG, three-dimentional motion capture and force plates. Magnitude and timing of sEMG linear envelopes peaks were extracted. Level walking was used as reference condition for the comparison with step negotiation. sEMG, speed and temporal parameters revealed significant differences among all groups of patients. Results showed an association between earlier activation of lower limb muscles and reduced speed in subjects with DPN. Speed and temporal parameters significantly correlated with sEMG (p < 0.05). The findings of this study are encouraging and could be used to improve rehabilitation programs aiming at reducing falls risk in diabetic subjects.     

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.     

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.     

On the analysis of hand synergies during grasping in weightlessness

The aim of this paper was to analyse how the strategies implemented by the Central Nervous System to control the hand during grasping are modified under microgravity conditions. Two right-handed subjects carried out simple grasping tasks during parabolic flights. The trajectories of the fingers of the hand were recorded using a sensorised glove and processed in order to extract a variable (here indicated as K) which can indicated the degree of synergies existing among the fingers. The results showed that K was quite small during the trial at 1g while becoming significantly greater than 1 during the first parabolas. Then, the value k decreased to the values at 1 g after some parabolas. These results suggested a possible adaptation process of the manipulation abilities during the permanence at 0g conditions. Future extensive trials will be performed in order to confirm these preliminary results.     

Distinct olfactory cross-modal effects on the human motor system

Background

Converging evidence indicates that action observation and action-related sounds activate cross-modally the human motor system. Since olfaction, the most ancestral sense, may have behavioural consequences on human activities, we causally investigated by transcranial magnetic stimulation (TMS) whether food odour could additionally facilitate the human motor system during the observation of grasping objects with alimentary valence, and the degree of specificity of these effects.

Methodology/Principal Findings

In a repeated-measure block design, carried out on 24 healthy individuals participating to three different experiments, we show that sniffing alimentary odorants immediately increases the motor potentials evoked in hand muscles by TMS of the motor cortex. This effect was odorant-specific and was absent when subjects were presented with odorants including a potentially noxious trigeminal component.The smell-induced corticospinal facilitation of hand muscles during observation of grasping was an additive effect which superimposed to that induced by the mere observation of grasping actions for food or non-food objects. The odour-induced motor facilitation took place only in case of congruence between the sniffed odour and the observed grasped food, and specifically involved the muscle acting as prime mover for hand/fingers shaping in the observed action.

Conclusions/Significance

Complex olfactory cross-modal effects on the human corticospinal system are physiologically demonstrable. They are odorant-specific and, depending on the experimental context, muscle- and action-specific as well. This finding implies potential new diagnostic and rehabilitative applications.     

Object Grasping using the Minimum Variance Model

Reaching-to-grasp has generally been classified as the coordination of two separate visuomotor processes: transporting the hand to the target object and performing the grip. An alternative view has recently been formed that grasping can be explained as pointing movements performed by the digits of the hand to target positions on the object. We have previously implemented the minimum variance model of human movement as an optimal control scheme suitable for control of a robot arm reaching to a target. Here, we extend that scheme to perform grasping movements with a hand and arm model. Since the minimum variance model requires that signal-dependent noise be present on the motor commands to the actuators of the movement, our approach is to plan the reach and the grasp separately, in line with the classical view, but using the same computational model for pointing, in line with the alternative view. We show that our model successfully captures some of the key characteristics of human grasping movements, including the observations that maximum grip size increases with object size (with a slope of approximately 0.8) and that this maximum grip occurs at 60–80% of the movement time. We then use our model to analyse contributions to the digit end-point variance from the two components of the grasp (the transport and the grip). We also briefly discuss further areas of investigation that are prompted by our model.     

Handwriting: Hand–pen contact force synergies in circle drawing tasks

This study investigated synergistic actions of hand–pen contact forces during circle drawing tasks in three-dimensional (3D) space. Twenty-four right-handed participants drew thirty concentric circles in the counterclockwise (CCW) and clockwise (CW) directions. Three-dimensional forces acting on an instrumented pen as well as 3D linear and angular positions of the pen were recorded. These contact forces were then transformed into the 3D radial, tangential, and normal force components specific to circle drawing. Uncontrolled manifold (UCM) analysis was employed to calculate the magnitude of the hand–pen contact force synergy. Three hypotheses were tested. First, hand–pen contact force synergies during circle drawing are dependent on the angular position of the pen tip. Second, hand–pen contact force synergies are dependent on force components in circle drawing. Third, hand–pen contact force synergies are greater in CCW direction than CW direction. The results showed that the strength of the hand–pen contact force synergy increased during the initial phase of circle drawing and decreased during the final phase. The synergy strength was greater for the radial and tangential components as compared to the normal component. Also, the circle drawing in CW direction was associated with greater hand–pen contact force synergy than the CCW direction. The results of this study suggest that the central nervous system (CNS) prioritizes hand–pen contact force synergies for the force components (i.e., radial and tangential) that are critical for circle drawing. The CNS modulates hand–pen contact force synergies for preparation and conclusion of circle drawing, respectively.     

Bio-inspired grasp control in a robotic hand with massive sensorial input

The capability of grasping and lifting an object in a suitable, stable and controlled way is an outstanding feature for a robot, and thus far, one of the major problems to be solved in robotics. No robotic tools able to perform an advanced control of the grasp as, for instance, the human hand does, have been demonstrated to date. Due to its capital importance in science and in many applications, namely from biomedics to manufacturing, the issue has been matter of deep scientific investigations in both the field of neurophysiology and robotics. While the former is contributing with a profound understanding of the dynamics of real-time control of the slippage and grasp force in the human hand, the latter tries more and more to reproduce, or take inspiration by, the nature’s approach, by means of hardware and software technology. On this regard, one of the major constraints robotics has to overcome is the real-time processing of a large amounts of data generated by the tactile sensors while grasping, which poses serious problems to the available computational power. In this paper a bio-inspired approach to tactile data processing has been followed in order to design and test a hardware–software robotic architecture that works on the parallel processing of a large amount of tactile sensing signals. The working principle of the architecture bases on the cellular nonlinear/neural network (CNN) paradigm, while using both hand shape and spatial–temporal features obtained from an array of microfabricated force sensors, in order to control the sensory-motor coordination of the robotic system. Prototypical grasping tasks were selected to measure the system performances applied to a computer-interfaced robotic hand. Successful grasps of several objects, completely unknown to the robot, e.g. soft and deformable objects like plastic bottles, soft balls, and Japanese tofu, have been demonstrated.     

Development of an Anthropomorphic Robotic Arm and Hand for Interactive Humanoids

Humanoid robots are designed and built to mimic human form and movement.Ultimately,they are meant to resemble the size and physical abilities of a human in order to function in human-oriented environments and to work autonomously but to pose no physical threat to humans.Here,a humanoid robot that resembles a human in appearance and movement is built using powerful actuators paired with gear trains,joint mechanisms,and motor drivers that are all encased in a package no larger than that of the human physique.In this paper,we propose the construction of a humanoid-applicable anthropomorphic 7-DoF arm complete with an 8-DoF hand.The novel mechanical design of this humanoid arm makes it sufficiently compact to be compatible with currently available narrating-model humanoids,and to be sufficiently powerful and flexible to be functional; the number of degrees of freedom endowed in this robotic arm is sufficient for executing a wide range of tasks,including dexterous hand movements.The developed humanoid arm and hand are capable of sensing and interpreting incoming external force using the motor in each joint current without conventional torque sensors.The humanoid arm adopts an algorithm to avoid obstacles and the dexterous hand is capable of grasping objects.The developed robotic arm is suitable for use in an interactive humanoid robot.     

Neural basis of learning new movements: evolution of classical concepts

According to classical consepts, the role of the motor cortex in performance of skilled movements of distal parts of extremities is confined to control of appropriate motoneurons by the "point-to-point" principle. However, much evidence of plasticity of the motor cortex and its active role in motor learning appeared in last decade. Fos-gene expression in the motor cortex was found to accompany learning a skill. Strengthening of horizontal pathways in layers II-III was revealed, and cholinergic input to tese layers was found to be important. The imaging data show that activity of the motor cortex increases during motor practice as well. This raises the question of specificity of the motor cortex in the motor learning per se. During acquisition of new movements some previously used synergies prevent the necessary coordination from being learned, so they must be suppressed in the process of motor learning. Investigations of central mechanisms of coordination interference in humans are still at the beginning. However, there are some animal models of reorganization and suppression of interfering synergies. The reorganization and suppression of coordination preventing realization of a new movement is shown to be a specific function of the motor cortex. After automation of new synergies the cortical control is still present, as distinct from the learned movements, which do not require suppression of interfering synergies. However, it does not mean that the conscious control of the performance is still present.     

Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography

The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements. However, the sEMG spatial distribution on the forearm during hand and finger movements and its changes due to different hand positions has never been quantified. The aim of this work is to quantify 1) the spatial localization of surface EMG activity of distinct forearm muscles during dynamic free movements of wrist and single fingers and 2) the effect of hand position on sEMG activity distribution. The subjects performed cyclic dynamic tasks involving the wrist and the fingers. The wrist tasks and the hand opening/closing task were performed with the hand in prone and neutral positions. A sensorized glove was used for kinematics recording. sEMG signals were acquired from the forearm muscles using a grid of 112 electrodes integrated into a stretchable textile sleeve. The areas of sEMG activity have been identified by a segmentation technique after a data dimensionality reduction step based on Non Negative Matrix Factorization applied to the EMG envelopes. The results show that 1) it is possible to identify distinct areas of sEMG activity on the forearm for different fingers; 2) hand position influences sEMG activity level and spatial distribution. This work gives new quantitative information about sEMG activity distribution on the forearm in healthy subjects and provides a basis for future works on the identification of optimal electrode configuration for sEMG based control of prostheses, exoskeletons, or orthoses. An example of use of this information for the optimization of the detection system for the estimation of joint kinematics from sEMG is reported.     

Precision grasping in humans: from motor control to cognition

In the past decade, functional neuroimaging has proved extremely useful in mapping the human motor circuits involved in skilled hand movements. However, one major drawback of this approach is the impossibility to determine the exact contribution of each individual cortical area to precision grasping. Because transcranial magnetic stimulation (TMS) makes it possible to induce a transient 'virtual' lesion of discrete brain regions in healthy subjects, it has been extensively used to provide direct insight into the causal role of a given area in human motor behaviour. Recent TMS studies have allowed us to determine the specific contribution, as well as the timing and the hemispheric lateralisation, of distinct parietal and frontal areas to the control of both the kinematics and dynamics of precision grasping. Moreover, recent researches have shown that the same cortical network may contribute to language and number processing, supporting the existence of tight interactions between processes involved in cognition and actions. The aim of this paper is to offer a concise overview of recent studies that have investigated the neural correlates of precision grasping and the possible contribution of the motor system to higher cognitive functions such as language and number processing.     

Toward an objective interpretation of surface EMG patterns: a voluntary response index (VRI).

Individuals with incomplete spinal cord injuries (SCI) retain varying degrees of voluntary motor control. The complexity of the motor control system and the nature of the recording biophysics have inhibited efforts to develop objective measures of voluntary motor control. This paper proposes the definition and use of a voluntary response index (VRI) calculated from quantitative analysis of surface electromyographic (sEMG) data recorded during defined voluntary movement as a sensitive measure of voluntary motor control in such individuals. The VRI is comprised of two numeric values, one derived from the total muscle activity recorded for the voluntary motor task (magnitude), and the other from the sEMG distribution across the recorded muscles (similarity index (SI)). Calculated as a vector, the distribution of sEMG from the test subject is compared to the average vector calculated from sEMG recordings of the same motor task from 10 neurologically intact subjects in a protocol called brain motor control assessment (BMCA). To evaluate the stability of the VRI, a group of five healthy subjects were individually compared to the prototype, average healthy-subject vectors for all of the maneuvers. To evaluate the sensitivity of this method, the VRI was obtained from two SCI subjects participating in other research studies. One was undergoing supported treadmill ambulation training, and the other a controlled withdrawal of anti-spasticity medications. The supported treadmill training patient's VRI, calculated from pre- and post-training BMCA recordings, reflected the qualitative changes in sEMG patterns and functional improvement of motor control. The VRI of the patient followed by serial BMCA during medication withdrawal also reflected changes in the motor control as a result of changes in anti-spasticity medication. To validate this index for clinical use, serial studies using larger numbers of subjects with compromised motor control should be performed.     

Evaluation of Respiratory Muscle Activation Using Respiratory Motor Control Assessment (RMCA) in Individuals with Chronic Spinal Cord Injury

During breathing, activation of respiratory muscles is coordinated by integrated input from the brain, brainstem, and spinal cord. When this coordination is disrupted by spinal cord injury (SCI), control of respiratory muscles innervated below the injury level is compromised^1,2 leading to respiratory muscle dysfunction and pulmonary complications. These conditions are among the leading causes of death in patients with SCI³. Standard pulmonary function tests that assess respiratory motor function include spirometrical and maximum airway pressure outcomes: Forced Vital Capacity (FVC), Forced Expiratory Volume in one second (FEV₁), Maximal Inspiratory Pressure (PI_max) and Maximal Expiratory Pressure (PE_max)^4,5. These values provide indirect measurements of respiratory muscle performance⁶. In clinical practice and research, a surface electromyography (sEMG) recorded from respiratory muscles can be used to assess respiratory motor function and help to diagnose neuromuscular pathology. However, variability in the sEMG amplitude inhibits efforts to develop objective and direct measures of respiratory motor function⁶. Based on a multi-muscle sEMG approach to characterize motor control of limb muscles⁷, known as the voluntary response index (VRI)⁸, we developed an analytical tool to characterize respiratory motor control directly from sEMG data recorded from multiple respiratory muscles during the voluntary respiratory tasks. We have termed this the Respiratory Motor Control Assessment (RMCA)⁹. This vector analysis method quantifies the amount and distribution of activity across muscles and presents it in the form of an index that relates the degree to which sEMG output within a test-subject resembles that from a group of healthy (non-injured) controls. The resulting index value has been shown to have high face validity, sensitivity and specificity^9-11. We showed previously⁹ that the RMCA outcomes significantly correlate with levels of SCI and pulmonary function measures. We are presenting here the method to quantitatively compare post-spinal cord injury respiratory multi-muscle activation patterns to those of healthy individuals.     

Grasping without Sight: Insights from the Congenitally Blind

We reach for and grasp different sized objects numerous times per day. Most of these movements are visually-guided, but some are guided by the sense of touch (i.e. haptically-guided), such as reaching for your keys in a bag, or for an object in a dark room. A marked right-hand preference has been reported during visually-guided grasping, particularly for small objects. However, little is known about hand preference for haptically-guided grasping. Recently, a study has shown a reduction in right-hand use in blindfolded individuals, and an absence of hand preference if grasping was preceded by a short haptic experience. These results suggest that vision plays a major role in hand preference for grasping. If this were the case, then one might expect congenitally blind (CB) individuals, who have never had a visual experience, to exhibit no hand preference. Two novel findings emerge from the current study: first, the results showed that contrary to our expectation, CB individuals used their right hand during haptically-guided grasping to the same extent as visually-unimpaired (VU) individuals did during visually-guided grasping. And second, object size affected hand use in an opposite manner for haptically- versus visually-guided grasping. Big objects were more often picked up with the right hand during haptically-guided, but less often during visually-guided grasping. This result highlights the different demands that object features pose on the two sensory systems. Overall the results demonstrate that hand preference for grasping is independent of visual experience, and they suggest a left-hemisphere specialization for the control of grasping that goes beyond sensory modality.     

Stiffness map of the grasping contact areas of the human hand

The elasticity and damping of the soft tissues of the hand contribute to dexterity while grasping and also help to stabilise the objects in manipulation tasks. Although some previous works have studied the force-displacement response of the fingertips, the responses in all other regions of the hand that usually participate in grasping have not been analysed to date. In this work we performed experimental measurements in 20 subjects to obtain a stiffness map of the different grasping contact areas of the human hand. A force-displacement apparatus was used to simultaneously measure force and displacement at 39 different points on the hand at six levels of force ranging from 1 N to 6 N. A non-linear force-displacement response was found for all points, with stiffness increasing with the amount of force applied. Mean stiffness for the different points and force levels was within the range from 0.2 N/mm to 7.7 N/mm. However, the stiffness range and variation with level of force were found to be different from point to point. A total of 13 regions with similar stiffness behaviours were identified. The stiffness in the fingertips increased linearly with the amount of force applied, while in the palm it remained more constant for the range of forces considered. It is hypothesised that the differences in the stiffness behaviour from one region to another allow these regions to play different roles during grasping.     

Distinct haptic cues do not reduce interference when learning to reach in multiple force fields

Background

Previous studies of learning to adapt reaching movements in the presence of novel forces show that learning multiple force fields is prone to interference. Recently it has been suggested that force field learning may reflect learning to manipulate a novel object. Within this theoretical framework, interference in force field learning may be the result of static tactile or haptic cues associated with grasp, which fail to indicate changing dynamic conditions. The idea that different haptic cues (e.g. those associated with different grasped objects) signal motor requirements and promote the learning and retention of multiple motor skills has previously been unexplored in the context of force field learning.

Methodology/Principle Findings

The present study tested the possibility that interference can be reduced when two different force fields are associated with differently shaped objects grasped in the hand. Human subjects were instructed to guide a cursor to targets while grasping a robotic manipulandum, which applied two opposing velocity-dependent curl fields to the hand. For one group of subjects the manipulandum was fitted with two different handles, one for each force field. No attenuation in interference was observed in these subjects relative to controls who used the same handle for both force fields.

Conclusions/Significance

These results suggest that in the context of the present learning paradigm, haptic cues on their own are not sufficient to reduce interference and promote learning multiple force fields.