The effects of precision demands during a low intensity pinching task on muscle activation and load sharing of the fingers.

High precision demands in manual tasks can be expected to cause more selective use of a part of the muscular synergy involved. To test this expectation, load sharing of the index finger and middle finger was investigated during a pinching task. Myoelectric activation of lower arm and neck-shoulder muscles was measured to see if overall level of effort was affected by precision demands. Ten healthy female subjects performed pinching tasks with three levels of force and three levels of precision demands. The force level did not significantly affect the relative contribution of the index and middle finger to the force. Higher precision demands, however, led to higher contribution of the index finger to the pinch force. Consequently, a more selective load of the forearm and hand occurs during tasks with high precision demands. The variability of the force contribution of the fingers increased during the task. No effects of precision demand on the activation of forearm and neck-shoulder muscles were found. Force level did affect the EMG parameters of several muscles. The effects were most apparent in the muscles responsible for the pinch force, the forearm muscles. Activation of these muscles was higher at higher force levels. In the trapezius muscle at the dominant side EMG amplitudes were lower at the high pinch force compared to the low force and median force conditions.     

High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control

Pattern recognition based control of powered upper limb myoelectric prostheses offers a means of extracting more information from the available muscles than conventional methods. By identifying repeatable patterns of muscle activity across multiple muscle sites rather than relying on independent EMG signals it is possible to provide more natural, reliable control of myoelectric prostheses. The purposes of this study were to (1) determine if participants can perform distinctive muscle activation patterns associated with multiple wrist and hand movements reliably and (2) to show that high density EMG can be applied individually to determine the electrode location of a clinically acceptable number of electrodes (maximally eight) to classify multiple wrist and hand movements reliably in transradial amputees. Eight normally limbed subjects (five female, three male) and four transradial amputee subjects (two traumatic and congenital) subjects participated in this study, which examined the classification accuracies of a pattern recognition control system. It was found that tasks could be classified with high accuracy (85-98%) with normally limbed subjects (10-13 tasks) and with amputees (4-6) tasks. In healthy subjects, reducing the number of electrodes to eight did not affect accuracy significantly when those electrodes were optimally placed, but did reduce accuracy significantly when those electrodes were distributed evenly. In the amputee subjects, reducing the number of electrodes up to 4 did not affect classification accuracy or the number of tasks with high accuracy, independent of whether those remaining electrodes were evenly distributed or optimally placed. The findings in healthy subjects suggest that high density EMG testing is a useful tool to identify optimal electrode sites for pattern recognition control, but its use in amputees still has to be proven. Instead of just identifying the electrode sites where EMG activity is strong, clinicians will be able to choose the electrode sites that provide the most important information for classification.     

A systematic method to quantify the presence of cross-talk in stimulus-evoked EMG responses: implications for TMS studies

Surface electromyography (EMG) responses to noninvasive nerve and brain stimulation are routinely used to provide insight into neural function in humans. However, this could lead to erroneous conclusions if evoked EMG responses contain significant contributions from neighboring muscles (i.e., due to "cross-talk"). We addressed this issue with a simple nerve stimulation method to provide quantitative information regarding the size of EMG cross-talk between muscles of the forearm and hand. Peak to peak amplitude of EMG responses to electrical stimulation of the radial, median, and ulnar nerves (i.e., M-waves) were plotted against stimulation intensity for four wrist muscles and two hand muscles (n = 12). Since electrical stimulation can selectively activate specific groups of muscles, the method can differentiate between evoked EMG arising from target muscles and EMG cross-talk arising from nontarget muscles. Intramuscular EMG responses to nerve stimulation and root mean square EMG produced during maximal voluntary contractions (MVC) of the wrist were recorded for comparison. Cross-talk was present in evoked surface EMG responses recorded from all nontarget wrist (5.05-39.38% Mmax) and hand muscles (1.50-24.25% Mmax) and to a lesser degree in intramuscular EMG signals (～3.7% Mmax). The degree of cross-talk was comparable for stimulus-evoked responses and voluntary activity recorded during MVC. Since cross-talk can make a considerable contribution to EMG responses in forearm and hand muscles, care is required to avoid misinterpretation of EMG data. The multiple nerve stimulation method described here can be used to quantify the potential contribution of EMG cross-talk in transcranial magnetic stimulation and reflex studies.     

Residual Upper Arm Motor Function Primes Innervation of Paretic Forearm Muscles in Chronic Stroke after Brain-Machine Interface (BMI) Training

Background

Abnormal upper arm-forearm muscle synergies after stroke are poorly understood. We investigated whether upper arm function primes paralyzed forearm muscles in chronic stroke patients after Brain-Machine Interface (BMI)-based rehabilitation. Shaping upper arm-forearm muscle synergies may support individualized motor rehabilitation strategies.

Methods

Thirty-two chronic stroke patients with no active finger extensions were randomly assigned to experimental or sham groups and underwent daily BMI training followed by physiotherapy during four weeks. BMI sessions included desynchronization of ipsilesional brain activity and a robotic orthosis to move the paretic limb (experimental group, n = 16). In the sham group (n = 16) orthosis movements were random. Motor function was evaluated with electromyography (EMG) of forearm extensors, and upper arm and hand Fugl-Meyer assessment (FMA) scores. Patients performed distinct upper arm (e.g., shoulder flexion) and hand movements (finger extensions). Forearm EMG activity significantly higher during upper arm movements as compared to finger extensions was considered facilitation of forearm EMG activity. Intraclass correlation coefficient (ICC) was used to test inter-session reliability of facilitation of forearm EMG activity.

Results

Facilitation of forearm EMG activity ICC ranges from 0.52 to 0.83, indicating fair to high reliability before intervention in both limbs. Facilitation of forearm muscles is higher in the paretic as compared to the healthy limb (p<0.001). Upper arm FMA scores predict facilitation of forearm muscles after intervention in both groups (significant correlations ranged from R = 0.752, p = 0.002 to R = 0.779, p = 0.001), but only in the experimental group upper arm FMA scores predict changes in facilitation of forearm muscles after intervention (R = 0.709, p = 0.002; R = 0.827, p<0.001).

Conclusions

Residual upper arm motor function primes recruitment of paralyzed forearm muscles in chronic stroke patients and predicts changes in their recruitment after BMI training. This study suggests that changes in upper arm-forearm synergies contribute to stroke motor recovery, and provides candidacy guidelines for similar BMI-based clinical practice.     

A musculoskeletal model of the hand and wrist: model definition and evaluation

Abstract

To improve our understanding on the neuromechanics of finger movements, a comprehensive musculoskeletal model is needed. The aim of this study was to build a musculoskeletal model of the hand and wrist, based on one consistent data set of the relevant anatomical parameters. We built and tested a model including the hand and wrist segments, as well as the muscles of the forearm and hand in OpenSim. In total, the model comprises 19 segments (with the carpal bones modeled as one segment) with 23 degrees of freedom and 43 muscles. All required anatomical input data, including bone masses and inertias, joint axis positions and orientations as well as muscle morphological parameters (i.e. PCSA, mass, optimal fiber length and tendon length) were obtained from one cadaver of which the data set was recently published. Model validity was investigated by first comparing computed muscle moment arms at the index finger metacarpophalangeal (MCP) joint and wrist joint to published reference values. Secondly, the muscle forces during pinching were computed using static optimization and compared to previously measured intraoperative reference values. Computed and measured moment arms of muscles at both index MCP and wrist showed high correlation coefficients (r?=?0.88 averaged across all muscles) and modest root mean square deviation (RMSD?=?23% averaged across all muscles). Computed extrinsic flexor forces of the index finger during index pinch task were within one standard deviation of previously measured in-vivo tendon forces. These results provide an indication of model validity for use in estimating muscle forces during static tasks.     

An EMG-CT method using multiple surface electrodes in the forearm

Electromyography computed tomography (EMG-CT) method is proposed for visualizing the individual muscle activities in the human forearm. An EMG conduction model was formulated for reverse-estimation of muscle activities using EMG signals obtained with multi surface electrodes. The optimization process was calculated using sequential quadratic programming by comparing the estimated EMG values from the model with the measured values. The individual muscle activities in the deep region were estimated and used to produce an EMG tomographic image. For validation of the method, isometric contractions of finger muscles were examined for three subjects, applying a flexion load (4.9, 7.4 and 9.8 N) to the proximal interphalangeal joint of the middle finger. EMG signals in the forearm were recorded during the tasks using multiple surface electrodes, which were bound around the subject’s forearm. The EMG-CT method illustrates the distribution of muscle activities within the forearm. The change in amplitude and area of activated muscles can be observed. The normalized muscle activities of all three subjects appear to increase monotonically with increases in the load. Kinesiologically, this method was able to estimate individual muscle activation values and could provide a novel tool for studying hand function and development of an examination for evaluating rehabilitation.     

An Anthropomorphic Robot Hand Developed Based on Underactuated Mechanism and Controlled by EMG Signals

When developing a humanoid myo-control hand,not only the mechanical structure should be considered to afford a highdexterity,but also the myoelectric (electromyography,EMG) control capability should be taken into account to fully accomplishthe actuation tasks.This paper presents a novel humanoid robotic myocontrol hand (AR hand Ⅲ) which adopted an underac-tuated mechanism and a forearm myocontrol EMG method.The AR hand Ⅲ has five fingers and 15 joints,and actuated by threeembedded motors.Underactuation can be found within each finger and between the rest three fingers (the middle finger,the ringfinger and the little finger) when the hand is grasping objects.For the EMG control,two specific methods are proposed:thethree-fingered hand gesture configuration of the AR hand Ⅲ and a pattern classification method of EMG signals based on astatistical learning algorithm-Support Vector Machine (SVM).Eighteen active hand gestures of a testee are recognized ef-fectively,which can be directly mapped into the motions of AR hand Ⅲ.An on-line EMG control scheme is established basedon two different decision functions:one is for the discrimination between the idle and active modes,the other is for the recog-nition of the active modes.As a result,the AR hand Ⅲ can swiftly follow the gesture instructions of the testee with a time delayless than 100 ms.     

Motor control theories improve biomechanical model of the hand for finger pressing tasks

BackgroundBiomechanical models are a useful tool to estimate tendon tensions. Unfortunately, in previous fingers' models, each finger acts independently from the others. This is contradictory with hand motor control theories which show that fingers are functionally linked in order to balance the wrist/forearm joint with minimal tendon tensions. (i.e. principle of minimization of the secondary moments). We propose to adapt a hand biomechanical model according to this principle by including the wrist joint. We will determine whether the finger tendon tensions changed with the wrist joint added to the model.MethodsTwo models have been tested: one considering fingers independently (model A) and one with the fingers mechanically linked by the inclusion of the wrist balance (model B). A single set of data, additional results from the literature and in-vivo values have been used to compare the results.ResultsModel A corroborates previous results in the literature. Contrast results were obtained with model B, especially for the Ring and Little fingers. Different tendon tensions were obtained, particularly, in finger extensor muscles critical to balance the wrist.DiscussionWe discuss the biomechanical results in accordance with the hand/finger motor control theories. It appears that the wrist joint balance is critical for finger tendon tension estimation. When including the wrist joint into finger models, the tendon tension estimations agree well with the minimization of secondary moments and the force deficit.     

Intra-session and inter-day reliability of forearm surface EMG during varying hand grip forces

Surface electromyography (EMG) is widely used to evaluate forearm muscle function and predict hand grip forces; however, there is a lack of literature on its intra-session and inter-day reliability. The aim of this study was to determine reliability of surface EMG of finger and wrist flexor muscles across varying grip forces. Surface EMG was measured from six forearm flexor muscles of 23 healthy adults. Eleven of these subjects undertook inter-day test–retest. Six repetitions of five randomized isometric grip forces between 0% and 80% of maximum force (MVC) were recorded and normalized to MVC. Intra- and inter-day reliability were calculated through the intraclass correlation coefficient (ICC) and standard error of measurement (SEM).Normalized EMG produced excellent intra-session ICC of 0.90 when repeated measurements were averaged. Intra-session SEM was low at low grip forces, however, corresponding normalized SEM was high (23–45%) due to the small magnitude of EMG signals. This may limit the ability to evaluate finer forearm muscle function and hand grip forces in daily tasks. Combining EMG of functionally related muscles improved intra-session SEM, improving within-subject reliability without taking multiple measurements. Removing and replacing electrodes inter-day produced poor ICC (ICC < 0.50) but did not substantially affect SEM.     

Incorporating the length-dependent passive-force generating muscle properties of the extrinsic finger muscles into a wrist and finger biomechanical musculoskeletal model

Dynamic movement trajectories of low mass systems have been shown to be predominantly influenced by passive viscoelastic joint forces and torques compared to momentum and inertia. The hand is comprised of 27 small mass segments. Because of the influence of the extrinsic finger muscles, the passive torques about each finger joint become a complex function dependent on the posture of multiple joints of the distal upper limb. However, biomechanical models implemented for the dynamic simulation of hand movements generally don’t extend proximally to include the wrist and distal upper limb. Thus, they cannot accurately represent these complex passive torques. The purpose of this short communication is to both describe a method to incorporate the length-dependent passive properties of the extrinsic index finger muscles into a biomechanical model of the upper limb and to demonstrate their influence on combined movement of the wrist and fingers. Leveraging a unique set of experimental data, that describes the net passive torque contributed by the extrinsic finger muscles about the metacarpophalangeal joint of the index finger as a function of both metacarpophalangeal and wrist postures, we simulated the length-dependent passive properties of the extrinsic finger muscles. Dynamic forward simulations demonstrate that a model including these properties passively exhibits coordinated movement between the wrist and finger joints, mimicking tenodesis, a behavior that is absent when the length-dependent properties are removed. This work emphasizes the importance of incorporating the length-dependent properties of the extrinsic finger muscles into biomechanical models to study healthy and impaired hand movements.     

Thumb postures and physical loads during mobile phone use – A comparison of young adults with and without musculoskeletal symptoms

The aim of this study was to evaluate thumb postures, thumb movements and muscle activity when using mobile phones for SMS messaging and to determine whether there were differences in these exposures (a) across various mobile phone tasks, (b) between gender and (c) between subjects with and without musculoskeletal symptoms in shoulders and upper extremities. Fifty-six young adults (15 healthy and 41 with musculoskeletal symptoms) performed a series of distinct tasks on a mobile phone. Muscular load in four forearm/hand muscles in the right arm and the right and left trapezius muscles were measured using electromyography (EMG). Thumb movements were registered using an electrogoniometer. The results showed that postures (sitting or standing) and the type of mobile phone task (holding the phone versus texting) affected muscle activity and thumb positions. Females compared to males had higher muscle activity in the extensor digitorum and the abductor pollicis longus when entering SMS messages and tended to have greater thumb abduction, higher thumb movement velocities and fewer pauses in the thumb movements. Subjects with symptoms had lower muscle activity levels in the abductor pollicis longus and tended to have higher thumb movement velocities and fewer pauses in the thumb movements compared to those without symptoms.     

Neural Coding of Finger and Wrist Movements

Previous work (Schieber and Hibbard, 1993) has shown that single motor cortical neurons do not discharge specifically for a particular flexion-extension finger movement but instead are active with movements of different fingers. In addition, neuronal populations active with movements of different fingers overlap extensively in their spatial locations in the motor cortex. These data suggested that control of any finger movement utilizes a distributed population of neurons. In this study we applied the neuronal population vector analysis (Georgopoulos et al., 1983) to these same data to determine (1) whether single cells are tuned in an abstract, three-dimensional (3D) instructed finger and wrist movement space with hand-like geometry and (2) whether the neuronal population encodes specific finger movements. We found that the activity of 132/176 (75%) motor cortical neurons related to finger movements was indeed tuned in this space. Moreover, the population vector computed in this space predicted well the instructed finger movement. Thus, although single neurons may be related to several disparate finger movements, and neurons related to different finger movements are intermingled throughout the hand area of the motor cortex, the neuronal population activity does specify particular finger movements.     

Upper-limb surface electro-myography at maximum supination and pronation torques: the effect of elbow and forearm angle.

Forearm pronation and supination, and increased muscular activity in the wrist extensors have been both linked separately to work-related injuries of the upper limb, especially humeral epicondylitis. However, there is a lack of information on forearm torque strength at ranges of elbow and forearm angles typical of industrial tasks. There is a need for strength data on forearm torques at different upper limb angles to be investigated. Such a study should also include the measurement of muscular activity for the prime torque muscles and also other muscles at possible risk of injury due to high exertion levels during tasks requiring forearm torques.Twenty-four male subjects participated in the study that involved maximum forearm torque exertions for the right arm, in the pronation and supination directions, and at four elbow and three forearm rotation angles. Surface EMG (SEMG) was used to evaluate the muscular activity of the pronator teres (PT), pronator quadratus (PQ), biceps brachi (BB), brachioradialis (BR), mid deltoid (DT) and the extensor carpi radialis brevis (ECRB) during maximum torque exertions. Repeated measures ANOVA indicated that both direction and forearm angle had a significant effect on the maximum torques (p<0.05) while elbow angle and the interactions were highly significant (p<0.001). The results revealed that supination torques were stronger overall with a mean maximum value of 16.2 Nm recorded for the forearm 75% prone. Mean maximum pronation torque was recorded as 13.1 Nm for a neutral forearm with the elbow flexed at 45 degrees. The data also indicated that forearm angle had a greater effect on supination torque than pronation torque. Supination torques were stronger for the mid-range of elbow flexion, but pronation torques increased with increasing elbow extension. The strength profiles for the maximum torque exertions were reflected in the EMG changes in the prime supinators and pronators. In addition, the EMG data expressed as the percentage of Maximum Voluntary Electrical activity (MVE), revealed high muscular activity in the ECRB for both supination (26-43% MVE) and pronation torques (17-55% MVE). The results suggest that the ECRB acts as a stabiliser to the forearm flexors for gripping during pronation torques depending on forearm angle, but acts as a prime mover in wrist extension for supination torques with little effect of elbow and forearm angle. This indicates a direct link between forearm rotations against resistance and high muscular activity in the wrist extensors, thereby increasing stress on the forearm musculo-skeletal system, especially the lateral epicondyle.     

Co-contraction of the pronator teres and extensor carpi radialis during wrist extension movements in humans.

In order to elucidate the functional significance of excitatory spinal reflex arcs (facilitation) between musculus (M.) pronator teres (PT) and M. extensor carpi radialis (ECR, longus: ECRL, brevis: ECRB) in humans, activities of the muscles were studied with electromyography (EMG) and electrical neuromuscular stimulation (ENS). In EMG study, activities of PT, ECRL, ECRB, and M. flexor carpi radialis during repetitive static (isometric) wrist extension and a series of a dynamic motion of wrist flexion/extension in the prone, semiprone, and supine positions of the forearm were recorded in 12 healthy human subjects. In the prone, semiprone, and supine positions, PT and ECR showed parallel activities during the static extension in all, eight, and eight subjects, respectively, and at the extension phase during the dynamic motion in all, eight and five subjects, respectively. These findings suggest that co-contraction of PT and ECR occurs during wrist extension movements at least with the prone forearm. The facilitation must be active during the co-contraction. In ENS study, ENS to PT was examined in 11 out of the 12 and that to ECRL was in the 12 subjects. Before ENS, the forearm was in the prone, semiprone, and supine positions. In all the subjects, ENS to PT induced a motion of forearm pronation to the maximum pronation. ENS to ECRL induced motions of wrist extension to the maximum extension and abduction (radial flexion) to 5-20 degrees of abduction regardless of the positions of the forearm. Moreover, it induced 30-80 degrees supination of the forearm from the prone position. Consequently, combined ENS to PT and ECRL resulted in motions of the extension and abduction while keeping the maximum pronation. These findings suggest that the co-contraction of PT and ECR during wrist extension movements occurs to prevent supinating the forearm. Forearm supination from the prone position should be added to one of the actions of ECRL.     

Crosstalk in surface electromyography of the proximal forearm during gripping tasks.

Electromyographic (EMG) crosstalk was systematically analyzed to evaluate the magnitude of common signal present between electrode pairs around the forearm. Surface EMG was recorded and analyzed from seven electrode pairs placed circumferentially around the proximal forearm in six healthy individuals. The cross-correlation function was used to determine the amount of common signal, which was found to decrease as the distance between electrode pairs increased, but was not significantly altered by forearm posture (pronation, neutral, supination). Overall, approximately 40% common signal was detected between adjacent electrode pairs (3 cm apart), dropping to about 10% at 6 cm spacing and 2.5% at 9 cm. The magnitude of common signal approached 50% between adjacent electrode pairs over the extensor muscles, while over 60% was observed between neighbouring sites on the flexor aspect of the forearm. Although flexor and extensor EMG amplitude was similar, less than 2% common signal was present between flexor and extensor electrode pairs during both pinch and grasp tasks. Maximum grip force production was not affected by forearm rotation for pinch, but reduced 18% from neutral (mid-prone) to pronation during grasp (p=0.01). In spite of differences in grip force, mean muscle activity did not vary between the three forearm postures during maximum pinch or grasp trials. While this study improved our knowledge of crosstalk and electrode spacing issues, further examination of forearm EMG is required to improve understanding of muscle loading, EMG properties and motor control during gripping tasks.     

Finger-specific flexor recruitment in humans: depiction by exercise-enhanced MRI.

To evaluate the spatial distribution of human forearm musculature stressed by finger-specific exercise, magnetic resonance imaging was performed in conjunction with exercise protocols designed to separately stress the flexor digitorum superficialis and flexor digitorum profundus. These muscles were shown to consist of subvolumes selectively recruited by flexion of the individual fingers. Knowledge of the finger-specific regions of muscle recruitment during finger flexion could improve sampling accuracy in electromyography, biopsy, magnetic resonance spectroscopy, and invasive vascular sampling studies of hand exercise.     

Evaluating protocols for normalizing forearm electromyograms during power grip

Many studies use a reference task of an isometric maximum voluntary power grip task in a mid-pronated forearm posture to normalize their forearm electromyographic (EMG) signal amplitude. Currently there are no recommended protocols to do this. In order to provide guidance on the topic, we examined the EMG amplitude of six forearm muscles (three flexors and three extensors) during twenty different maximal voluntary efforts that included various gripping postures, force and moment exertions and compared them to a frequently used normalization task of exerting a maximum grip force, termed the reference task. 16 participants (8 male and 8 female, aged 18–26) were recruited for this study. Overall, maximal muscle activity was produced during the resisted moment tasks. When contrasted with the reference task, the resisted moment tasks produced EMG activity that was up to 2.8 times higher (p < 0.05). Although there was no one task that produced greater EMG values than the reference task for all forearm muscles, the resisted flexor and extensor moment tasks produced similar, if not higher EMG activity than the reference task for the three flexors and three extensor muscles, respectively. This suggests that researchers wishing to normalize forearm EMG activity during power gripping prehensile tasks should use resisted flexor and extensor moment tasks to obtain better estimates of the forearm muscles’ maximum electrical activation magnitudes.     

Effects of Modulations of the Visual Conditions on Subjects’ Perception of Their Own and Another Person’s Hand

According to current concepts, the execution of expedient actions well-coordinated in space becomes possible owing to the creation of a system for internal representation, which includes a body coordinate system, in the central nervous system. The goal of this study was to assess the effects induced by the exclusion of vision and a left-right inversion in visual space on the accuracy in the internal representation of hands and on aimed arm movements. The study cohort included 16 participants aged from 18 to 25 years. The experiment consisted of two test series. In the first series, a subject placed his/her left hand under a transparent plexiglass screen. Upon the experimenter’s command, the subject had to indicate the position of his/her left wrist and the terminal phalanges of the thumb, middle and little fingers with his/her right index finger on the plexiglass, which was accompanied by the corresponding marks displayed on the screen. The positional accuracy in the subject’s perception of his/her own hand position was recorded in the conditions of a leftright inversion of visual space, which were created by wearing prismatic spectacles and the exclusion of visual control. In the second case, the subject’s left hand was replaced on the table under the transparent screen by a similarly shaped left hand belonging to another person. It has been shown that the positions of the middle fingertip and the wrist were sufficiently precisely perceived by the subject through prismatic spectacles. However, the position of the tips of the thumb and little finger relative to the axis connecting the wrist and the terminal phalanx of the middle finger (the hand axis) was perceptually inverted. The accuracy of the indication was reduced for all fingers when the eyes were closed. In testing another person’s hand, a left–right inversion in the visual space created an illusory 90° turn of the hand’s axis and an illusory bias relative to the wrist towards elongation in the marker points corresponding to another person’s fingers. We can suggest that when the alien hand replaced the subject’s own hand, in accordance with the modulations in the motor task conditions, the egocentric system of coordinates was replaced by the allocentric system. The role of vision in the execution of spatially oriented and accurate hand movements increased in this case.     

Classification of finger movements by using the ultra-wide band radar

The coding system of finger movements depends on the differences in the characteristics of the muscles that are responsible for these movements. The ability of ultra-wide band (UWB) radar for use as a tool for identifying the movements of each finger is presented. This will facilitate the ability of the UWB radar in designing a coding system for the movement of fingers of each hand.     

Classification of finger movements by using the ultra-wide band radar

The coding system of finger movements depends on the differences in the characteristics of the muscles that are responsible for these movements. The ability of ultra-wide band (UWB) radar for use as a tool for identifying the movements of each finger is presented. This will facilitate the ability of the UWB radar in designing a coding system for the movement of fingers of each hand.