Virtual Training of the Myosignal

Objective

To investigate which of three virtual training methods produces the largest learning effects on discrete and continuous myocontrol. The secondary objective was to examine the relation between myocontrol and manual motor control tests.

Design

A cohort analytic study.

Setting

University laboratory.

Participants

3 groups of 12 able-bodied participants (N = 36).

Interventions

Participants trained the control over their myosignals on 3 consecutive days. Training was done with either myosignal feedback on a computer screen, a virtual myoelectric prosthetic hand or a computer game. Participants performed 2 myocontrol tests and 2 manual motor control tests before the first and after the last training session. They were asked to open and close a virtual prosthetic hand on 3 different velocities as a discrete myocontrol test and followed a line with their myosignals for 30 seconds as a continuous myocontrol test. The motor control tests were a pegboard and grip-force test.

Main Outcome Measures

Discrete myocontrol test: mean velocities. Continuous myocontrol test: error and error SD. Pegboard test: time to complete. Grip-force test: produced forces.

Results

No differences in learning effects on myocontrol were found for the different virtual training methods. Discrete myocontrol ability did not significantly improve as a result of training. Continuous myocontrol ability improved significantly as a result of training, both on average control and variability. All correlations between the motor control and myocontrol test outcome measures were below .50.

Conclusions

Three different virtual training methods showed comparable results when learning myocontrol. Continuous myocontrol was improved by training while discrete myocontrol was not. Myocontrol ability could not be predicted by the manual motor control tests.     

Kinematic evaluation of the finger’s interphalangeal joints coupling mechanism—variability,flexion–extension differences,triggers, locking swanneck deformities,anthropometric correlations

The human finger contains tendon/ligament mechanisms essential for proper control. One mechanism couples the movements of the interphalangeal joints when the (unloaded) finger is flexed with active deep flexor. This study’s aim was to accurately determine in a large finger sample the kinematics and variability of the coupled interphalangeal joint motions, for potential clinical and finger model validation applications. The data could also be applied to humanoid robotic hands. Sixty-eight fingers were measured in seventeen hands in nine subjects. Fingers exhibited great joint mobility variability, with passive proximal interphalangeal hyperextension ranging from zero to almost fifty degrees. Increased measurement accuracy was obtained by using marker frames to amplify finger segment motions. Gravitational forces on the marker frames were not found to invalidate measurements. The recorded interphalangeal joint trajectories were highly consistent, demonstrating the underlying coupling mechanism. The increased accuracy and large sample size allowed for evaluation of detailed trajectory variability, systematic differences between flexion and extension trajectories, and three trigger types, distinct from flexor tendon triggers, involving initial flexion deficits in either proximal or distal interphalangeal joint. The experimental methods, data and analysis should advance insight into normal and pathological finger biomechanics (e.g., swanneck deformities), and could help improve clinical differential diagnostics of trigger finger causes. The marker frame measuring method may be useful to quantify interphalangeal joints trajectories in surgical/rehabilitative outcome studies. The data as a whole provide the most comprehensive collection of interphalangeal joint trajectories for clinical reference and model validation known to us to date.     

含被动关节的仿人型塑料电子假手研究

为了突破传统假手的不足,使假手在形状和功能上更像人手、重量进一步减轻,设计了包含被动关节的仿人型塑料电子假手。在健全手的帮助下,假手手指关节可被动弯曲和旋转;与传统单自由度假手相比,可以完成更加复杂的动作。仿真塑料假手样机重量较轻、外形美观,通过配置肌电控制系统实验,实现了对假手实现准确可靠的控制。     

Artificial Sweat for Humanoid Finger

To achieve favorable Frictional Tactile Sensation （FTS） for robot and humanoid fingers, this report investigated the effects of human finger sweat on Friction Coefficient （FC） and verified the effectiveness of artificial sweat on FTS tbr a humanoid finger. The results show that the model sweat （salt and urea water faked real sweat） increases the FC of the real finger sliding on the high hygroscopic and rough surface （paper）, whereas on the low hygroscopic and smooth surface （PMMA）, the sweat forms a fluid film and decreases FC, restricting severe finger adhesion. Further, the film formation and capillary adhesion force of sweat were discussed. The experimental results with the artificial sweats （ethanol and water） and humanoid finger （silicone rubber skin with tactile sensors） verifies the effectiveness. The artificial sweat restricts severe adhesion （stick-slip vibration）, and enhances cognitive capability of FTS.     

Coordinated force production in multi-finger tasks: finger interaction and neural network modeling

During maximal voluntary contraction (MVC) with several fingers, the following three phenomena are observed: (1) the total force produced by all the involved fingers is shared among the fingers in a specific manner (sharing); (2) the force produced by a given finger in a multi-finger task is smaller than the force generated by this finger in a single-finger task (force deficit); (3) the fingers that are not required to produce any force by instruction are involuntary activated (enslaving). We studied involuntary force production by individual fingers (enslaving effects, EE) during tasks when (an)other finger(s) of the hand generated maximal voluntary pressing force in isometric conditions. The subjects (n = 10) were instructed to press as hard as possible on the force sensors with one, two, three and four fingers acting in parallel in all possible combinations. The EE were (A) large, the slave fingers always producing a force ranging from 10.9% to 54.7% of the maximal force produced by the finger in the single-finger task; (B) nearly symmetrical; (C) larger for the neighboring fingers; and (D) non-additive. In most cases, the EE from two or three fingers were smaller than the EE from at least one finger (this phenomenon was coined occlusion). The occlusion cannot be explained only by anatomical musculo-tendinous connections. Therefore, neural factors contribute substantially to the EE. A neural network model that accounts for all the three effects has been developed. The model consists of three layers: the input layer that models a central neural drive; the hidden layer modeling transformation of the central drive into an input signal to the muscles serving several fingers simultaneously (multi-digit muscles); and the output layer representing finger force output. The output of the hidden layer is set inversely proportional to the number of fingers involved. In addition, direct connections between the input and output layers represent signals to the hand muscles serving individual fingers (uni-digit muscles). The network was validated using three different training sets. Single digit muscles contributed from 25% to 50% of the total finger force. The master matrix and the enslaving matrix were computed; they characterize the ability of a given finger to enslave other fingers and its ability to be enslaved. Overall, the neural network modeling suggests that no direct correspondence exists between neural command to an individual finger and finger force. To produce a desired finger force, a command sent to an intended finger should be scaled in accordance with the commands sent to the other fingers. Received: 17 October 1997 / Accepted in revised form: 12 May 1998     

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.     

Tendon displacements during voluntary and involuntary finger movements

In the human hand, independent movement control of individual fingers is limited. One potential cause for this is mechanical connections between the tendons and muscle bellies corresponding to the different fingers. The aim of this study was to determine the tendon displacement of the flexor digitorum superficialis (FDS) of both the instructed and the neighboring, non-instructed fingers during single finger flexion movements. In nine healthy subjects (age 22–29 years), instructed and non-instructed FDS finger tendon displacement of the index, middle and ring finger was measured using 2D ultrasound analyzed with speckle tracking software in two conditions: active flexion of all finger joints with all fingers free to move and active flexion while the non-instructed fingers were restricted. Our results of the free movement protocol showed an average tendon displacement of 27 mm for index finger flexion, 21 mm for middle finger flexion and 17 mm for ring finger flexion. Displacements of the non-instructed finger tendons (≈12 mm) were higher than expected based of the amount of non-instructed finger movement. In the restricted protocol, we found that, despite minimal joint movements, substantial non-instructed finger tendon displacement (≈9 mm) was still observed, which was interpreted as a result of tendon strain. When this strain component was subtracted from the tendon displacement of the non-instructed fingers during the free movement condition, the relationship between finger movement and tendon displacement of the instructed and non-instructed finger became comparable. Thus, when studying non-instructed finger tendon displacement it is important to take tendon strain into consideration.     

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.     

A light weight compliant hand mechanism with high degrees of freedom

This paper presents the design and prototyping of an inherently compliant lightweight hand mechanism. The hand mechanism itself has 15 degrees of freedom and five fingers. Although the degrees of freedom in each finger are coupled, reducing the number of independent degrees of freedom to 5, the 15 degrees of freedom of the hand could potentially be individually actuated. Each joint consists of a novel flexing mechanism that is based on the loading of a compression spring in the axial and transverse direction via a cable and conduit system. Currently, a bench top version of the prototype is being developed; the three joints of each finger are coupled together to simplify the control system. The current control scheme under investigation simulates a control scheme where myoelectric signals in the wrist flexor and extensor muscles are converted in to x and y coordinates on a control scheme chart. Static load-deformation analysis of finger segments is studied based on a 3-dimensional model without taking the stiffener into account, and the experiment validates the simulation.     

X-ray anatomic features of the skeletons of twin children

The size of right and left middle phalanges in the II-V fingers and the III finger have been studied in 108 pairs of monozygotic and dizygotic twins at 8--19 years of age and in 60 paris (pedagogical experiment) of separated twins (from the same pair), schoolchildren of the 2d--5th forms trained according different programs of physical culture. A great similarity of osteometric signs has been stated in monozygotic twins. As to local (intradigital) differences, a prevalence of the IIId finger should be mentioned. Maximum of genetic determination is evident for transversal growth--under high mechanical loadings on the bones, for longitudinal--under low mechanical loadings. The data of the pedagogical experiment demonstrate that increased mechanical loading on the hand bones activates osteogenesis. According to the majority of indices the main is the "radial" (II--III fingers) type.     

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.     

Dermatoglyphic analysis in borderline personality disorder and schizophrenia--results of a Croatian study

Dermatoglyphic features of 52 male patients with borderline personality disorder (BPD) were compared with those of 200 male controls (control group-CG) and 195 males with schizophrenia (SCH). Quantitative analysis showed statistically significant differences between BPD-CG and between BPD-SCH, mainly regarding the palmar traits, but also the 5th, the 4th and the 1st finger of the right hand as well as the 5th and the 4th finger of the left hand between BPD and SCH patients. The canonical discriminant analysis permitted correct classification with 69.84% probability between the BPD and CG and with 76.11% probability between the BPD and the SCH group. Qualitative finger and palmar traits analysis showed differences between the BPD and SCH groups on the 3rd finger of the left hand, total frequency for all fingers and in the III interdigital space. Significant differences between the BPD and CG were found on the 3rd finger of the left hand. Our results show that the dermatoglyphic features of BPD differ from those of schizophrenia and from those of control subjects. The possible significance of these findings is discussed.     

Biodynamic response of human fingers in a power grip subjected to a random vibration

BACKGROUND: Knowledge of the biodynamic response (BR) of the human hand-arm system is an important part of the foundation for the measurement and assessment of hand-transmitted vibration exposure. This study investigated the BR of human fingers in a power grip subjected to a random vibration. METHOD: Ten male subjects were used in the experiment. Each subject applied three coupling actions to a simulated tool handle at three different finger grip force levels. RESULTS AND CONCLUSIONS: The BR is practically independent of the hand coupling actions for frequencies at or above 100 Hz. Above 50 Hz, the BR is correlated to finger and hand sizes. Increasing the finger coupling force significantly increases the BR. Therefore, hand forces should be measured and used when assessing hand-transmitted vibration exposure. The results also show that under a constant-velocity vibration, the finger vibration power absorption at frequencies above 200 Hz is approximately twice that at frequencies below 100 Hz. This suggests that the frequency weighting specified in the current ISO 5349-1 (2001) may underestimate the high frequency effect on vibration-induced finger disorders.     

Using EMG data to constrain optimization procedure improves finger tendon tension estimations during static fingertip force production

Determining tendon tensions of the finger muscles is crucial for the understanding and the rehabilitation of hand pathologies. Since no direct measurement is possible for a large number of finger muscle tendons, biomechanical modelling presents an alternative solution to indirectly evaluate these forces. However, the main problem is that the number of muscles spanning a joint exceeds the number of degrees of freedom of the joint resulting in mathematical under-determinate problems. In the current study, a method using both numerical optimization and the intra-muscular electromyography (EMG) data was developed to estimate the middle finger tendon tensions during static fingertip force production. The method used a numerical optimization procedure with the muscle stress squared criterion to determine a solution while the EMG data of three extrinsic hand muscles serve to enforce additional inequality constraints. The results were compared with those obtained with a classical numerical optimization and a method based on EMG only. The proposed method provides satisfactory results since the tendon tension estimations respected the mechanical equilibrium of the musculoskeletal system and were concordant with the EMG distribution pattern of the subjects. These results were not observed neither with the classical numerical optimization nor with the EMG-based method. This study demonstrates that including the EMG data of the three extrinsic muscles of the middle finger as inequality constraints in an optimization process can yield relevant tendon tensions with regard to individual muscle activation patterns, particularly concerning the antagonist muscles.     

Handgrip strength and hand dimensions in young handball and basketball players

In handball and basketball the longer the finger length the better the accuracy of the shot or throw. All shots and throws are finished with the wrist and fingers. It can be proposed that athletes with longer fingers and greater hand surface parameters also probably have greater grip strength. The aim of this study was to investigate the influence of general body and hand-specific anthropometric dimensions on handgrip strength in boys participating in handball and basketball training. In total, 193 boys aged 10-17 years participated in this study. They were divided into 6 groups: 10-, 11-, 12-, 13-, 14-15-, and 16-17-year-olds. The body height and body mass were measured and body mass index was calculated as general anthropometric parameters. The outlines of the hands of the boys were drawn on paper with a thin marker. Three groups of hand anthropometric parameters were measured: 5 finger spans, 5 finger lengths, and 5 perimeters of the hand. Handgrip strength was measured on the dominant hand with a Lafayette dynamometer. As a rule, general anthropometric parameters determined the maximal handgrip strength more accurately than did specific hand anthropometric parameters. From the specific hand anthropometric parameters, finger lengths and perimeters of the hand significantly correlated with the maximal handgrip strength. In summary, fingers are the smallest, lightest parts of the motor apparatus, and, therefore, they represent the parts most easily deflected by force from the ball, but at the same time, finger control is especially important for the accuracy of different shots, both in handball and basketball. Thus, it is especially necessary to measure finger length and perimeters of the hand for practical reasons.     

Comparative and functional morphology of hominoid fingers.

Comparisons of hominoid metacarpals and phalanges reveal differences, many of which are closely linked to locomotor hand postures. The African apes display features of the metacarpals and phalanges which distinguish them from the other Hominoidea. These features are most evident in digits III and IV. The orangutan hand is demonstrably less well adapted to knuckle-walking and is distinctive in its adaptation to power and hook grasping of vertical and horizontal supports, respectively. Orangutan fingers possess a "double-locking" mechanism (Napier, '60), and a slight ulnad shift in the axis of the hand which results in lengthened phalanges of ray IV. Hylobatid apes are more like orangutans in their finger morphology than any of the other Hominoidea, but exhibit unique features of their own. These include elongate phalanges of fingers II-V. Human metacarpals II-V form two sets composed of II-III, and IV-V. The heads of both metacarpals II and III are characterized by axial torsion. This reflects the enhanced manipulatory role of the third finger in humans. Human distal phalanges are unique in the development of pronounced apical tufts. Multivariate analysis of metacarpal III and proximal III yields variables that array the extant apes along an arboreal-terrestrial axis, from hylobatid apes to male gorillas. The positions of taxa on this discriminant concur with observations on the locomotion of free-ranging apes.     

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.