Activity patterns of extrinsic finger flexors and extensors during movements of instructed and non-instructed fingers

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.

Towards Efficient Neural Decoder for Dexterous Finger Force Predictions

OBJECTIVE

Dexterous control of robot hands requires a robust neural-machine interface capable of accurately decoding multiple finger movements. Existing studies primarily focus on single-finger movement or rely heavily on multi-finger data for decoder training, which requires large datasets and high computation demand. In this study, we investigated the feasibility of using limited single-finger surface electromyogram (sEMG) data to train a neural decoder capable of predicting the forces of unseen multi-finger combinations.

METHODS

We developed a deep forest-based neural decoder to concurrently predict the extension and flexion forces of three fingers (index, middle, and ring-pinky). We trained the model using varying amounts of high-density EMG data in a limited condition (i.e., single-finger data).

RESULTS

We showed that the deep forest decoder could achieve consistently commendable performance with 7.0% of force prediction errors and R2 value of 0.874, significantly surpassing the conventional EMG amplitude method and convolutional neural network approach. However, the deep forest decoder accuracy degraded when a smaller amount of data was used for training and when the testing data became noisy.

CONCLUSION

The deep forest decoder shows accurate performance in multi-finger force prediction tasks. The efficiency aspect of the deep forest lies in the short training time and small volume of training data, which are two critical factors in current neural decoding applications.

SIGNIFICANCE

This study offers insights into efficient and accurate neural decoder training for advanced robotic hand control, which has the potential for real-life applications during human-machine interactions.

Myoelectric manifestations of fatigue of the finger flexor muscles and endurance capacity in experienced versus intermediate climbers during suspension tasks

Climbing is a physically demanding discipline, placing significant loads on the finger flexors. Notwithstanding the documented greater endurance capacity of experienced climbers, the mechanisms explaining these training-induced adaptations remain unknown. We therefore investigate whether two non-competing strategies - muscle adaptation and alternate muscle recruitment - may explain the disparity in endurance capacity in participants with different climbing experience. We analysed high-density surface electromyograms (EMGs) from 38 Advanced and Intermediate climbers, during suspension exercises over three different depths (15, 20, 30 mm) using a half-crimp grip position. From the spatial distribution of changes in MeDian Frequency and Root Mean Square values until failure, we assessed how much and how diffusely the myoelectric manifestations of fatigue took place. Advanced climbers exhibited greater endurance, as evidenced by significantly longer failure time (p < 0.009) and lower changes in MDF values (p < 0.013) for the three grip depths. These changes were confined to a small skin region (nearly 25% of the grid size), centred at variable locations across participants. Moreover, lower MDF changes were significantly associated with longer suspension times. Collectively, our results suggest that muscle adaptation rather than load sharing between and within muscles is more likely to explain the improved endurance in experienced climbers.

Effects of wrist posture and stabilization on precision grip force production and muscle activation patterns

Most of the power for generating forces in the fingers arises from muscles located in the forearm. This configuration maximizes finger joint range of motion while minimizing finger mass and inertia. The resulting multiarticular arrangement of the tendons, however, complicates independent control of the wrist and the digits. Actuating the wrist impacts sensorimotor control of the fingers and vice versa. The goal of this study was to systematically investigate interactions between isometric wrist and digit control. Specifically, we examined how the need to maintain a specified wrist posture influences precision grip. Fifteen healthy adults produced maximum precision grip force at 11 different wrist flexion/extension angles, with the arm supported, under two conditions: 1) the participant maintained the desired wrist angle while performing the precision grip and 2) a robot maintained the specified wrist angle. Wrist flexion/extension posture significantly impacted maximum precision grip force (P < 0.001), with the greatest grip force achieved when the wrist was extended 30° from neutral. External wrist stabilization by the robot led to a 20% increase in precision grip force across wrist postures. Increased force was accompanied by increased muscle activation but with an activation pattern similar to the one used when the participant had to stabilize their wrist. Thus, simultaneous wrist and finger requirements impacted performance of an isometric finger task. External wrist stabilization can promote increased precision grip force resulting from increased muscle activation. These findings have potential clinical significance for individuals with neurologically driven finger weakness, such as stroke survivors.NEW & NOTEWORTHY We explored the interdependence between wrist and fingers by assessing the influence of wrist posture and external stabilization on precision grip force generation. We found that maximum precision grip force occurred at an extended wrist posture and was 20% greater when the wrist was Externally Stabilized. The latter resulted from amplification of muscle activation patterns from the Self-Stabilized condition rather than adoption of new patterns exploiting external wrist stabilization.

Exploring Kinetic and Kinematic Finger Individuation Capability in Children With Hemiplegic Cerebral Palsy

While fine manual dexterity develops over time, the extent to which children show independent control of their digits in each hand and the impact of perinatal brain injury on this individuation have not been well quantified. Our goal in this study was to assess and compare finger force and movement individuation in 8-14 year old children with hemiplegic cerebral palsy (hCP; n = 4) and their typically developing peers (TD; n = 10). We evaluated finger force individuation with five independent load cells and captured joint movement individuation with video tracking. We observed no significant differences in individuation indices between the dominant and non-dominant hands of TD children, but individuated force and movement were substantially reduced in the paretic versus non paretic hands of children with hCP (p < 0.001). In TD participants, the thumb tended to have the greatest level of independent control. This small sample of children with hCP showed substantial loss of individuation in the paretic hand and some deficits in the non-paretic hand, suggesting possible benefit from targeted training of digit independence in both hands for children with CP.

Finger Force Estimation using Motor Unit Discharges Across Forearm Postures

BACKGROUND

Myoelectric-based decoding has gained popularity in upper-limb neural-machine interfaces. Motor unit (MU) firings decomposed from surface electromyographic (EMG) signals can represent motor intent, but EMG properties at different arm configurations can change due to electrode shift and differing neuromuscular states. This study investigated whether isometric fingertip force estimation using MU firings is robust to forearm rotations from a neutral to either a fully pronated or supinated posture.

METHODS

We extracted MU information from high-density EMG of the extensor digitorum communis in two ways: (1) Decomposed EMG in all three postures (MU-AllPost); and (2) Decomposed EMG in neutral posture (MU-Neu), and extracted MUs (separation matrix) were applied to other postures. Populational MU firing frequency estimated forces scaled to subjects' maximum voluntary contraction (MVC) using a regression analysis. The results were compared with the conventional EMG-amplitude method.

RESULTS

We found largely similar root-mean-square errors (RMSE) for the two MU-methods, indicating that MU decomposition was robust to postural differences. MU-methods demonstrated lower RMSE in the ring (EMG = 6.23, MU-AllPost = 5.72, MU-Neu = 5.64 %MVC) and pinky (EMG = 6.12, MU-AllPost = 4.95, MU-Neu = 5.36 %MVC) fingers, with mixed results in the middle finger (EMG = 5.47, MU-AllPost = 5.52, MU-Neu = 6.19% MVC).

CONCLUSION

Our results suggest that MU firings can be extracted reliably with little influence from forearm posture, highlighting its potential as an alternative decoding scheme for robust and continuous control of assistive devices.

Intention to be force efficient improves high-level anticipatory coordination of finger positions and forces in young and elderly adults

Successful object manipulation requires anticipatory high-level control of finger positions and forces to prevent object slip and tilt. Unlike young adults, who efficiently scale grip forces (GFs) according to surface conditions, old adults were reported to exert excessive grip forces. In this study, we theoretically show how grip force economy depends on the modulation of the centers of pressure on opposing grip surfaces (ΔCoP) according to object properties. In a grasp-to-lift study with young and elderly participants, we investigated how the instruction to lift the object with efficient GF influences the anticipation of torques, ΔCoP and GF control during complex variations of mass distributions and surface properties. Provision of the explicit instruction to strive for force efficiency prompted both age groups to optimize their ΔCoP modulation, although to a lesser degree in the elderly, and also led to a refinement of torque anticipation for a right-sided weight distribution in the young, but not the elderly participants. Consequently, marked drops in GF levels resulted. Furthermore, participants enhanced ΔCoP modulation and lowered GF safety ratios in challenging surface conditions. Higher GF in the elderly was due to decreased skin-surface friction but also worse ΔCoP modulation for lateralized mass distributions when trying to be force efficient. In contrast, safety margins were not elevated in the elderly, suggesting preserved GF control. Our findings demonstrate how task goals influence high-level motor control of object manipulation differentially in young and elderly participants and highlight the necessity to control for both instructions and friction when investigating GF control.NEW & NOTEWORTHY Previous studies have shown that forces are covaried as a function of centers of pressure (CoPs) to exert adequate torques. Here, we demonstrate that force-efficient object manipulation requires the modulation of CoPs and show that providing the instruction to be force efficient and challenging surface conditions elicits a GF safety ratio reduction as well as an optimization of anticipatory CoP modulation and torques in the young and, to a lesser degree, in the elderly.

Performance evaluation of pattern recognition networks using electromyography signal and time-domain features for the classification of hand gestures

The problem of classifying individual finger movements of one hand is focused in this article. The input electromyography signal is processed and eight time-domain features are extracted for classifying hand gestures. The classified finger movements are thumb, middle, index, little, ring, hand close, thumb index, thumb ring, thumb little and thumb middle and the hand grasps are palmar class, spherical class, hook class, cylindrical class, tip class and lateral class. Four state-of-the-art classifiers namely feed forward artificial neural network, cascaded feed forward artificial neural network, deep learning neural network and support vector machine are selected for this work to classify the finger movements and hand grasps using the extracted time-domain features. The experimental results show that the artificial neural network classifier is stabilized at 6 epochs for finger movement dataset and at 4 epochs for hand grasps dataset with low mean square error. However, the support vector machine classifier attains the maximum accuracy of 97.3077% for finger movement dataset and 98.875% for hand grasp dataset which is significantly greater than feed forward artificial neural network, cascaded feed forward artificial neural network and deep learning neural network classifiers.

putEMG-A Surface Electromyography Hand Gesture Recognition Dataset

In this paper, we present a putEMG dataset intended for the evaluation of hand gesture recognition methods based on sEMG signal. The dataset was acquired for 44 able-bodied subjects and include 8 gestures (3 full hand gestures, 4 pinches and idle). It consists of uninterrupted recordings of 24 sEMG channels from the subject's forearm, RGB video stream and depth camera images used for hand motion tracking. Moreover, exemplary processing scripts are also published. The putEMG dataset is available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. An accuracy of 90% was achieved for SVM classifier utilising RMS feature and for LDA classifier using Hudgin's and Du's feature sets. Analysis of performance for particular gestures showed that LDA/Du combination has significantly higher accuracy for full hand gestures, while SVM/RMS performs better for pinch gestures. The presented dataset can be used as a benchmark for various classification methods, the evaluation of electrode localisation concepts, or the development of classification methods invariant to user-specific features or electrode displacement.

Preliminary Study of Virtual sEMG Signal-Assisted Classification

Surface electromyography (sEMG) is widely used in various fields to analyze user intentions. Conventional sEMG-based classifications are electrode-dependent; thus, trained classifiers cannot be applied to other electrodes that have different parameters. This defect degrades the practicability of sEMG-based applications. In this study, we propose a virtual sEMG signal-assisted classification to achieve electrode-independent classification. The virtual signal for any electrode configuration can be generated using muscle activation signals obtained from the proposed model. The feasibility of the virtual signal is demonstrated with regard to i) classifications using fewer sEMG channels by a pre-trained classifier without re-training and ii) electrode-independent classifications. This study focuses on preliminary tests of virtual sEMG signal-assisted classification. Future studies should consider model improvement and experiments involving more subjects to achieve plug-and-play classification.

Single finger movements in the aging hand: changes in finger independence, muscle activation patterns and tendon displacement in older adults

With aging, hand mobility and manual dexterity decline, even under healthy circumstances. To assess how aging affects finger movement control, we compared elderly and young subjects with respect to (1) finger movement independence, (2) neural control of extrinsic finger muscles and (3) finger tendon displacements during single finger flexion. In twelve healthy older (age 68-84) and nine young (age 22-29) subjects, finger kinematics were measured to assess finger movement enslaving and the range of independent finger movement. Muscle activation was assessed using a multi-channel electrode grid placed over the flexor digitorum superficialis (FDS) and the extensor digitorum (ED). FDS tendon displacements of the index, middle and ring fingers were measured using ultrasound. In older subjects compared to the younger subjects, we found: (1) increased enslaving of the middle finger during index finger flexion (young: 25.6 ± 12.4%, elderly: 47.0 ± 25.1%; p = 0.018), (2) a lower range of independent movement of the index finger (young_middle = 74.0%, elderly_middle: 45.9%; p < 0.001), (3) a more evenly distributed muscle activation pattern over the finger-specific FDS and ED muscle regions and (4) a lower slope at the beginning of the finger movement to tendon displacement relationship, presenting a distinct period with little to no tendon displacement. Our study indicates that primarily the movement independence of the index finger is affected by aging. This can partly be attributed to a muscle activation pattern that is more evenly distributed over the finger-specific FDS and ED muscle regions in the elderly.

Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings

The human hand is capable of producing versatile yet precise movements largely owing to the complex neuromuscular systems that control our finger movement. This study seeks to quantify the spatial activation patterns of the forearm flexor muscles during individualized finger flexions. High-density (HD) surface electromyogram (sEMG) signals of forearm flexor muscles were obtained, and individual motor units were decomposed from the sEMG. Both macro-level spatial patterns of EMG activity and micro-level motor unit distributions were used to systematically characterize the forearm flexor activation patterns. Different features capturing the spatial patterns were extracted, and the unique patterns of forearm flexor activation were then quantified using pattern recognition approaches. We found that the forearm flexor spatial activation during the ring finger flexion was mostly distinct from other fingers, whereas the activation patterns of the middle finger were least distinguishable. However, all the different activation patterns can still be classified in high accuracy (94–100%) using pattern recognition. Our findings indicate that the partial overlapping of neural activation can limit accurate identification of specific finger movement based on limited recordings and sEMG features, and that HD sEMG recordings capturing detailed spatial activation patterns at both macro- and micro-levels are needed.

The digital human forearm and hand

How changes in anatomy affect joint biomechanics can be studied using musculoskeletal modelling, making it a valuable tool to explore joint function in healthy and pathological joints. However, gathering the anatomical, geometrical and physiological data necessary to create a model can be challenging. Very few integrated datasets exist and even less raw data is openly available to create new models. Therefore, the goal of the present study is to create an integrated digital forearm and make the raw data available via an open-access database. An un-embalmed cadaveric arm was digitized using 7T MRI and CT scans. 3D geometrical models of bones, cartilage, muscle and muscle pathways were created. After MRI and CT scanning, physiological muscle parameters (e.g. muscle volume, mass, length, pennation angle, physiological cross-sectional area, tendon length) were obtained via detailed dissection. After dissection, muscle biopsies were fixated and confocal microscopy was used to visualize and measure sarcomere lengths. This study provides an integrated anatomical dataset on which complete and accurate musculoskeletal models of the hand can be based. By creating a 3D digital human forearm, including all relevant anatomical parameters, a more realistic musculoskeletal model can be created. Furthermore, open access to the anatomical dataset makes it possible for other researchers to use these data in the development of a musculoskeletal model of the hand.

Increased enslaving in elderly is associated with changes in neural control of the extrinsic finger muscles

Aging has consequences for hand motor control, among others affecting finger force enslaving during static pressing tasks. The aim of this study was to assess whether the extent of finger force enslaving changes with aging during a task that involves both static and dynamic phases. Ten right-handed young (22-30 years) and ten elderly subjects (67-79 years) were instructed to first exert a constant force (static phase) and then flex their index finger while counteracting constant resistance forces orthogonal to their fingertips (dynamic phase). The other fingers (non-instructed) were held in extension. EMG activities of the flexor digitorum superficialis (FDS) and extensor digitorum (ED) muscles in the regions corresponding to the index, middle and ring fingers together with their forces and position of index finger were measured. In both elderly and young, forces exerted by the non-instructed fingers increased (around 0.6 N for both young and elderly) during isotonic flexion of the index finger, but with a different delay of on average 100 ± 72 ms in elderly and 334 ± 101 ms in young subjects. Results also suggest different responses in activity of FDS and ED muscle regions of the non-instructed fingers to index finger flexion between elderly and young subjects. The enslaving effect was significantly higher in elderly than in young subjects both in the static (12% more) and dynamic (14% more) phases. These differences in enslaving can at least partly be explained by changes in neuromuscular control.

Wireless sEMG System with a Microneedle-Based High-Density Electrode Array on a Flexible Substrate

Surface electromyography (sEMG) signals reflect muscle contraction and hence, can provide information regarding a user's movement intention. High-density sEMG systems have been proposed to measure muscle activity in small areas and to estimate complex motion using spatial patterns. However, conventional systems based on wet electrodes have several limitations. For example, the electrolyte enclosed in wet electrodes restricts spatial resolution, and these conventional bulky systems limit natural movements. In this paper, a microneedle-based high-density electrode array on a circuit integrated flexible substrate for sEMG is proposed. Microneedles allow for high spatial resolution without requiring conductive substances, and flexible substrates guarantee stable skin-electrode contact. Moreover, a compact signal processing system is integrated with the electrode array. Therefore, sEMG measurements are comfortable to the user and do not interfere with the movement. The system performance was demonstrated by testing its operation and estimating motion using a Gaussian mixture model-based, simplified 2D spatial pattern.

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).