Anatomic basis for individuated surface EMG and homogeneous electrostimulation with neuroprostheses of the extensor digitorum communis

Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study

Objective.The absence of intuitive control in present myoelectric interfaces makes it a challenge for users to communicate with assistive devices efficiently in real-world conditions. This study aims to tackle this difficulty by incorporating neurophysiological entities, namely muscle and force synergies, onto multi-finger force estimation to allow intuitive myoelectric control.Approach. Eleven healthy subjects performed six isometric grasping tasks at three muscle contraction levels. The exerted fingertip forces were collected concurrently with the surface electromyographic (sEMG) signals from six extrinsic and intrinsic muscles of hand. Muscle synergies were then extracted from recorded sEMG signals, while force synergies were identified from measured force data. Afterwards, a linear regressor was trained to associate the two types of synergies. This would allow us to predict multi-finger forces simply by multiplying the activation signals derived from muscle synergies with the weighting matrix of initially identified force synergies. To mitigate the false activation of unintended fingers, the force predictions were finally corrected by a finger state recognition procedure.Main results. We found that five muscle synergies and four force synergies are able to make a tradeoff between the computation load and the prediction accuracy for the proposed model; When trained and tested on all six grasping tasks, our method (SYN-II) achieved better performance (R2= 0.80 ± 0.04, NRMSE = 0.19 ± 0.01) than conventional sEMG amplitude-based method; Interestingly, SYN-II performed better than all other methods when tested on two unknown tasks outside the four training tasks (R2= 0.74 ± 0.03, NRMSE = 0.22 ± 0.02), which indicated better generalization ability.Significance. This study shows the first attempt to link between muscle and force synergies to allow concurrent and continuous estimation of multi-finger forces from sEMG. The proposed approach may lay the foundation for high-performance myoelectric interfaces that allow users to control robotic hands in a more natural and intuitive manner.

Robust neural decoding for dexterous control of robotic hand kinematics

BACKGROUND

Manual dexterity is a fundamental motor skill that allows us to perform complex daily tasks. Neuromuscular injuries, however, can lead to the loss of hand dexterity. Although numerous advanced assistive robotic hands have been developed, we still lack dexterous and continuous control of multiple degrees of freedom in real-time. In this study, we developed an efficient and robust neural decoding approach that can continuously decode intended finger dynamic movements for real-time control of a prosthetic hand.

METHODS

High-density electromyogram (HD-EMG) signals were obtained from the extrinsic finger flexor and extensor muscles, while participants performed either single-finger or multi-finger flexion-extension movements. We implemented a deep learning-based neural network approach to learn the mapping from HD-EMG features to finger-specific population motoneuron firing frequency (i.e., neural-drive signals). The neural-drive signals reflected motor commands specific to individual fingers. The predicted neural-drive signals were then used to continuously control the fingers (index, middle, and ring) of a prosthetic hand in real-time.

RESULTS

Our developed neural-drive decoder could consistently and accurately predict joint angles with significantly lower prediction errors across single-finger and multi-finger tasks, compared with a deep learning model directly trained on finger force signals and the conventional EMG-amplitude estimate. The decoder performance was stable over time and was robust to variations of the EMG signals. The decoder also demonstrated a substantially better finger separation with minimal predicted error of joint angle in the unintended fingers.

CONCLUSIONS

This neural decoding technique offers a novel and efficient neural-machine interface that can consistently predict robotic finger kinematics with high accuracy, which can enable dexterous control of assistive robotic hands.

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.

Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification

With the soaring development of body sensor network (BSN)-based health informatics, information security in such medical devices has attracted increasing attention in recent years. Employing the biosignals acquired directly by the BSN as biometrics for personal identification is an effective approach. Noncancelability and cross-application invariance are two natural flaws of most traditional biometric modalities. Once the biometric template is exposed, it is compromised forever. Even worse, because the same biometrics may be employed as tokens for different accounts in multiple applications, the exposed template can be used to compromise other accounts. In this work, we propose a cancelable and cross-application discrepant biometric approach based on high-density surface electromyogram (HD-sEMG) for personal identification. We enrolled two accounts for each user. HD-sEMG signals from the right dorsal hand under isometric contractions of different finger muscles were employed as biometric tokens. Since isometric contraction, in contrast to dynamic contraction, requires no actual movement, the users' choice to login to different accounts is greatly protected against impostors. We realized a promising identification accuracy of 85.8% for 44 identities (22 subjects × 2 accounts) with training and testing data acquired 9 days apart. The high identification accuracy of different accounts for the same user demonstrates the promising cancelability and cross-application discrepancy of the proposed HD-sEMG-based biometrics. To the best of our knowledge, this is the first study to employ HD-sEMG in personal identification applications, with signal variation across days considered.

Quantifying Spatial Activation Patterns of Motor Units in Finger Extensor Muscles

The ability to expertly control different fingers contributes to hand dexterity during object manipulation in daily life activities. The macroscopic spatial patterns of muscle activations during finger movements using global surface electromyography (sEMG) have been widely researched. However, the spatial activation patterns of microscopic motor units (MUs) under different finger movements have not been well investigated. The present work aims to quantify MU spatial activation patterns during movement of distinct fingers (index, middle, ring and little finger). Specifically, we focused on extensor muscles during extension contractions. Motor unit action potentials (MUAPs) during movement of each finger were obtained through decomposition of high-density sEMG (HD-sEMG). First, we quantified the spatial activation patterns of MUs for each finger based on 2-dimension (2-D) root-mean-square (RMS) maps of MUAP grids after spike-triggered averaging. We found that these activation patterns under different finger movements are distinct along the distal-proximal direction, but with partial overlap. Second, to further evaluate MU separability, we classified the spatial activation pattern of each individual MU under distinct finger movement and associated each MU with its corresponding finger with Regularized Uncorrelated Multilinear Discriminant Analysis (RUMLDA). A high accuracy of MU-finger classification tested on 12 subjects with a mean of 88.98% was achieved. The quantification of MU spatial activation patterns could be beneficial to studies of neural mechanisms of the hand. To the best of our knowledge, this is the first work which manages to quantify MU behaviors under different finger movements.

Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information

A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.

Muscle Synergies Reliability in the Power Clean Exercise

Muscle synergy extraction has been utilized to investigate muscle coordination in human movement, namely in sports. The reliability of the method has been proposed, although it has not been assessed previously during a complex sportive task. Therefore, the aim of the study was to evaluate intra- and inter-day reliability of a strength training complex task, the power clean, assessing participants' variability in the task across sets and days. Twelve unexperienced participants performed four sets of power cleans in two test days after strength tests, and muscle synergies were extracted from electromyography (EMG) data of 16 muscles. Three muscle synergies accounted for almost 90% of variance accounted for (VAF) across sets and days. Intra-day VAF, muscle synergy vectors, synergy activation coefficients and individual EMG profiles showed high similarity values. Inter-day muscle synergy vectors had moderate similarity, while the variables regarding temporal activation were still strongly related. The present findings revealed that the muscle synergies extracted during the power clean remained stable across sets and days in unexperienced participants. Thus, the mathematical procedure for the extraction of muscle synergies through nonnegative matrix factorization (NMF) may be considered a reliable method to study muscle coordination adaptations from muscle strength programs.

Modelling force-length-activation relationships of wrist and finger extensor muscles

The wrist and finger extensors play a crucial role in the muscle coordination during grasping tasks. Nevertheless, few data are available regarding their force-generating capacities. The objective of this study was to provide a model of the force-length-activation relationships of the hand extensors using non-invasive methods. The extensor carpi radialis (ECR) and the extensor digitorum communis (EDC) were studied as representative of wrist and finger extensors. Ten participants performed isometric extension force-varying contractions in different postures on an ergometer recording resultant moment. The joint angle, the myotendinous junction displacement and activation were synchronously tracked using motion capture, ultrasound and electromyography. Muscle force was estimated via a musculoskeletal model using the measured joint angle and moment. The force-length-activation relationship was then obtained by fitting a force-length model at different activation levels to the measured data. The obtained relationships agreed with previously reported data regarding muscle architecture, sarcomere length and activation-dependent shift of optimal length. Muscle forces estimated from kinematics and electromyography using the force-length-activation relationships were comparable, below 15% differences, to those estimated from moment via the musculoskeletal model. The obtained quantitative data provides a new insight into the different muscle mechanics of finger and wrist extensors. Graphical abstract By combining in vivo data (kinematics, dynamometry, electromyography, ultrasonography) during isometric force-varying contractions with musculoskeletal modelling, the force-length-activation relationships of both finger and wrist extensors were obtained. The results provided a new insight into the role of hand extensors in the generation and control of hand movements.

Spatial Reorganization of Myoelectric Activities in Extensor Digitorum for Sustained Finger Force Production

The study aims to explore the spatial distribution of multi-tendinous muscle modulated by central nervous system (CNS) during sustained contraction. Nine subjects were recruited to trace constant target forces with right index finger extension. Surface electromyography (sEMG) of extensor digitorum (ED) were recorded with a 32-channel electrode array. Nine successive topographic maps (TM) were obtained. Pixel wise analysis was utilized to extract subtracted topographic maps (STM), which exhibited inhomogeneous distribution. STMs were characterized into hot, warm, and cool regions corresponding to higher, moderate, and lower change ranges, respectively. The relative normalized area (normalized to the first phase) of these regions demonstrated different changing trends as rising, plateauing, and falling over time, respectively. Moreover, the duration of these trends were found to be affected by force level. The rising/falling periods were longer at lower force levels, while the plateau can be achieved from the initial phase for higher force output (45% maximal voluntary contraction). The results suggested muscle activity reorganization in ED plays a role to maintain sustained contraction. Furthermore, the decreased dynamical regulation ability to spatial reorganization may be prone to induce fatigue. This finding implied that spatial reorganization of muscle activity as a regulation mechanism contribute to maintain constant force production.

Electrode placement on the forearm for selective stimulation of finger extension/flexion

It is still challenging to achieve a complex grasp or fine finger control by using surface functional electrical stimulation (FES), which usually requires a precise electrode configuration under laboratory or clinical settings. The goals of this study are as follows: 1) to study the possibility of selectively activating individual fingers; 2) to investigate whether the current activation threshold and selective range of individual fingers are affected by two factors: changes in the electrode position and forearm rotation (pronation, neutral and supination); and 3) to explore a theoretical model for guidance of the electrode placement used for selective activation of individual fingers. A coordinate system with more than 400 grid points was established over the forearm skin surface. A searching procedure was used to traverse all grid points to identify the stimulation points for finger extension/flexion by applying monophasic stimulation pulses. Some of the stimulation points for finger extension and flexion were selected and tested in their respective two different forearm postures according to the number and the type of the activated fingers and the strength of finger action response to the electrical stimulation at the stimulation point. The activation thresholds and current ranges of the selectively activated finger at each stimulation point were determined by visual analysis. The stimulation points were divided into three groups (“Low”, “Medium” and “High”) according to the thresholds of the 1^st activated fingers. The angles produced by the selectively activated finger within selective current ranges were measured and analyzed. Selective stimulation of extension/flexion is possible for most fingers. Small changes in electrode position and forearm rotation have no significant effect on the threshold amplitude and the current range for the selective activation of most fingers (p > 0.05). The current range is the largest (more than 2 mA) for selective activation of the thumb, followed by those for the index, ring, middle and little fingers. The stimulation points in the “Low” group for all five fingers lead to noticeable finger angles at low current intensity, especially for the index, middle, and ring fingers. The slopes of the finger angle variation in the “Low” group for digits 2~4 are inversely proportional to the current intensity, whereas the slopes of the finger angle variation in other groups and in all groups for the thumb and little finger are proportional to the current intensity. It is possible to selectively activate the extension/flexion of most fingers by stimulating the forearm muscles. The physiological characteristics of each finger should be considered when placing the negative electrode for selective stimulation of individual fingers. The electrode placement used for the selective activation of individual fingers should not be confined to the location with the lowest activation threshold.

ICA-based muscle-tendon units localization and activation analysis during dynamic motion tasks

This study proposed an independent component analysis (ICA)-based framework for localization and activation level analysis of muscle-tendon units (MTUs) within skeletal muscles during dynamic motion. The gastrocnemius muscle and extensor digitorum communis were selected as target muscles. High-density electrode arrays were used to record surface electromyographic (sEMG) data of the targeted muscles during dynamic motion tasks. First, the ICA algorithm was used to decompose multi-channel sEMG data into a weight coefficient matrix and a source matrix. Then, the source signal matrix was analyzed to determine EMG sources and noise sources. The weight coefficient vectors corresponding to the EMG sources were mapped to target muscles to find the location of the MTUs. Meanwhile, the activation level changes in MTUs during dynamic motion tasks were analyzed based on the corresponding EMG source signals. Eight subjects were recruited for this study, and the experimental results verified the feasibility and practicality of the proposed ICA-based method for the MTUs' localization and activation level analysis during dynamic motion. This study provided a new, in-depth way to analyze the functional state of MTUs during dynamic tasks and laid a solid foundation for MTU-based accurate muscle force estimation, muscle fatigue prediction, neuromuscular control characteristic analysis, etc.

Extracting extensor digitorum communis activation patterns using high-density surface electromyography

The extensor digitorum communis muscle plays an important role in hand dexterity during object manipulations. This multi-tendinous muscle is believed to be controlled through separate motoneuron pools, thereby forming different compartments that control individual digits. However, due to the complex anatomical variations across individuals and the flexibility of neural control strategies, the spatial activation patterns of the extensor digitorum communis compartments during individual finger extension have not been fully tracked under different task conditions. The objective of this study was to quantify the global spatial activation patterns of the extensor digitorum communis using high-density (7 × 9) surface electromyogram (EMG) recordings. The muscle activation map (based on the root mean square of the EMG) was constructed when subjects performed individual four finger extensions at the metacarpophalangeal joint, at different effort levels and under different finger constraints (static and dynamic). Our results revealed distinct activation patterns during individual finger extensions, especially between index and middle finger extensions, although the activation between ring and little finger extensions showed strong covariance. The activation map was relatively consistent at different muscle contraction levels and for different finger constraint conditions. We also found that distinct activation patterns were more discernible in the proximal–distal direction than in the radial–ulnar direction. The global spatial activation map utilizing surface grid EMG of the extensor digitorum communis muscle provides information for localizing individual compartments of the extensor muscle during finger extensions. This is of potential value for identifying more selective control input for assistive devices. Such information can also provide a basis for understanding hand impairment in individuals with neural disorders.

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.

Spatial localization of electromyographic amplitude distributions associated to the activation of dorsal forearm muscles

In this study we investigated whether the spatial distribution of surface electromyographic (EMG) amplitude can be used to describe the activation of muscle portions with different biomechanical actions. Ten healthy subjects performed isometric contractions aimed to selectively activate a number of forearm muscles or muscle subportions. Monopolar electromyographic signals were collected with an electrode grid of 128 electrodes placed on the proximal, dorsal portion of the forearm. The monopolar EMG amplitude [root mean square (RMS) value] distribution was calculated for each contraction, and high-amplitude channels were identified through an automatic procedure; the position of the EMG source was estimated with the barycenter of these channels. Each of the contractions tested was associated to a specific EMG amplitude distribution, whose location in space was consistent with the expected anatomical position of the main agonist muscle (or subportion). The position of each source was significantly different from the others in at least one direction (ANOVA; transversally to the forearm: P < 0.01, F = 125.92; longitudinally: P < 0.01, F = 35.83). With such an approach, we could distinguish the spatial position of EMG distributions related to the activation of contiguous muscles [e.g., extensor carpi ulnaris (ECU) and extensor digitorum communis (EDC)], different heads of the same muscle (i.e., extensor carpi radialis (ECR) brevis and longus) and different functional compartments (i.e., EDC, middle, and ring fingers). These findings are discussed in terms of how forces along a given direction can be produced by recruiting population of motor units clustered not only in specific muscles, but also in muscle sub-portions. In addition, this study supports the use of high-density EMG systems to characterize the activation of muscle subportions with different biomechanical actions.

Activation of individual extrinsic thumb muscles and compartments of extrinsic finger muscles

Mechanical and neurological couplings exist between musculotendon units of the human hand and digits. Studies have begun to understand how these muscles interact when accomplishing everyday tasks, but there are still unanswered questions regarding the control limitations of individual muscles. Using intramuscular electromyographic (EMG) electrodes, this study examined subjects' ability to individually initiate and sustain three levels of normalized muscular activity in the index and middle finger muscle compartments of extensor digitorum communis (EDC), flexor digitorum profundus (FDP), and flexor digitorum superficialis (FDS), as well as the extrinsic thumb muscles abductor pollicis longus (APL), extensor pollicis brevis (EPB), extensor pollicis longus (EPL), and flexor pollicis longus (FPL). The index and middle finger compartments each sustained activations with significantly different levels of coactivity from the other finger muscle compartments. The middle finger compartment of EDC was the exception. Only two extrinsic thumb muscles, EPL and FPL, were capable of sustaining individual activations from the other thumb muscles, at all tested activity levels. Activation of APL was achieved at 20 and 30% MVC activity levels with significantly different levels of coactivity. Activation of EPB elicited coactivity levels from EPL and APL that were not significantly different. These results suggest that most finger muscle compartments receive unique motor commands, but of the four thumb muscles, only EPL and FPL were capable of individually activating. This work is encouraging for the neural control of prosthetic limbs because these muscles and compartments may potentially serve as additional user inputs to command prostheses.

Independence and control of the fingers depend on direction and contraction mode

Both biomechanical and neural factors are suggested to contribute to the limited independence of finger movement and involuntary force production. The purpose of this study was to evaluate finger independence by examining the activity of the four compartments of extensor digitorum (ED) and flexor digitorum superficialis (FDS) and involuntary force production in the non-task fingers using the "enslaving effect" (EE). Twelve male participants performed a series of 5s sub-maximal exertions at 5%, 25%, 50% and 75% of maximum using isometric isotonic and ramp flexion and extension exertions. Ramp exertions were performed from 0% to 85% of each finger's maximum force with ascending and descending phases taking 4.5s. EE was lower in flexion exertions likely due to the higher activity of the antagonist ED compartments counterbalancing the involuntary activation of the non-task FDS compartments. Minimal FDS activity was seen during extension exertions. At forces up to and including 50%, both EE and muscle activity of the non-task compartments were significantly higher in descending exertions than isotonic or ascending exertions. Up to mid-level forces, both finger proximity and contraction mode affect involuntary force production and muscle activation while only finger proximity contributed to finger independence at higher forces.

Testing of instantaneity hypothesis for blind source separation of extensor indicis and extensor digiti minimi surface electromyograms

Human muscle activity can be assessed with surface electromyography (SEMG). Depending on electrode location and size, the recording volume under the sensor is likely to measure electrical potentials emanating from muscles other than the muscle of interest. This crosstalk issue makes interpretation of SEMG data difficult. The purpose of this paper was to study a crosstalk reduction technique called blind source separation (BSS). Most straightforward separation techniques rely on linearity and instantaneity (LI) of signal mixtures on the sensors. Literature on BSS for SEMG often makes hypothesis of linearity and instantaneity of the mixing model. Using simulation of SEMG mixtures and real SEMG recordings on the human extensor indicis (EI) and extensor digiti minimi (EDM) muscles during a task consisting of selective successive activations of EI and EDM muscles, cross-correlation between the sensors was proven to be directly dependent on instantaneity of the sources. Instantaneity hypothesis testing on real SEMG recordings showed that source instantaneity hypothesis is very sensitive to electrode location along the fibers direction. Source separation gains using JADE BSS algorithm depend strongly on instantaneity hypothesis. Using LI BSS on SEMG requires great attention to electrode positioning; we provide a tool to test these on EI/EDM muscles.

The effect of high pass filtering and non-linear normalization on the EMG-force relationship during sub-maximal finger exertions

Muscle force estimates are important for full understanding of the musculoskeletal system and EMG is a modeling method used to estimate muscle force. The purpose of this investigation was to examine the effect of high pass filtering and non-linear normalization on the EMG-force relationship of sub-maximal finger exertions. Sub-maximal isometric ramp exertions were performed under three conditions (i) extension with restraint at the mid-proximal phalanx, (ii) flexion at the proximal phalanx and (iii) flexion at the distal phalanx. Thirty high pass filter designs were compared to a standardized processing procedure and an exponential fit equation was used for non-linear normalization. High pass filtering significantly reduced the %RMS error and increased the peak cross correlation between EMG and force in the distal flexion condition and in the other two conditions there was a trend towards improving force predictions with high pass filtering. The degree of linearity differed between the three contraction conditions and high pass filtering improved the linearity in all conditions. Non-linear normalization had greater impact on the EMG-force relationship than high pass filtering. The difference in optimal processing parameters suggests that high pass filtering and linearity are dependent on contraction mode as well as the muscle analyzed.

Muscle-tendon units provide limited contributions to the passive stiffness of the index finger metacarpophalangeal joint

The passive stiffness at the MCP joint is a result of the elasticity of muscle-tendon units (MTUs) and capsule ligament complex (CLC), however, the relative contributions of these two components are unknown. We hypothesize that the MTUs provide the majority of the contributions to the joint stiffness by generating resistive forces when the MCP joint is flexed or extended. We used the work done by passive moments as a measure for the determination of the contributions to the joint stiffness. We conducted experiments with ten human subjects and collected joint angle and finger tip force data. The total passive moment and joint angle data were fitted with a double exponential model, and the passive moments due to the MTUs were determined by developing subject-specific models of the passive force-length change relationships. Our results show that for all the subjects, the work done by the passive moments from the MTUs is less than 50% of the total work done, and the CLC provides dominant contributions to the joint stiffness throughout the flexion-extension range of the joint angle. Therefore, the hypothesis that the MTUs provide the majority of the contributions to the MCP joint stiffness is not supported. We also determined that the majority of the MTUs passive moment was generated by the extrinsic MTUs and the contributions of the intrinsic MTUs was negligible.

Quantification of isolated muscle compartment activity in extrinsic finger muscles for potential prosthesis control sites

Prosthetic hands are becoming more advanced and gaining degrees-of-freedom similar to their human counterparts. However, the command interface enabling control of these prostheses needs to be improved for more intuitive functional use. One barrier to using electromyographic (EMG) signals as the command interface is measuring independent muscle control sites in the residual limb. Surface electrodes are commonly used to detect muscle activity in the forearm; however, the measured signals are often comprised of EMG signals from multiple muscles that are close together. This study investigated the suitability of the index and middle finger compartments of the extrinsic muscles as control sites for prostheses using a direct myocontrol interface. Fine-wire intramuscular electrodes were inserted into seven subjects and their ability to achieve isolated activations of each compartment was tested. The results showed five of the six compartments yield signals suitable for independent volitional control. The middle finger compartment of extensor digitorum communis was found to be incapable of isolated contractions and is therefore not recommended as a control site for direct myocontrol prostheses. A cross-correlation threshold was used to verify that simultaneously measured EMG signals were free from crosstalk and were therefore attributed to muscle co-activations.

Finger interaction in a three-dimensional pressing task

Accurate control of forces produced by the fingers is essential for performing object manipulation. This study examines the indices of finger interaction when accurate time profiles of force are produced in different directions, while using one of the fingers or all four fingers of the hand. We hypothesized that patterns of unintended force production among shear force components may involve features not observed in the earlier studies of vertical force production. In particular, we expected to see unintended forces generated by non-task fingers not in the direction of the instructed force but in the opposite direction as well as substantial force production in directions orthogonal to the instructed direction. We also tested a hypothesis that multi-finger synergies, quantified using the framework of the uncontrolled manifold hypothesis, will help reduce across-trials variance of both total force magnitude and direction. Young, healthy subjects were required to produce accurate ramps of force in five different directions by pressing on force sensors with the fingers of the right (dominant) hand. The index finger induced the smallest unintended forces in non-task fingers. The little finger showed the smallest unintended forces when it was a non-task finger. Task fingers showed substantial force production in directions orthogonal to the intended force direction. During four-finger tasks, individual force vectors typically pointed off the task direction, with these deviations nearly perfectly matched to produce a resultant force in the task direction. Multi-finger synergy indices reflected strong co-variation in the space of finger modes (commands to fingers) that reduced variability of the total force magnitude and direction across trials. The synergy indices increased in magnitude over the first 30% of the trial time and then stayed at a nearly constant level. The synergy index for stabilization of total force magnitude was higher for shear force components when compared to the downward pressing force component. The results suggest complex interactions between enslaving and synergic force adjustments, possibly reflecting the experience with everyday prehensile tasks. For the first time, the data document multi-finger synergies stabilizing both shear force magnitude and force vector direction. These synergies may play a major role in stabilizing the hand action during object manipulation.

Limited ability to extend the digits of the human hand independently with extensor digitorum

While the human hand has an extraordinary capacity to manipulate objects, movement of its digits is usually not completely independent. These limits have been documented for extrinsic flexor muscles, although hand skills also require selectivity for extension movements. Hence, we measured the degree of independent control of the major extrinsic extensor (extensor digitorum, ED). Subjects grasped a cylinder, with the thumb perpendicular to the fingers. Load cells were connected to the proximal phalanges of the fingers and the thumb's distal phalanx. Intramuscular recordings using needle electrodes were made from the individual digital compartments of ED. Subjects were instructed to extend each digit isometrically in a voluntary ramp contraction to 50% maximal force. In total, the behaviour of 283 single motor units was analysed. More than half of the units associated with one 'test' finger were recruited inadvertently when another digit contracted to 50% maximum, with most units being recruited by extension of the adjacent digits. Usually, test motor units were recruited at higher forces by extension of fingers further from the test finger. Unexpectedly, extension of the thumb recruited many motor units acting on the little finger. Across tasks, at recruitment of the test motor units, the force produced by the test finger often differed between the voluntary and inadvertent contractions. Overall, the independent control of the output of ED is limited; this may reflect 'spill-over' of motor commands to other digital extensor compartments. This level of control of the extrinsic extensor muscles is more independent than the control of the deep extrinsic flexor muscle but less independent than that of the superficial extrinsic flexor muscle.

Assessment of individual finger muscle activity in the extensor digitorum communis by surface EMG

The extensor digitorum communis (ED) is a slender muscle group in the dorsal forearm from which tendons arise to the index (D2), medius (D3), ring (D4), and little (D5) fingers. Limited independence has been attributed to the parts that actuate the individual fingers. However, in a detailed anatomical analysis, it was found that the ED parts to the different fingers have constant and widely spaced anatomical locations that promote independent function. These observations and the superficial muscle belly locations prompted the hypothesis that these ED parts would be individually assessable by small anatomically placed surface EMG electrodes. In the present study, this hypothesis was evaluated by measuring electromyography (EMG) from the ED parts and surrounding muscles during individual finger tapping tasks with the forearm resting on a flat surface. It was found that individual ED activity can be well measured in ED2, ED3, ED4, and extensor digiti minimi (EDM). ED3 did not give nor did its electrodes receive significant crosstalk from other ED parts. ED4 electrodes recorded an EMG level of 30 +/- 19% (mean +/- SD) ED2 EMG in D2 tapping and ED2 electrodes a level of 53 +/- 22% ED4 EMG in D4 tapping, by hypothesis mostly crosstalk. EDM electrodes may record EMG at the level of ED4 EMG in D4 tapping. In D2 tapping, the mutual ED2 and extensor indicis redundancy reflected in large intersubject EMG differences with sometimes one or the other almost silent. The results may expand the possibilities of EMG analysis and finger muscle electrostimulation in ergonomic and clinical applications.