Differences between kinematic synergies and muscle synergies during two-digit grasping

Muscle synergy and kinematic synergy analyses during sit-to-stand motions in hallux valgus patients before and after treatment with Kinesio taping

OBJECTIVES

To explore the impact of hallux valgus (HV) on lower limb neuromuscular control strategies during the sit-to-stand (STS) movement, and to evaluate the effects of Kinesio taping (KT) intervention on these control strategies in HV patients.

METHODS

We included 14 young healthy controls (HY), 13 patients in the HV group (HV), and 11 patients in the HV group (HVI) who underwent a Kinesio taping (KT) intervention during sit-to-stand (STS) motions. We extracted muscle and kinematic synergies from EMG and motion capture data using non-negative matrix factorization (NNMF). In addition, we calculated the center of pressure (COP) and ground reaction forces (GRF) to assess balance performance.

RESULTS

There were no significant differences in the numbers of muscle and kinematic synergies between groups. In the HV group, knee flexors and ankle plantar flexors were abnormally activated, and muscle synergy D was differentiated. Muscle synergy D was not differentiated in the HVI group.

CONCLUSION

Abnormal activation of knee flexors and plantar flexors led to the differentiation of module D in HV patients, which can be used as an indicator of the progress of HV rehabilitation. KT intervention improved motor control mechanisms in HV patients.

Hand muscle synergy in chopstick use: effect of object size and weight

This study explains the role of muscle coordination in chopstick manipulation and investigates the effects of object width and weight on intrinsic and extrinsic hand muscle activity when picking up objects with chopsticks. Surface electromyography was used to measure the activity of the intrinsic and extrinsic hand muscles when picking up objects of varying widths and weights using chopsticks. The results revealed coordinated muscle activity patterns in the intrinsic and extrinsic hand muscles and coordination between them during chopstick manipulation. Object widths varying between 1 and 3 cm did not significantly affect muscle activity; however, object weight influenced muscle activity during both chopstick closing and object grasping, with greater muscle activity in the 40 g condition than in the 10 g condition. Intrinsic hand muscles were found to be involved in object grasping, regardless of object weight. These findings suggest that object weight should be considered when practicing picking up objects with chopsticks in scenarios resembling daily dining, to prevent excessive muscle activity during rehabilitation.

Biomimetic learning of hand gestures in a humanoid robot

Hand gestures are a natural and intuitive form of communication, and integrating this communication method into robotic systems presents significant potential to improve human-robot collaboration. Recent advances in motor neuroscience have focused on replicating human hand movements from synergies also known as movement primitives. Synergies, fundamental building blocks of movement, serve as a potential strategy adapted by the central nervous system to generate and control movements. Identifying how synergies contribute to movement can help in dexterous control of robotics, exoskeletons, prosthetics and extend its applications to rehabilitation. In this paper, 33 static hand gestures were recorded through a single RGB camera and identified in real-time through the MediaPipe framework as participants made various postures with their dominant hand. Assuming an open palm as initial posture, uniform joint angular velocities were obtained from all these gestures. By applying a dimensionality reduction method, kinematic synergies were obtained from these joint angular velocities. Kinematic synergies that explain 98% of variance of movements were utilized to reconstruct new hand gestures using convex optimization. Reconstructed hand gestures and selected kinematic synergies were translated onto a humanoid robot, Mitra, in real-time, as the participants demonstrated various hand gestures. The results showed that by using only few kinematic synergies it is possible to generate various hand gestures, with 95.7% accuracy. Furthermore, utilizing low-dimensional synergies in control of high dimensional end effectors holds promise to enable near-natural human-robot collaboration.

A tensor decomposition reveals ageing-induced differences in muscle and grip-load force couplings during object lifting

Do motor patterns of object lifting movements change as a result of ageing? Here we propose a methodology for the characterization of these motor patterns across individuals of different age groups. Specifically, we employ a bimanual grasp-lift-replace protocol with younger and older adults and combine measurements of muscle activity with grip and load forces to provide a window into the motor strategies supporting effective object lifts. We introduce a tensor decomposition to identify patterns of muscle activity and grip-load force ratios while also characterizing their temporal profiles and relative activation across object weights and participants of different age groups. We then probe age-induced changes in these components. A classification analysis reveals three motor components that are differentially recruited between the two age groups. Linear regression analyses further show that advanced age and poorer manual dexterity can be predicted by the coupled activation of forearm and hand muscles which is associated with high levels of grip force. Our findings suggest that ageing may induce stronger muscle couplings in distal aspects of the upper limbs, and a less economic grasping strategy to overcome age-related decline in manual dexterity.

Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies

Objective.We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control.Approach.Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes. Motion capture was concurrently recorded to estimate hand kinematics. Muscle synergies were extracted separately for arm and forearm muscles, and postural synergies were extracted from hand joint angles. We assessed whether activation coefficients of postural synergies positively correlate with and can be regressed from activation coefficients of muscle synergies. Each type of synergies was clustered across subjects.Main results.We found consistency of the identified synergies across subjects, and we functionally evaluated synergy clusters computed across subjects to identify synergies representative of all subjects. We found a positive correlation between pairs of activation coefficients of muscle and postural synergies with important functional implications. We demonstrated a significant positive contribution in the combination between arm and forearm muscle synergies in estimating hand postural synergies with respect to estimation based on muscle synergies of only one body segment, either arm or forearm (p< 0.01). We found that dimensionality reduction of multi-muscle EMG root mean square (RMS) signals did not significantly affect hand posture estimation, as demonstrated by comparable results with regression of hand angles from EMG RMS signals.Significance.We demonstrated that hand posture prediction improves by combining activity of arm and forearm muscles and we evaluate, for the first time, correlation and regression between activation coefficients of arm muscle and hand postural synergies. Our findings can be beneficial for myoelectric control of hand prosthesis and upper-limb exoskeletons, and for biomarker evaluation during neurorehabilitation.

Gait asymmetry in children with Duchenne muscular dystrophy: evaluated through kinematic synergies and muscle synergies of lower limbs

BACKGROUND

Gait is a complex, whole-body movement that requires the coordinated action of multiple joints and muscles of our musculoskeletal system. In the context of Duchenne muscular dystrophy (DMD), a disease characterized by progressive muscle weakness and joint contractures, previous studies have generally assumed symmetrical behavior of the lower limbs during gait. However, such a symmetric gait pattern of DMD was controversial. One aspect of this is criticized, because most of these studies have primarily focused on univariate variables, rather than on the coordination of multiple body segments and even less investigate gait symmetry under a motor synergy of view.

METHODS

We investigated the gait pattern of 20 patients with DMD, compared to 18 typical developing children (TD) through 3D Gait Analysis. Kinematic and muscle synergies were extracted with principal component analysis (PCA) and non-negative matrix factorization (NNMF), respectively. The synergies extracted from the left and right sides were compared with each other to obtain a symmetry value. In addition, bilateral spatiotemporal variables of gait, such as stride length, percentage of stance and swing phase, step length, and percentage of double support phase, were used for calculating the symmetry index (SI) to evaluate gait symmetry as well.

RESULTS

Compared with the TD group, the DMD group walked with decreased gait velocity (both p < 0.01), stride length (both p < 0.01), and step length (both p < 0.001). No significant difference was found between groups in SI of all spatiotemporal parameters extracted between the left and right lower limbs. In addition, the DMD group exhibited lower kinematic synergy symmetry values compared to the TD group (p < 0.001), while no such significant group difference was observed in symmetry values of muscle synergy.

CONCLUSIONS

The findings of this study suggest that DMD influences, to some extent, the symmetry of synergistic movement of multiple segments of lower limbs, and thus kinematic synergy appears capable of discriminating gait asymmetry in children with DMD when conventional spatiotemporal parameters are unchanged.

Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion

Transhumeral amputees experience considerable difficulties with controlling a multifunctional prosthesis (powered hand, wrist, and elbow) due to the lack of available muscles to provide electromyographic (EMG) signals. The residual limb motion strategy has become a popular alternative for transhumeral prosthesis control. It provides an intuitive way to estimate the motion of the prosthesis based on the residual shoulder motion, especially for target reaching tasks. Conventionally, a predictive model, typically an artificial neural network (ANN), is directly trained and relied upon to map the shoulder-elbow kinematics using the data from able-bodied subjects without extracting any prior synergistic information. However, it is essential to explicitly identify effective synergies and make them transferable across amputee users for higher accuracy and robustness. To overcome this limitation of the conventional ANN learning approach, this study explicitly combines the kinematic synergies with a recurrent neural network (RNN) to propose a synergy-space neural network for estimating forearm motions (i.e., elbow joint flexion-extension and pronation-supination angles) based on residual shoulder motions. We tested 36 training strategies for each of the 14 subjects, comparing the proposed synergy-space and conventional neural network learning approaches, and we statistically evaluated the results using Pearson's correlation method and the analysis of variance (ANOVA) test. The offline cross-subject analysis indicates that the synergy-space neural network exhibits superior robustness to inter-individual variability, demonstrating the potential of this approach as a transferable and generalized control strategy for transhumeral prosthesis control.

Analysis of cerebral palsy gait based on movement primitives

BACKGROUND

Cerebral palsy is the most prevalent motor disorder among children. Despite extensive studies on motor modularity of gait of children with cerebral palsy, kinematic modularity of their gait has not been addressed which is the main goal of this study.

METHODS

The kinematics of the gait of 13 typical development children and 188 children with cerebral palsy was captured and analyzed, where the cerebral palsy children were grouped into True, Jump, Apparent, and Crouch. Non-negative matrix factorization method was used to extract the kinematic modulus of each group, which were then clustered to find their characteristic movement primitives. The movement primitives of groups were then matched based on the similarity of their activation profiles.

FINDINGS

The number of movement primitives was three for the Crouch group, four for the other cerebral palsy groups, and five for the typical development group. Compared to the typical development children, the kinematic modules and activations of the cerebral palsy groups involved higher variability and co-activation, respectively (P < 0.05). Three temporally matched movement primitives were shared by all groups, but with altered structures.

INTERPRETATION

The gait of children with cerebral palsy involved lower complexity and higher variability due to the reduced and inconsistent kinematic modularity. Three basic movement primitives were sufficient to prodcue the overall gait kinematics, as observed in the Crouch group. Other movement primitives, were responsible for providing smooth transitions between basic movement primitives, as seen in more complex gait patterns.

Similarity of hand muscle synergies elicited by transcranial magnetic stimulation and those found during voluntary movement

Converging evidence in human and animal models suggests that exogenous stimulation of the motor cortex (M1) elicits responses in the hand with similar modular structure to that found during voluntary grasping movements. The aim of this study was to establish the extent to which modularity in muscle responses to transcranial magnetic stimulation (TMS) to M1 resembles modularity in muscle activation during voluntary hand movements involving finger fractionation. Electromyography (EMG) was recorded from eight hand-forearm muscles in eight healthy individuals. Modularity was defined using non-negative matrix factorization to identify low-rank approximations (spatial muscle synergies) of the complex activation patterns of EMG data recorded during high-density TMS mapping of M1 and voluntary formation of gestures in the American Sign Language alphabet. Analysis of synergies revealed greater than chance similarity between those derived from TMS and those derived from voluntary movement. Both data sets included synergies dominated by single intrinsic hand muscles presumably to meet the demand for highly fractionated finger movement. These results suggest that corticospinal connectivity to individual intrinsic hand muscles may be combined with modular multimuscle activation via synergies in the formation of hand postures.NEW & NOTEWORTHY This is the first work to examine the similarity of modularity in hand muscle responses to transcranial magnetic stimulation (TMS) of the motor cortex and that derived from voluntary hand movement. We show that TMS-elicited muscle synergies of the hand, measured at rest, reflect those found in voluntary behavior involving finger fractionation. This work provides a basis for future work using TMS to investigate muscle activation modularity in the human motor system.

Age-related differences in gait symmetry obtained from kinematic synergies and muscle synergies of lower limbs during childhood

The age-related changes of gait symmetry in healthy children concerning individual joint and muscle activation data have previously been widely studied. Extending beyond individual joints or muscles, identifying age-related changes in the coordination of multiple joints or muscles (i.e., muscle synergies and kinematic synergies) could capture more closely the underlying mechanisms responsible for gait symmetry development. To evaluate the effect of age on the symmetry of the coordination of multiple joints or muscles during childhood, we measured gait symmetry by kinematic and EMG data in 39 healthy children from 2 years old to 14 years old, divided into three equal age groups: preschool children (G1; 2.0-5.9 years), children (G2; 6.0-9.9 years), pubertal children (G3; 10.0-13.9 years). Participants walked barefoot at a self-selected walking speed during three-dimensional gait analysis (3DGA). Kinematic synergies and muscle synergies were extracted with principal component analysis (PCA) and non-negative matrix factorization (NNMF), respectively. The synergies extracted from the left and right sides were compared with each other to obtain a symmetry value. Statistical analysis was performed to examine intergroup differences. The results showed that the effect of age was significant on the symmetry values extracted by kinematic synergies, while older children exhibited higher kinematic synergy symmetry values compared to the younger group. However, no significant age-related changes in symmetry values of muscle synergy were observed. It is suggested that kinematic synergy of lower joints can be asymmetric at the onset of independent walking and showed improving symmetry with increasing age, whereas the age-related effect on the symmetry of muscle synergies was not demonstrated. These data provide an age-related framework and normative dataset to distinguish age-related differences from pathology in children with neuromotor disorders.

Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals

Brain-machine interfaces (BMIs) have become increasingly popular in restoring the lost motor function in individuals with disabilities. Several research studies suggest that the CNS may employ synergies or movement primitives to reduce the complexity of control rather than controlling each DoF independently, and the synergies can be used as an optimal control mechanism by the CNS in simplifying and achieving complex movements. Our group has previously demonstrated neural decoding of synergy-based hand movements and used synergies effectively in driving hand exoskeletons. In this study, ten healthy right-handed participants were asked to perform six types of hand grasps representative of the activities of daily living while their neural activities were recorded using electroencephalography (EEG). From half of the participants, hand kinematic synergies were derived, and a neural decoder was developed, based on the correlation between hand synergies and corresponding cortical activity, using multivariate linear regression. Using the synergies and the neural decoder derived from the first half of the participants and only cortical activities from the remaining half of the participants, their hand kinematics were reconstructed with an average accuracy above 70%. Potential applications of synergy-based BMIs for controlling assistive devices in individuals with upper limb motor deficits, implications of the results in individuals with stroke and the limitations of the study were discussed.

Kinematic synergies in over-ground slip recovery outcomes: Distinct strategies or a single strategy?

BACKGROUND

After experiencing an unexpected slip perturbation, individuals' behavioral performance can be classified into three categories: recovery, feet-forward fall, and split fall. Researchers are uncertain whether these differences in slip outcomes are due to distinct strategies or part of a single strategy.

RESEARCH QUESTION

Whether older adults with different behavioral outcomes during their novel slip have different kinematic synergies?

METHODS

The kinematic synergies were extracted from segment angles in 87 participants using principal component analysis (PCA). The first two principal components (PC1 and PC2) in pre-slip, early-reactive, and late-reactive phases were compared across different slip outcomes.

RESULTS

Results showed that the kinematic synergies in pre-slip and early-reactive phases are highly consistent among the three outcomes (recovery, split fall, and feet-forward fall). For the late-reactive phase, both split falls and feet-forward falls showed different kinematics synergies from recoveries.

SIGNIFICANCE

Our findings indicated that a single strategy might be used for different slip outcomes in the pre-slip and early-reactive phases, while distinct strategies were used by fallers compared to recovered individuals. Specifically, larger trunk flexion in pre-slip phase, larger knee flexion and plantar flexion of the slipping limb in both early-reactive and late-reactive phase, and larger knee extension of the recovery limb in late-reactive phase would lower the fall risk. This study would help to assess the vulnerabilities in control strategy, according to which individualized treatment could be provided to reduce predisposition to specific types of falls.

Motor synergy generalization framework for new targets in multi-planar and multi-directional reaching task

Humans can rapidly adapt to new situations, even though they have redundant degrees of freedom (d.f.). Previous studies in neuroscience revealed that human movements could be accounted for by low-dimensional control signals, known as motor synergies. Many studies have suggested that humans use the same repertories of motor synergies among similar tasks. However, it has not yet been confirmed whether the combinations of motor synergy repertories can be re-used for new targets in a systematic way. Here we show that the combination of motor synergies can be generalized to new targets that each repertory cannot handle. We use the multi-directional reaching task as an example. We first trained multiple policies with limited ranges of targets by reinforcement learning and extracted sets of motor synergies. Finally, we optimized the activation patterns of sets of motor synergies and demonstrated that combined motor synergy repertories were able to reach new targets that were not achieved with either original policies or single repertories of motor synergies. We believe this is the first study that has succeeded in motor synergy generalization for new targets in new planes, using a full 7-d.f. arm model, which is a realistic mechanical environment for general reaching tasks.

Effect of the position of the interphalangeal joint on movements of the trapeziometacarpal joint during thumb opposition

The Kapandji test is a simple method to score thumb opposition; however, the position of the interphalangeal joint of the thumb during this test has not been described. We aimed to quantitatively examine the effect of the thumb interphalangeal joint position on movements of the trapeziometacarpal joint during thumb opposition using the Kapandji test. The Kapandji test was carried out in 20 healthy participants during thumb interphalangeal joint extension and flexion. Movements of the joints and the activity of thenar muscles were recorded using motion capture and electromyography, respectively. We found that interphalangeal joint extension increased the trapeziometacarpal joint movement and thenar muscle activity compared with interphalangeal joint flexion, which contributed to thumb opposition at Kapandji Positions 0-6. These findings suggest the position of the thumb interphalangeal joint affects the trapeziometacarpal joint during thumb opposition, and assessment of thumb opposition using the Kapandji test is best done with the thumb interphalangeal joint in extension.

Temporal Synergies Detection in Gait Cyclograms Using Wearable Technology

The human gait can be described as the synergistic activity of all individual components of the sensory–motor system. The central nervous system (CNS) develops synergies to execute endpoint motion by coordinating muscle activity to reflect the global goals of the endpoint trajectory. This paper proposes a new method for assessing temporal dynamic synergies. Principal component analysis (PCA) has been applied on the signals acquired by wearable sensors (inertial measurement units, IMU and ground reaction force sensors, GRF mounted on feet) to detect temporal synergies in the space of two-dimensional PCA cyclograms. The temporal synergy results for different gait speeds in healthy subjects and stroke patients before and after the therapy were compared. The hypothesis of invariant temporal synergies at different gait velocities was statistically confirmed, without the need to record and analyze muscle activity. A significant difference in temporal synergies was noticed in hemiplegic gait compared to healthy gait. Finally, the proposed PCA-based cyclogram method provided the therapy follow-up information about paretic leg gait in stroke patients that was not available by observing conventional parameters, such as temporal and symmetry gait measures.

Mixed matrix factorization: a novel algorithm for the extraction of kinematic-muscular synergies

Synergistic models have been employed to investigate motor coordination separately in the muscular and kinematic domains. However, the relationship between muscle synergies, constrained to be non-negative, and kinematic synergies, whose elements can be positive and negative, has received limited attention. Existing algorithms for extracting synergies from combined kinematic and muscular data either do not enforce non-negativity constraints or separate non-negative variables into positive and negative components. We propose a mixed matrix factorization (MMF) algorithm based on a gradient descent update rule which overcomes these limitations. It allows to directly assess the relationship between kinematic and muscle activity variables, by enforcing the non-negativity constrain on a subset of variables. We validated the algorithm on simulated kinematic-muscular data generated from known spatial synergies and temporal coefficients, by evaluating the similarity between extracted and ground truth synergies and temporal coefficients when the data are corrupted by different noise levels. We also compared the performance of MMF to that of non-negative matrix factorization applied to separate positive and negative components (NMFpn). Finally, we factorized kinematic and EMG data collected during upper-limb movements to demonstrate the potential of the algorithm. MMF achieved almost perfect reconstruction on noiseless simulated data. It performed better than NMFpn in recovering the correct spatial synergies and temporal coefficients with noisy simulated data. It also allowed to correctly select the original number of ground truth synergies. We showed meaningful applicability to real data; MMF can also be applied to any multivariate data that contains both non-negative and unconstrained variables.

Biomechanical demands of percussive techniques in the context of early stone toolmaking

Recent discoveries in archaeology and palaeoanthropology highlight that stone tool knapping could have emerged first within the genera Australopithecus or Kenyanthropus rather than Homo. To explore the implications of this hypothesis determining the physical demands and motor control needed for performing the percussive movements during the oldest stone toolmaking technology (i.e. Lomekwian) would help. We analysed the joint angle patterns and muscle activity of a knapping expert using three stone tool replication techniques: unipolar flaking on the passive hammer (PH), bipolar (BP) flaking on the anvil, and multidirectional and multifacial flaking with free hand (FH). PH presents high levels of activity for Biceps brachii and wrist extensors and flexors. By contrast, BP and FH are characterized by high solicitation of forearm pronation. The synergy analyses depict a high muscular and kinematic coordination. Whereas the muscle pattern is very close between the techniques, the kinematic pattern is more variable, especially for PH. FH displays better muscle coordination and conversely lesser joint angle coordination. These observations suggest that the transition from anvil and hammer to freehand knapping techniques in early hominins would have been made possible by the acquisition of a behavioural repertoire producing an evolutionary advantage that gradually would have been beneficial for stone tool production.

Modularity in Motor Control: Similarities in Kinematic Synergies Across Varying Locomotion Tasks

Kinematic synergies (kSYN) provide an approach to quantify the covariation of joint motions and to explain the mechanisms underlying human motor behavior. A low-dimensional control strategy by means of the activation of a moderate number of kSYN would simplify the performance of complex motor tasks. The purpose of this study was to examine similarities between the kSYN of varying locomotion tasks: straight-line walking, walking a 90° spin turn and walking upstairs. Task-specific kSYN were extracted from full body kinematic recordings of 13 participants by principal component analysis. The first five kSYN accounting for most of the variance within each task were selected for further analysis following previous studies. The similarities between the kSYN of the three different locomotion tasks were quantified by calculating cosine similarities (SIM), as a vector-based similarity measure ranging from 0 (no similarity) to 1 (high similarity), between absolute principal component loading vectors. A SIM between two kSYN > 0.8 was interpreted as highly similar. Two to three highly similar kSYN were identified when comparing two individual tasks with each other. One kSYN, primarily characterized by anteversion and retroversion of the arms and legs, were found to be similar in all three tasks. Additional kSYN that occurred between individual tasks reflected mainly an upwards/downwards movement of the body or a countercyclical knee flexion/extension. The results demonstrate that the three investigated locomotion tasks are characterized by kSYN and that certain kSYN repeatedly occur across the three locomotion tasks. PCA yields kSYN which are in descent order according to their amount of total variance accounted for. Referring to the placing of a kSYN within the order as priorization, we found a change in priorization of repeatedly occurring kSYN across the individual tasks. The findings support the idea that movements can be efficiently performed through a flexible combination of a lower number of control-relevant variables.

Hand Kinematics Characterization While Performing Activities of Daily Living Through Kinematics Reduction

Improving the understanding of hand kinematics during the performance of activities of daily living may help improve the control of hand prostheses and hand function assessment. This work identifies sparse synergies (each degree of freedom is present mainly in only one synergy), representative of the global population, with emphasis in unveiling the coordination of joints with small range of motion (palmar arching and fingers abduction). The study is the most complete study described in the literature till now, involving 22 healthy subjects and 26 representative day-to-day life activities. Principal component analysis was used to reduce the original 16 angles recorded with an instrumented glove. Five synergies explained 75% of total variance: closeness (coordinated flexion and abduction of metacarpophalangeal finger joints), digit arching (flexion of proximal interphalangeal joints), palmar-thumb coordination (coordination of palmar arching and thumb carpometacarpal flexion), thumb opposition, and thumb arch. The temporal evolution of these synergies is provided during reaching per intended grasp and during manipulation per specific task, which could be used as normative patterns for the global population. Reaching has been observed to require the modulation of closeness, digit arch and thumb opposition synergies, with different control patterns per grasp. All the synergies are very important during manipulation and need to be modulated for all the tasks. Finally, groups of tasks with similar kinematic requirements in terms of synergies have been identified, which could benefit the selection of tasks for rehabilitation and hand function assessments.

Computational evidence for nonlinear feedforward modulation of fusimotor drive to antagonistic co-contracting muscles

The sensorimotor integration during unconstrained reaching movements in the presence of variable environmental forces remains poorly understood. The objective of this study was to quantify how much the primary afferent activity of muscle spindles can contribute to shaping muscle coactivation patterns during reaching movements with complex dynamics. To achieve this objective, we designed a virtual reality task that guided healthy human participants through a set of planar reaching movements with controlled kinematic and dynamic conditions that were accompanied by variable muscle co-contraction. Next, we approximated the Ia afferent activity using a phenomenological model of the muscle spindle and muscle lengths derived from a musculoskeletal model. The parameters of the spindle model were altered systematically to evaluate the effect of fusimotor drive on the shape of the temporal profile of afferent activity during movement. The experimental and simulated data were analyzed with hierarchical clustering. We found that the pattern of co-activation of agonistic and antagonistic muscles changed based on whether passive forces in each movement played assistive or resistive roles in limb dynamics. The reaching task with assistive limb dynamics was associated with the most muscle co-contraction. In contrast, the simulated Ia afferent profiles were not changing between tasks and they were largely reciprocal with homonymous muscle activity. Simulated physiological changes to the fusimotor drive were not sufficient to reproduce muscle co-contraction. These results largely rule out the static set and α-γ coactivation as the main types of fusimotor drive that transform the monosynaptic Ia afferent feedback into task-dependent co-contraction of antagonistic muscles. We speculate that another type of nonlinear transformation of Ia afferent signals that is independent of signals modulating the activity of α motoneurons is required for Ia afferent-based co-contraction. This transformation could either be applied through a complex nonlinear profile of fusimotor drive that is not yet experimentally observed or through presynaptic inhibition.

Extraction of Nonlinear Synergies for Proportional and Simultaneous Estimation of Finger Kinematics

OBJECTIVE

Proportional and simultaneous est-imation of finger kinematics from surface EMG based on the assumption that there exists a correlation between muscle activations and finger kinematics in low dimensional space.

METHODS

We employ Manifold Relevance Determination (MRD), a multi-view learning model with a nonparametric Bayesian approach, to extract the nonlinear muscle and kinematics synergies and the relationship between them by studying muscle activations (input-space) together with the finger kinematics (output-space).

RESULTS

This study finds that there exist muscle synergies which are associated with kinematic synergies. The acquired nonlinear synergies and the association between them has further been utilized for the estimation of finger kinematics from muscle activation inputs, and the proposed approach has outperformed other commonly used linear and nonlinear regression approaches with an average correlation coefficient of 0.91±0.03.

CONCLUSION

There exists an association between muscle and kinematic synergies which can be used for the proportional and simultaneous estimation of finger kinematics from the muscle activation inputs.

SIGNIFICANCE

The findings of this study not only presents a viable approach for accurate and intuitive myoelectric control but also provides a new perspective on the muscle synergies in the motor control community.

A calibrated database of kinematics and EMG of the forearm and hand during activities of daily living

Linking hand kinematics and forearm muscle activity is a challenging and crucial problem for several domains, such as prosthetics, 3D modelling or rehabilitation. To advance in this relationship between hand kinematics and muscle activity, synchronised and well-defined data are needed. However, currently available datasets are scarce, and the presented tasks and data are often limited. This paper presents the KIN-MUS UJI Dataset that contains 572 recordings with anatomical angles and forearm muscle activity of 22 subjects while performing 26 representative activities of daily living. This dataset is, to our knowledge, the biggest currently available hand kinematics and muscle activity dataset to focus on goal-oriented actions. Data were recorded using a CyberGlove instrumented glove and surface EMG electrodes, both properly synchronised. Eighteen hand anatomical angles were obtained from the glove sensors by a validated calibration procedure. Surface EMG activity was recorded from seven representative forearm areas. The statistics verified that data were not affected by the experimental procedures and were similar to the data acquired under real-life conditions.

Muscle synergies for predicting non-isometric complex hand function for commanding FES neuroprosthetic hand systems

OBJECTIVE

Myoelectric controlled neuroprostheses can restore hand function to mid-cervical level (C5/C6) paralyzed individuals through voluntary control. However restored functionality is limited due to the small number of available voluntary electromyographic (EMG) signals after paralysis. The purpose of this study was to determine whether dynamic hand function could be reduced to as few as three degrees of freedom using the time-invariant muscle synergy model thereby showing the feasibility of synergy-based neuroprosthetic control.

APPROACH

Task cross-validated, time-invariant synergies were derived from static hand postures and from dynamic functional task data collected from five able-bodied participants. The time-invariant synergies were extracted from EMG data in task cross-validation using non-negative matrix factorization.

MAIN RESULTS

Three functional synergies yielded significantly higher performance than chance (p   <  0.01) with 66.0%  ±  4.9% variance accounted for (VAF) compared to 42.5%  ±  4.4% VAF.

SIGNIFICANCE

The results of this study, along with other studies showing continuous 3D EMG control, show the feasibility of a possible synergy-based controller for hand neuroprostheses.

On the Relationship Between Human Motor Control Performance and Kinematic Synergies in Upper Limb Prosthetics

Current prosthesis command interfaces only allow for a single degree of freedom to be commanded at a time, making coordinated motion difficult to achieve. Thus it becomes crucial to develop methods that complement these interfaces to allow for intuitive coordinated arm motion. Kinematic synergies have been shown as an alternate method where the motion of the prosthetic device is coordinated with that of the residual limb. In this paper, the mapping between the parameters of a kinematic synergy model and a measure of task performance is established experimentally in order to test the applicability of online optimization methods for the identification of synergies. To achieve this, a cost function that captures the objective of the reaching task and a linear kinematic synergy model were chosen. A human experiment was developed in a Virtual Reality (VR) platform in order to determine the synergy-performance relationship. The experiments were performed on 10 able-bodied subjects. The relationship observed between the synergy parameter and the reaching task cost function suggests existing online optimization methods may be applicable.

Exploring representations of human grasping in neural, muscle and kinematic signals

Movement covariates, such as electromyographic or kinematic activity, have been proposed as candidates for the neural representation of hand control. However, it remains unclear how these movement covariates are reflected in electroencephalographic (EEG) activity during different stages of grasping movements. In this exploratory study, we simultaneously acquired EEG, kinematic and electromyographic recordings of human subjects performing 33 types of grasps, yielding the largest such dataset to date. We observed that EEG activity reflected different movement covariates in different stages of grasping. During the pre-shaping stage, centro-parietal EEG in the lower beta frequency band reflected the object's shape and size, whereas during the finalization and holding stages, contralateral parietal EEG in the mu frequency band reflected muscle activity. These findings contribute to the understanding of the temporal organization of neural grasping patterns, and could inform the design of noninvasive neuroprosthetics and brain-computer interfaces with more natural control.

Muscle Synergy Analysis of a Hand-Grasp Dataset: A Limited Subset of Motor Modules May Underlie a Large Variety of Grasps

Background: Kinematic and muscle patterns underlying hand grasps have been widely investigated in the literature. However, the identification of a reduced set of motor modules, generalizing across subjects and grasps, may be valuable for increasing the knowledge of hand motor control, and provide methods to be exploited in prosthesis control and hand rehabilitation.

Methods: Motor muscle synergies were extracted from a publicly available database including 28 subjects, executing 20 hand grasps selected for daily-life activities. The spatial synergies and temporal components were analyzed with a clustering algorithm to characterize the patterns underlying hand-grasps.

Results: Motor synergies were successfully extracted on all 28 subjects. Clustering orders ranging from 2 to 50 were tested. A subset of ten clusters, each one represented by a spatial motor module, approximates the original dataset with a mean maximum error of 5% on reconstructed modules; however, each spatial synergy might be employed with different timing and recruited at different grasp stages. Two temporal activation patterns are often recognized, corresponding to the grasp/hold phase, and to the pre-shaping and release phase.

Conclusions: This paper presents one of the biggest analysis of muscle synergies of hand grasps currently available. The results of 28 subjects performing 20 different grasps suggest that a limited number of time dependent motor modules (shared among subjects), correctly elicited by a control activation signal, may underlie the execution of a large variety of hand grasps. However, spatial synergies are not strongly related to specific motor functions but may be recruited at different stages, depending on subject and grasp. This result can lead to applications in rehabilitation and assistive robotics.

A Systematic Review on Muscle Synergies: From Building Blocks of Motor Behavior to a Neurorehabilitation Tool

The central nervous system (CNS) is believed to utilize specific predefined modules, called muscle synergies (MS), to accomplish a motor task. Yet questions persist about how the CNS combines these primitives in different ways to suit the task conditions. The MS hypothesis has been a subject of debate as to whether they originate from neural origins or nonneural constraints. In this review article, we present three aspects related to the MS hypothesis: (1) the experimental and computational evidence in support of the existence of MS, (2) algorithmic approaches for extracting them from surface electromyography (EMG) signals, and (3) the possible role of MS as a neurorehabilitation tool. We note that recent advances in computational neuroscience have utilized the MS hypothesis in motor control and learning. Prospective advances in clinical, medical, and engineering sciences and in fields such as robotics and rehabilitation stand to benefit from a more thorough understanding of MS.

Analyzing finger interdependencies during the Purdue Pegboard Test and comparative activities of daily living

STUDY DESIGN

Bench and cross-sectional study.

INTRODUCTION

Information obtained from dexterity tests is an important component of a comprehensive examination of the hand.

PURPOSE OF THE STUDY

To analyze and compare finger interdependencies during the performance of the Purdue Pegboard Test (PBT) and comparative daily tasks.

METHODS

A method based on the optoelectronic kinematic analysis of the precision grip style and on the calculation of cross-correlation coefficients between relevant joint angles, which provided measures of the degree of finger coordination, was conducted on 10 healthy participants performing the PBT and 2 comparative daily living tasks.

RESULTS

Daily tasks showed identifiable interdependencies patterns between the metacarpophalangeal joints of the fingers involved in the grip. Tasks related to activities of daily living resulted in significantly higher cross-correlation coefficients across subjects and movements during the formation and manipulation phases of the tasks (0.7-0.9), whereas the release stage produced significantly lower movement correlation values (0.3-0.7). Contrarily, the formation and manipulation stages of the PBT showed low finger correlation across most subjects (0.2-0.6), whereas the release stage resulted in the highest values for all relevant movements (0.65-0.9).

DISCUSSION

Interdependencies patterns were consistent for the activities of daily living but differ from the patterns observed from the PBT.

CONCLUSIONS

The PBT does not compare well with the whole range of finger movements that account for hand performance during daily tasks.

LEVEL OF EVIDENCE

Not applicable.

Muscle Synergies-Based Characterization and Clustering of Poststroke Patients in Reaching Movements

Background

A deep characterization of neurological patients is a crucial step for a detailed knowledge of the pathology and maximal exploitation and customization of the rehabilitation therapy. The muscle synergies analysis was designed to investigate how muscles coactivate and how their eliciting commands change in time during movement production. Few studies investigated the value of muscle synergies for the characterization of neurological patients before rehabilitation therapies. In this article, the synergy analysis was used to characterize a group of chronic poststroke hemiplegic patients.

Methods

Twenty-two poststroke patients performed a session composed of a sequence of 3D reaching movements. They were assessed through an instrumental assessment, by recording kinematics and electromyography to extract muscle synergies and their activation commands. Patients’ motor synergies were grouped by the means of cluster analysis. Consistency and characterization of each cluster was assessed and clinically profiled by comparison with standard motor assessments.

Results

Motor synergies were successfully extracted on all 22 patients. Five basic clusters were identified as a trade-off between clustering precision and synthesis power, representing: healthy-like activations, two shoulder compensatory strategies, two elbow predominance patterns. Each cluster was provided with a deep characterization and correlation with clinical scales, range of motion, and smoothness.

Conclusion

The clustering of muscle synergies enabled a pretherapy characterization of patients. Such technique may affect several aspects of the therapy: prediction of outcomes, evaluation of the treatments, customization of doses, and therapies.

Data sample size needed for analysis of kinematic and muscle synergies in healthy and stroke populations

Multiple studies have suggested the central nervous system (CNS) generates motions by using modular control of muscles and joints (synergies). However, the synergies reported by these studies are task dependent and might not reflect the true control strategies adopted by the CNS. Studying exploratory motions (EMs) can reveal biomechanical constraints and motor control strategies in healthy and clinical populations. The first logical step to consider EMs in study of motor synergies is to determine how much data is required to reliably and fully profile the motion patterns of an individual. Here we present how the quality of motor synergies analysis depends on the amount of EM data included in the analysis. We recruited 10 healthy and 10 post-stroke participants and collected electromyography (EMG) and joint motion data of their arms as they completed a motor exploration task. We compared the effects of clinical status and limb strength/dominance on the amount of data required to identify synergies. Clinical status had a significant elïect on the required amount of data for both datasets. Limb strength had a significant effect only for kinematic data. We determined the upper bound 95% confidence interval to set the amount of data required for synergy analysis in both populations: 235 sec for EMG data and 265 sec for kinematic data. Our results provide an important step toward using motor exploration in the study of healthy motor synergies and how stroke alters them.

On identifying kinematic and muscle synergies: a comparison of matrix factorization methods using experimental data from the healthy population

Human motor behavior is highly goal directed, requiring the central nervous system to coordinate different aspects of motion generation to achieve the motion goals. The concept of motor synergies provides an approach to quantify the covariation of joint motions and of muscle activations, i.e., elemental variables, during a task. To analyze goal-directed movements, factorization methods can be used to reduce the high dimensionality of these variables while accounting for much of the variance in large data sets. Three factorization methods considered in this paper are principal component analysis (PCA), nonnegative matrix factorization (NNMF), and independent component analysis (ICA). Bilateral human reaching data sets are used to compare the methods, and advantages of each are presented and discussed. PCA and NNMF had a comparable performance on both EMG and joint motion data and both outperformed ICA. However, NNMF's nonnegativity condition for activation of basis vectors is a useful attribute in identifying physiologically meaningful synergies, making it a more appealing method for future studies. A simulated data set is introduced to clarify the approaches and interpretation of the synergy structures returned by the three factorization methods.

NEW & NOTEWORTHY

Literature on comparing factorization methods in identifying motor synergies using numerically generated, simulation, and muscle activation data from animal studies already exists. We present an empirical evaluation of the performance of three of these methods on muscle activation and joint angles data from human reaching motion: principal component analysis, nonnegative matrix factorization, and independent component analysis. Using numerical simulation, we also studied the meaning and differences in the synergy structures returned by each method. The results can be used to unify approaches in identifying and interpreting motor synergies.

Analysis of effective sit-to-stand therapy using kinematic synergies

Understanding effective sit-to-stand (STS) movement is essential for improving rehabilitation strategies and developing services for the rapidly increasing number of elderly people. This study aims at identifying effective STS therapy by analyzing the kinematic synergies of movements induced by therapists of different skill-levels. Three synergies were found to share the same temporal pattern in both joint angles and center-of-mass spaces across all therapists. Effective strategy used by a skilled therapist and strategy flaws of less-experienced therapists were revealed through comparison of spatial patterns.

Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.

Effect of homogenous object stiffness on tri-digit grasp properties

This paper presents experimental findings on how humans modulate their muscle activity while grasping objects of varying levels of compliance. We hypothesize that one of the key abilities that allows humans to successfully cope with uncertainties while grasping compliant objects is the ability to modulate muscle activity to control both grasp force and stiffness in a way that is coherent with the task. To that end, subjects were recruited to perform a grasp and lift task with a tripod-grasp device with contact surfaces of variable compliance. Subjects performed the task under four different compliance conditions while surface EMG from the main finger flexor and extensor muscles was recorded along with force and torque data at the contact points. Significant increases in the extensor muscle (the antagonist in the task) and co-contraction levels were found with increasing compliance at the contact points. These results suggest that the motor system may employ a strategy of increasing co-contraction, and thereby stiffness, to counteract the decreased stability in grasping compliant objects. Future experiments will examine the extent to which this phenomenon is also related to specific task features, such as precision versus power grasp and object weight.