The effect of the extensor mechanism on maximum isometric fingertip forces: A numerical study on the index finger

Muscle redundancy is greatly reduced by the spatiotemporal nature of neuromuscular control

Animals must control numerous muscles to produce forces and movements with their limbs. Current theories of motor optimization and synergistic control are predicated on the assumption that there are multiple highly diverse feasible activations for any motor task ("muscle redundancy"). Here, we demonstrate that the dimensionality of the neuromuscular control problem is greatly reduced when adding the temporal constraints inherent to any sequence of motor commands: the physiological time constants for muscle activation-contraction dynamics. We used a seven-muscle model of a human finger to fully characterize the seven-dimensional polytope of all possible motor commands that can produce fingertip force vector in any direction in 3D, in alignment with the core models of Feasibility Theory. For a given sequence of seven force vectors lasting 300 ms, a novel single-step extended linear program finds the 49-dimensional polytope of all possible motor commands that can produce the sequence of forces. We find that muscle redundancy is severely reduced when the temporal limits on muscle activation-contraction dynamics are added. For example, allowing a generous ± 12% change in muscle activation within 50 ms allows visiting only ∼ 7% of the feasible activation space in the next time step. By considering that every motor command conditions future commands, we find that the motor-control landscape is much more highly structured and spatially constrained than previously recognized. We discuss how this challenges traditional computational and conceptual theories of motor control and neurorehabilitation for which muscle redundancy is a foundational assumption.

A Musculoskeletal Model of the Hand and Wrist Capable of Simulating Functional Tasks

OBJECTIVE

The purpose of this work was to develop an open-source musculoskeletal model of the hand and wrist and to evaluate its performance during simulations of functional tasks.

METHODS

The current model was developed by adapting and expanding upon existing models. An optimal control theory framework that combines forward-dynamics simulations with a simulated-annealing optimization was used to simulate maximum grip and pinch force. Active and passive hand opening were simulated to evaluate coordinated kinematic hand movements.

RESULTS

The model's maximum grip force production matched experimental measures of grip force, force distribution amongst the digits, and displayed sensitivity to wrist flexion. Simulated lateral pinch strength replicated in vivo palmar pinch strength data. Additionally, predicted activations for 7 of 8 muscles fell within variability of EMG data during palmar pinch. The active and passive hand opening simulations predicted reasonable activations and demonstrated passive motion mimicking tenodesis, respectively.

CONCLUSION

This work advances simulation capabilities of hand and wrist models and provides a foundation for future work to build upon.

SIGNIFICANCE

This is the first open-source musculoskeletal model of the hand and wrist to be implemented during both functional kinetic and kinematic tasks. We provide a novel simulation framework to predict maximal grip and pinch force which can be used to evaluate how potential surgical and rehabilitation interventions influence these functional outcomes while requiring minimal experimental data.

Analysis on synergistic cocontraction of extrinsic finger flexors and extensors during flexion movements: A finite element digital human hand model

Fine hand movements require the synergistic contraction of intrinsic and extrinsic muscles to achieve them. In this paper, a Finite Element Digital Human Hand Model (FE-DHHM) containing solid tendons and ligaments and driven by the Muscle-Tendon Junction (MTJ) displacements of FDS, FDP and ED measured by ultrasound imaging was developed. The synergistic contraction of these muscles during the finger flexion movements was analyzed by simulating five sets of finger flexion movements. The results showed that the FDS and FDP contracted together to provide power during the flexion movements, while the ED acted as an antagonist. The peak stresses of the FDS, FDP and ED were all at the joints. In the flexion without resistance, the FDS provided the main driving force, and the FDS and FDP alternated in a "plateau" of muscle force. In the flexion with resistance, the muscle forces of FDS, FDP, and ED were all positively correlated with fingertip forces. The FDS still provided the main driving force, but the stress maxima occurred in the FDP at the DIP joint.

Risk of tendon failure with repeated passage of Kirschner wires or hypodermic needles: an experimental study

During hand surgery, tendons may be at risk of damage. This biomechanical study aims to assess the risk of tendon rupture due to passage of Kirschner wires or hypodermic needles. Porcine extensor tendons were divided into four groups. Group 1: a control group was tested to ensure that repeated stress alone did not cause failure. Group 2a: 1.1-mm Kirschner wires were hand pushed through tendons 50 times and then stressed to 40 N, repeated until tendon failure. In Group 2b, K-wires were passed while rotating using a drill. Group 3: the experiment was repeated using a 20 G hypodermic needle. Group 2a tendons required a median of 2450 passes (1150-3500) to propagate failure, Group 2b a median of 2250 (1200-3850) and Group 3a median of 200 passes (150-450). The risk of tendon rupture from wires or hypodermic needles in procedures appears very low.

Learning Grasp Configuration Through Object-Specific Hand Primitives for Posture Planning of Anthropomorphic Hands

The proposal of postural synergy theory has provided a new approach to solve the problem of controlling anthropomorphic hands with multiple degrees of freedom. However, generating the grasp configuration for new tasks in this context remains challenging. This study proposes a method to learn grasp configuration according to the shape of the object by using postural synergy theory. By referring to past research, an experimental paradigm is first designed that enables the grasping of 50 typical objects in grasping and operational tasks. The angles of the finger joints of 10 subjects were then recorded when performing these tasks. Following this, four hand primitives were extracted by using principal component analysis, and a low-dimensional synergy subspace was established. The problem of planning the trajectories of the joints was thus transformed into that of determining the synergy input for trajectory planning in low-dimensional space. The average synergy inputs for the trajectories of each task were obtained through the Gaussian mixture regression, and several Gaussian processes were trained to infer the inputs trajectories of a given shape descriptor for similar tasks. Finally, the feasibility of the proposed method was verified by simulations involving the generation of grasp configurations for a prosthetic hand control. The error in the reconstructed posture was compared with those obtained by using postural synergies in past work. The results show that the proposed method can realize movements similar to those of the human hand during grasping actions, and its range of use can be extended from simple grasping tasks to complex operational tasks.

Estimation of joint contact pressure in the index finger using a hybrid finite element musculoskeletal approach

The knowledge of local stress distribution in hand joints is crucial to understand injuries and osteoarthritis occurrence. However, determining cartilage contact stresses remains a challenge, requiring numerical models including both accurate anatomical components and realistic tendon force actuation. Contact forces in finger joints have frequently been calculated but little data is available on joint contact pressures. This study aimed to develop and assess a hybrid biomechanical model of the index finger to estimate in-vivo joint contact pressure during a static maximal strength pinch grip task. A finite element model including bones, cartilage, tendons, and ligaments was developed, with tendon force transmission based on a tendon-pulley system. This model was driven by realistic tendon forces estimated from a musculoskeletal model and motion capture data for six subjects. The hybrid model outputs agreed well with the experimental measurement of fingertip forces and literature data on the physiological distribution of tendon forces through the index finger. Mean contact pressures were 6.9 ± 2.7 MPa, 6.2 ± 1.0 MPa and 7.2 ± 1.3 MPa for distal, proximal interphalangeal and metacarpophalangeal joints, respectively. Two subjects had higher mean contact pressure in the distal joint than in the other two joints, suggesting a mechanical cause for the prevalence of osteoarthritis in the index distal joint. The inter-subject variability in joint contact pressure could be explained by different neuromuscular strategies employed for the task. This first application of an effective hybrid model to the index finger is promising for estimating hand joint stresses under daily grip tasks and simulating surgical procedures.

Metacarpophalangeal joint loads during bonobo locomotion: model predictions versus proxies

The analysis of internal trabecular and cortical bone has been an informative tool for drawing inferences about behaviour in extant and fossil primate taxa. Within the hand, metacarpal bone architecture has been shown to correlate well with primate locomotion; however, the extent of morphological differences across taxa is unexpectedly small given the variability in hand use. One explanation for this observation is that the activity-related differences in the joint loads acting on the bone are simply smaller than estimated based on commonly used proxies (i.e. external loading and joint posture), which neglect the influence of muscle forces. In this study, experimental data and a musculoskeletal finger model are used to test this hypothesis by comparing differences between climbing and knuckle-walking locomotion of captive bonobos (Pan paniscus) based on (i) joint load magnitude and direction predicted by the models and (ii) proxy estimations. The results showed that the activity-related differences in predicted joint loads are indeed much smaller than the proxies would suggest, with joint load magnitudes being almost identical between the two locomotor modes. Differences in joint load directions were smaller but still evident, indicating that joint load directions might be a more robust indicator of variation in hand use than joint load magnitudes. Overall, this study emphasizes the importance of including muscular forces in the interpretation of skeletal remains and promotes the use of musculoskeletal models for correct functional interpretations.

Musculoskeletal models of a human and bonobo finger: parameter identification and comparison to in vitro experiments

Introduction

Knowledge of internal finger loading during human and non-human primate activities such as tool use or knuckle-walking has become increasingly important to reconstruct the behaviour of fossil hominins based on bone morphology. Musculoskeletal models have proven useful for predicting these internal loads during human activities, but load predictions for non-human primate activities are missing due to a lack of suitable finger models. The main goal of this study was to implement both a human and a representative non-human primate finger model to facilitate comparative studies on metacarpal bone loading. To ensure that the model predictions are sufficiently accurate, the specific goals were: (1) to identify species-specific model parameters based on in vitro measured fingertip forces resulting from single tendon loading and (2) to evaluate the model accuracy of predicted fingertip forces and net metacarpal bone loading in a different loading scenario.

Materials & Methods

Three human and one bonobo (Pan paniscus) fingers were tested in vitro using a previously developed experimental setup. The cadaveric fingers were positioned in four static postures and load was applied by attaching weights to the tendons of the finger muscles. For parameter identification, fingertip forces were measured by loading each tendon individually in each posture. For the evaluation of model accuracy, the extrinsic flexor muscles were loaded simultaneously and both the fingertip force and net metacarpal bone force were measured. The finger models were implemented using custom Python scripts. Initial parameters were taken from literature for the human model and own dissection data for the bonobo model. Optimized model parameters were identified by minimizing the error between predicted and experimentally measured fingertip forces. Fingertip forces and net metacarpal bone loading in the combined loading scenario were predicted using the optimized models and the remaining error with respect to the experimental data was evaluated.

Results

The parameter identification procedure led to minor model adjustments but considerably reduced the error in the predicted fingertip forces (root mean square error reduced from 0.53/0.69 N to 0.11/0.20 N for the human/bonobo model). Both models remained physiologically plausible after the parameter identification. In the combined loading scenario, fingertip and net metacarpal forces were predicted with average directional errors below 6° and magnitude errors below 12%.

Conclusions

This study presents the first attempt to implement both a human and non-human primate finger model for comparative palaeoanthropological studies. The good agreement between predicted and experimental forces involving the action of extrinsic flexors—which are most relevant for forceful grasping—shows that the models are likely sufficiently accurate for comparisons of internal loads occurring during human and non-human primate manual activities.

Feasibility Theory Reconciles and Informs Alternative Approaches to Neuromuscular Control

We present Feasibility Theory, a conceptual and computational framework to unify today's theories of neuromuscular control. We begin by describing how the musculoskeletal anatomy of the limb, the need to control individual tendons, and the physics of a motor task uniquely specify the family of all valid muscle activations that accomplish it (its ‘feasible activation space’). For our example of producing static force with a finger driven by seven muscles, computational geometry characterizes—in a complete way—the structure of feasible activation spaces as 3-dimensional polytopes embedded in 7-D. The feasible activation space for a given task is the landscape where all neuromuscular learning, control, and performance must occur. This approach unifies current theories of neuromuscular control because the structure of feasible activation spaces can be separately approximated as either low-dimensional basis functions (synergies), high-dimensional joint probability distributions (Bayesian priors), or fitness landscapes (to optimize cost functions).