Biodynamic modeling, system identification, and variability of multi-finger movements

Parameter estimation of a model describing the human fingers

The goal of this paper is twofold: firstly, to provide a novel mathematical model that describes the kinematic chain of motion of the human fingers based on Lagrangian mechanics with four degrees of freedom and secondly, to estimate the model parameters using data from able-bodied individuals. In the literature there are a variety of mathematical models that have been developed to describe the motion of the human finger. These models offer little to no information on the underlying mechanisms or corresponding equations of motion. Furthermore, these models do not provide information as to how they scale with different anthropometries. The data used here is generated using an experimental procedure that considers the free response motion of each finger segment with data captured via a motion capture system. The angular data collected are then filtered and fitted to a linear second-order differential approximation of the equations of motion. The results of the study show that the free response motion of the segments is underdamped across flexion/extension and ad/abduction.

A hypothetical neural network model for generation of human precision grip

Humans can stably hold and skillfully manipulate an object by coordinated control of a complex, redundant musculoskeletal system. However, how the human central nervous system actually accomplishes precision grip tasks by coordinated control of fingertip forces remains unclear. In the present study, we aimed to construct a hypothetical neural network model that can spontaneously generate humanlike precision grip. The nervous system was modeled as a recurrent neural network model prescribing kinematic and kinetic constraints that must be satisfied in precision grip tasks in the form of energy functions. The recurrent neural network autonomously behaves so as to decrease the energy functions; therefore, given the estimated mass and center-of-mass location of the target object, the nervous system model can spontaneously generate muscle activation signals that achieve stable precision grips due to dynamic relaxation of the energy functions embedded in the nervous system. Fingertip forces are modulated by sensory information about slip between the object and fingertips. A two-dimensional musculoskeletal model of the human hand with a thumb and an index finger was constructed. Forward dynamic simulation of the precision grip was performed using the proposed neural network model. Our results demonstrated that the proposed neural network model could stably pinch and successfully hold up the object in various conditions, including changes in friction, object shape, object mass, and center-of-mass location. The proposed hypothetical neuro-computational model may possibly explain some aspects of the control strategy humans use for precision grip.

Development of a dynamic index finger and thumb model to study impairment

Modeling of the human hand provides insight for explaining deficits and planning treatment following injury. Creation of a dynamic model, however, is complicated by the actions of multi-articular tendons and their complex interactions with other soft tissues in the hand. This study explores the creation of a musculoskeletal model, including the thumb and index finger, to explore the effects of muscle activation deficits. The OpenSim model utilizes physiological axes of rotation at all joints, passive joint torques, and appropriate moment arms. The model was validated through comparison with kinematic and kinetic experimental data. Simulated fingertip forces resulting from modeled musculotendon loading largely fell within one standard deviation of experimental ranges for most index finger and thumb muscles, although agreement in the sagittal plane was generally better than for the coronal plane. Input of experimentally obtained electromyography data produced the expected simulated finger and thumb motion. Use of the model to predict the effects of activation deficits on pinch force production revealed that the intrinsic muscles, especially first dorsal interosseous (FDI) and adductor pollicis (ADP), had a substantial impact on the resulting fingertip force. Reducing FDI activation, such as might occur following stroke, altered fingertip force direction by up to 83° for production of a dorsal fingertip force; reducing ADP activation reduced force production in the thumb by up to 62%. This validated model can provide a means for evaluating clinical interventions.

Modeling of multiarticular muscles: importance of inclusion of tendon-pulley interactions in the finger

The purpose of this study was to examine force transmission from one of the major multiarticular muscles of the finger, flexor digitorum profundus (FDP), to the index finger. Specifically, we examined whether the popular moment arm (MA)-joint torque technique of modeling muscle force transmission can accurately represent the effects of the FDP on finger movement. A dynamic finger model employing geometric MA values (model I) was compared with another model including realistic tendon force transformation mechanisms via pulley structures and joint reaction forces (model II). Finger flexion movements generated by these models were compared with those obtained from in vivo stimulation experiments. The model with the force transformation mechanisms (model II) resulted in more realistic joint spatial coordination (i.e., proximal interphalangeal > metacarpophalangeal > or = distal interphalangeal) than the MA-based model (model I) in relation to the movement patterns evoked by stimulation. Also, the importance of the pulley structures and passive joint characteristics was confirmed in the model simulation; altering/eliminating these components significantly changed the spatial coordination of the joint angles during the resulting movements. The results of this study emphasize the functional importance of the force transformation through various biomechanical components, and suggest the importance of including these components when investigating finger motor control, such as for examining injury mechanisms or designing rehabilitation protocols.

A novel two-stage framework for musculoskeletal dynamic modeling: an application to multifingered hand movement

In this paper, we present a new computational framework for biodynamic modeling of human movement. The framework decouples the conventional dynamic modeling process into two stages: in the first stage, two-component "agonist-antagonist" torque actuators under hypothesized and testable parametric control drive the forward dynamics, and parameters are identified by tracking both kinematics and kinetics; the second stage completes the mapping from the muscle-tendon forces to the predicted joint torques. An empirical test using multifinger grasping movement data was conducted to illustrate the application of the proposed framework and showed that the model reproduced the measurement accurately in both kinematics and kinetics. The torque components exhibited consistent spatial-temporal patterns across joints, digits, and subjects. The muscle-tendon forces computed based on the model-predicted kinematics and kinetics had the peak values within the same order of magnitude as in vivo data reported in the literature. The potential to predict was also demonstrated as we applied the control parameters of one subject to another and achieved close matches.

Development and validation of a biodynamic model for predicting multi-finger movements in cylinder-grasping tasks

This article describes the development and validation of a model for predicting multi-finger movements in grasping activities. The model builds upon a newly proposed approach that incorporates forward dynamics and a system identification procedure, and is amenable to empirical tests. A database of multi-fingered grasping movements performed by 28 subjects was established and divided into four sets, one for model development and three for model validation. In the development phase, model parameter values were estimated by the iterative system identification procedure through a physics-based heuristic algorithm. The estimated parameter values were then statistically synthesised and integrated into the prediction model. In the validation phase, the model was applied to three novel datasets containing different grasping movements involving objects of varied sizes and different subjects. The results demonstrated the model's ability to predict hand prehensile movements with error magnitudes comparable to the inter-person variability in performing such movements. New insights into the control of multi-fingered hand prehensile movements at the systems and joint levels emerged from the model development and validation process. The current study contributes to building a foundation for long-term development of realistic biodynamic simulation of multi-finger hand movements. Such simulation capabilities will aid in design of hand-operated tools, devices or hand-intensive work for proactive ergonomics and in evaluation as well as treatment of functional impairment of the hand.