A 3-D dynamic model of human finger for studying free movements

Contribution of Soft Tissue Passive Forces in Thumb Carpometacarpal Joint Distraction

Thumb carpometacarpal joint space changes when the surrounding soft tissues including the capsule, ligaments, and tendons are stretched or pulled away. When at rest, joint forces originate from passive contraction of muscles and the involvement of joint capsule and ligaments. Previous biomechanical models of hand and finger joints have mostly focused on the assessment of joint properties when muscles were active. This study aims to present an experimental-numerical biomechanical model of thumb carpometacarpal joint to explore the contribution of tendons, ligaments, and other soft tissues in the passive forces during distraction. Five fresh cadaveric specimens were tested using a distractor device to measure the applied forces upon gradual distraction of the intact joint. The subsequent step involved inserting a minuscule sensor into the joint capsule through a small incision, while preserving the integrity of tendons and ligaments, in order to accurately measure the fundamental intra-articular forces. A numerical model was also used to calculate the passive forces of tendons and ligaments. Before the separation of bones, the forces exerted by tendons and ligaments were relatively small compared to the capsule force, which accounted for approximately 92% of the total applied force. Contribution of tendons and ligaments, however, increased by further distraction. The passive force contribution by tendons at 2-mm distraction was determined less than 11%, whereas it reached up to 74% for the ligaments. The present study demonstrated that the ligament-capsule complex plays significant contribution in passive forces of thumb carpometacarpal joint during distraction.

Finger Kinematics during Human Hand Grip and Release

A bionic robotic hand can perform many movements similar to a human hand. However, there is still a significant gap in manipulation between robot and human hands. It is necessary to understand the finger kinematics and motion patterns of human hands to improve the performance of robotic hands. This study aimed to comprehensively investigate normal hand motion patterns by evaluating the kinematics of hand grip and release in healthy individuals. The data corresponding to rapid grip and release were collected from the dominant hands of 22 healthy people by sensory glove. The kinematics of 14 finger joints were analyzed, including the dynamic range of motion (ROM), peak velocity, joint sequence and finger sequence. The results show that the proximal interphalangeal (PIP) joint had a larger dynamic ROM than metacarpophalangeal (MCP) and distal interphalangeal (DIP) joints. Additionally, the PIP joint had the highest peak velocity, both in flexion and extension. For joint sequence, the PIP joint moved prior to the DIP or MCP joints during flexion, while extension started in DIP or MCP joints, followed by the PIP joint. Regarding the finger sequence, the thumb started to move before the four fingers, and stopped moving after the fingers during both grip and release. This study explored the normal motion patterns in hand grip and release, which provided a kinematic reference for the design of robotic hands and thus contributes to its development.

Prediction of anatomically and biomechanically feasible precision grip posture of the human hand based on minimization of muscle effort

We developed a method to estimate a biomechanically feasible precision grip posture of the human hand for a given object based on a minimization of the muscle effort. The hand musculoskeletal model was constructed as a chain of 21 rigid links with 37 intrinsic and extrinsic muscles. To grasp an object, the static force and moment equilibrium condition of the object, force balance between the muscle and fingertip forces, and static frictional conditions must be satisfied. We calculated the hand posture, fingertip forces, and muscle activation signals for a given object to minimize the square sum of the muscle activations while satisfying the above kinetic constraints using an evolutionary optimization technique. To evaluate the estimated hand posture and fingertip forces, a wireless fingertip force-sensing device with two six-axis load cells was developed. When grasping the object, the fingertip forces and hand posture were experimentally measured to compare with the corresponding estimated values. The estimated hand postures and fingertip forces were in reasonable agreement to the corresponding measured data, indicating that the proposed hand posture estimation method based on the minimization of muscle effort is effective for the virtual ergonomic assessment of a handheld product.

Evaluation of the bio-dynamic response of the hand-arm system and hand-tool designs - A brief review

Hand-operated tools transmit a high magnitude of vibration exposure to the hand-arm system that causes occupational diseases. The health effects caused in various countries for the past years due to usage of hand tools are necessary to identify the occupational disorders. Researchers have conducted various studies on biological effects, hand-transmitted vibration exposure and biodynamic responses throughout the years. This article goes over each of these studies in detail, as well as identifying areas where more research is needed. The majority of studies deal with the following topics: general guidelines for hand-transmitted vibrations; assessment techniques of vibration exposure; hand-tool evaluation methods; influence of hand-tool design to overcome the biomechanical effects; and finite element modelling for quantifying vibration exposure. In response to this, understanding the biodynamic behaviour of the hand-arm system is useful for better ergonomic intervention in hand tools to reduce fatigue and increase comfort.

Musculoskeletal Modeling and Inverse Dynamic Analysis of Precision Grip in the Japanese Macaque

Toward clarifying the biomechanics and neural mechanisms underlying coordinated control of the complex hand musculoskeletal system, we constructed an anatomically based musculoskeletal model of the Japanese macaque (Macaca fuscata) hand, and then estimated the muscle force of all the hand muscles during a precision grip task using inverse dynamic calculation. The musculoskeletal model was constructed from a computed tomography scan of one adult male macaque cadaver. The hand skeleton was modeled as a chain of rigid links connected by revolute joints. The path of each muscle was defined as a series of points connected by line segments. Using this anatomical model and a model-based matching technique, we constructed 3D hand kinematics during the precision grip task from five simultaneous video recordings. Specifically, we collected electromyographic and kinematic data from one adult male Japanese macaque during the precision grip task and two sequences of the precision grip task were analyzed based on inverse dynamics. Our estimated muscular force patterns were generally in agreement with simultaneously measured electromyographic data. Direct measurement of muscle activations for all the muscles involved in the precision grip task is not feasible, but the present inverse dynamic approach allows estimation for all the hand muscles. Although some methodological limitations certainly exist, the constructed model analysis framework has potential in clarifying the biomechanics and neural control of manual dexterity in macaques and humans.

Load bearing performance of mechanical joints inspired by elbow of quadrupedal mammals

One of the biggest issues of the mechanical cylindrical joints is related to premature wear appearing. Application of bioinspiration principles in an engineering context taking advantage of smart solutions offered by nature in terms of kinematic joints could be a way of solving those problems. This work is focussed on joints of one degrees of freedom in rotation (revolute or ginglymus joints in biological terms), as this is one of the most common type of mechanical joints. This type of joints can be found in the elbow of some quadrupedal mammals. The articular morphology of the elbow of these animals differs in the presence/absence of a trochlear sulcus. In this study, bio-inspired mechanical joints based on these morphologies (with/without trochlear sulcus) were designed and numerically tested. Their load bearing performance was numerically analysed. This was done through contact simulations using the finite element method under different external loading conditions (axial load, radial load and turnover moment). Results showed that the tested morphologies behave differently in transmission of external mechanical loads. It was found that bio-inspired joints without trochlea sulcus showed to be more specialized in the bearing of turnover moments. Bio-inspired joints with trochlea sulcus are more suitable for supporting combined loads (axial and radial load and turnover moments). Learning about the natural rules of mechanical design can provide new insights to improve the design of current mechanical joints.

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.

Simultaneous Kinematic and Contact Force Modeling of a Human Finger Tendon System Using Bond Graphs and Robotic Validation

This paper aims to use bond graph modeling to create the most comprehensive finger tendon model and simulation to date. Current models are limited to either free motion without external contact or fixed finger force transmission between tendons and fingertip. The forward dynamics model, presented in this work, simultaneously simulates the kinematics of tendon-finger motion and contact forces of a central finger given finger tendon inputs. The model equations derived from bond graphs are accompanied by nonlinear relationships modeling the anatomical complexities of moment arms, tendon slacking, and joint range of motion (ROM). The structure of the model is validated using a robotic testbed, Utah's Anatomically correct Robotic Testbed (UART) finger. Experimental motion of the UART finger during free motion (no external contact) and surface contact are simulated using the bond graph model. The contact forces during the surface contact experiments are also simulated. On average, the model was able to predict the steady-state pose of the finger with joint angle errors less than 6 deg across both free motion and surface contact experiments. The static contact forces were accurately predicted with an average of 11.5% force magnitude error and average direction error of 12 deg.

Mapping Together Kinetic and Kinematic Abilities of the Hand

Millions of people have reduced hand function; this loss of function can be due to injury, disease, or aging. Loss of hand function is identified as reduced motion abilities in the fingers or a decrease in the ability of the fingers to generate force. Unfortunately, there are limited data available regarding each finger's ability to produce force and how those force characteristics vary with changes in finger posture. To relate motion and force abilities of the fingers, first, an approach to measure and map them together is needed. The goal of this work was to develop and demonstrate a method to quantify the force abilities of the fingers and map these forces to the kinematic space associated with each finger. Using motion capture and multiaxis load cells, finger forces were quantified at different positions over their ranges of motion. These two sets of data were then converted to the same coordinate space and mapped together. Further, the data were normalized for the index finger and mapped as a population space model. The ability to quantify motion and force data for each finger and map them together will provide an improved understanding of the effects of treatments and rehabilitation, identifying functional loss due to injury or disease, and device design.

Finite element analysis to assess the biomechanical behavior of a finger model gripping handles with different diameters

Study aim: Interactions between the fingers and a handle can be analyzed using a finite element finger model. Hence, the biomechanical response of a hybrid human finger model during contact with varying diameter cylindrical handles was investigated numerically in the present study using ABAQUS/CAE.

Materials and methods: The finite element index finger model consists of three segments: the proximal, middle, and distal phalanges. The finger model comprises skin, bone, subcutaneous tissue and nail. The skin and subcutaneous tissues were assumed to be non-linearly elastic and linearly visco-elastic. The FE model was applied to predict the contact interaction between the fingers and a handle with 10 N, 20 N, 40 N and 50 N grip forces for four different diameter handles (30 mm, 40 mm, 44mm and 50 mm). The model predictions projected the biomechanical response of the finger during the static gripping analysis with 200 incremental steps.

Results: The simulation results showed that the increase in contact area reduced the maximal compressive stress/strain and also the contact pressure on finger skin. It was hypothesized in this study that the diameter of the handle influences the stress/strain and contact pressure within the soft tissue during the contact interactions.

Conclusions: The present study may be useful to study the behavior of the finger model under the static gripping of hand-held power tools.

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.

Importance of Consistent Datasets in Musculoskeletal Modelling: A Study of the Hand and Wrist

Hand musculoskeletal models provide a valuable insight into the loads withstood by the upper limb; however, their development remains challenging because there are few datasets describing both the musculoskeletal geometry and muscle morphology from the elbow to the finger tips. Clinical imaging, optical motion capture and microscopy were used to create a dataset from a single specimen. Subsequently, a musculoskeletal model of the wrist was developed based on these data to estimate muscle tensions and to demonstrate the potential of the provided parameters. Tendon excursions and moment arms predicted by this model were in agreement with previously reported experimental data. When simulating a flexion–extension motion, muscle forces reached 90 N among extensors and a co-contraction of flexors, amounting to 62.6 N, was estimated by the model. Two alternative musculoskeletal models were also created based on anatomical data available in the literature to illustrate the effect of combining incomplete datasets. Compared to the initial model, the intensities and load sharing of the muscles estimated by the two alternative models differed by up to 180% for a single muscle. This confirms the importance of using a single source of anatomical data when developing such models.

Quantitative evaluation of hand functions using a wearable hand exoskeleton system

To investigate, improve, and observe the effect of rehabilitation therapy, many studies have been conducted on evaluating the motor function quantitatively by developing various types of robotic systems. Even though the robotic systems have been developed, functional evaluation of the hand has been rarely investigated, because it is difficult to install a number of actuators or sensors to the hand due to limited space around the fingers. Therefore, in this study, a hand exoskeleton was developed to satisfy the required specifications for evaluating the hand functions including spasticity of finger flexors, finger independence, and multi-digit synergy and algorithms to evaluate such functions were proposed. The hand exoskeleton was composed with the four 4-bar linkages, two motors, and three loadcells for each finger, and it was able to flex/extend the metacarpal (MCP) and proximal interphalangeal(PIP) joints independently while measuring the pulling force at each phalanx. Using the hand exoskeleton, the hand functions of the three healthy subject were evaluated and the experimental results were analyzed.

Incorporating the length-dependent passive-force generating muscle properties of the extrinsic finger muscles into a wrist and finger biomechanical musculoskeletal model

Dynamic movement trajectories of low mass systems have been shown to be predominantly influenced by passive viscoelastic joint forces and torques compared to momentum and inertia. The hand is comprised of 27smallmass segments. Because of the influence of the extrinsic finger muscles, the passive torques about each finger joint become a complex function dependent on the posture of multiple joints of the distal upper limb. However, biomechanical models implemented for the dynamic simulation of hand movements generally don't extend proximally to include the wrist and distal upper limb. Thus, they cannot accurately represent these complex passive torques. The purpose of this short communication is to both describe a method to incorporate the length-dependent passive properties of the extrinsic index finger muscles into a biomechanical model of the upper limb and to demonstrate their influence on combined movement of the wrist and fingers. Leveraging a unique set of experimental data, that describes the net passive torque contributed by the extrinsic finger muscles about the metacarpophalangeal joint of the index finger as a function of both metacarpophalangeal and wrist postures, we simulated the length-dependent passive properties of the extrinsic finger muscles. Dynamic forward simulations demonstrate that a model including these properties passively exhibits coordinated movement between the wrist and finger joints, mimicking tenodesis, a behavior that is absent when the length-dependent properties are removed. This work emphasizes the importance of incorporating the length-dependent properties of the extrinsic finger muscles into biomechanical models to study healthy and impaired hand movements.

A scaling method to individualise muscle force capacities in musculoskeletal models of the hand and wrist using isometric strength measurements

Because the force-generating capacities of muscles are currently estimated using anatomical data obtained from cadaver specimens, hand musculoskeletal models provide only a limited representation of the specific features of individual subjects. A scaling method is proposed to individualise muscle capacities using dynamometric measurements and electromyography. For each subject, a strength profile was first defined by measuring net moments during eight maximum isometric contractions about the wrist and metacarpophalangeal joints. The capacities of the five muscle groups were then determined by adjusting several parameters of an initial musculoskeletal model using an optimisation procedure which minimised the differences between measured moments and model estimates. Sixteen volunteers, including three particular participants (one climber, one boxer and one arthritic patient), were recruited. Compared with the initial literature-based model, the estimated subject-specific capacities were on average five times higher for the wrist muscles and twice as high for the finger muscles. The adjustments for particular subjects were consistent with their expected specific characteristics, e.g. high finger flexor capacities for the climber. Using the subject-specific capacities, the model estimates were markedly modified. The proposed protocol and scaling procedure can capture the specific characteristics of the participants and improved the representation of their capacities in the musculoskeletal model.

The biomechanical model of the long finger extensor mechanism and its parametric identification

The extensor mechanism of the finger is a structure transmitting the forces from several muscles to the finger joints. Force transmission in the extensor mechanism is usually modeled by equations with constant coefficients which are determined experimentally only for finger extension posture. However, the coefficient values change with finger flexion because of the extensor mechanism deformation. This induces inaccurate results for any other finger postures. We proposed a biomechanical model of the extensor mechanism represented as elastic strings. The model includes the main tendons and ligaments. The parametric identification of the model in extension posture was performed to match the distribution of the forces among the tendons to experimental data. The parametrized model was used to simulate three degrees of flexion. Furthermore, the ability of the model to reproduce how the force distribution in simulated extensor mechanism changes according to the muscle forces was also demonstrated. The proposed model could be used to simulate the extensor mechanism for any physiological finger posture for which the coefficients involved in the equations are unknown.

Hand Posture Prediction Using Neural Networks within a Biomechanical Model

This paper proposes the use of artificial neural networks (ANNs) in the framework of a biomechanical hand model for grasping. ANNs enhance the model capabilities as they substitute estimated data for the experimental inputs required by the grasping algorithm used. These inputs are the tentative grasping posture and the most open posture during grasping. As a consequence, more realistic grasping postures are predicted by the grasping algorithm, along with the contact information required by the dynamic biomechanical model (contact points and normals). Several neural network architectures are tested and compared in terms of prediction errors, leading to encouraging results. The performance of the overall proposal is also shown through simulation, where a grasping experiment is replicated and compared to the real grasping data collected by a data glove device.

Modeling of the interaction between grip force and vibration transmissibility of a finger

It is known that the vibration characteristics of the fingers and hand and the level of grip action interacts when operating a power tool. In the current study, we developed a hybrid finger model to simulate the vibrations of the hand-finger system when gripping a vibrating handle covered with soft materials. The hybrid finger model combines the characteristics of conventional finite element (FE) models, multi-body musculoskeletal models, and lumped mass models. The distal, middle, and proximal finger segments were constructed using FE models, the finger segments were connected via three flexible joint linkages (i.e., distal interphalangeal joint (DIP), proximal interphalangeal joint (PIP), and metacarpophalangeal (MCP) joint), and the MCP joint was connected to the ground and handle via lumped parameter elements. The effects of the active muscle forces were accounted for via the joint moments. The bone, nail, and hard connective tissues were assumed to be linearly elastic whereas the soft tissues, which include the skin and subcutaneous tissues, were considered as hyperelastic and viscoelastic. The general trends of the model predictions agree well with the previous experimental measurements in that the resonant frequency increased from proximal to the middle and to the distal finger segments for the same grip force, that the resonant frequency tends to increase with increasing grip force for the same finger segment, especially for the distal segment, and that the magnitude of vibration transmissibility tends to increase with increasing grip force, especially for the proximal segment. The advantage of the proposed model over the traditional vibration models is that it can predict the local vibration behavior of the finger to a tissue level, while taking into account the effects of the active musculoskeletal force, the effects of the contact conditions on vibrations, the global vibration characteristics.

Using subject-specific three-dimensional (3D) anthropometry data in digital human modelling: case study in hand motion simulation

Digital human modelling enables ergonomists and designers to consider ergonomic concerns and design alternatives in a timely and cost-efficient manner in the early stages of design. However, the reliability of the simulation could be limited due to the percentile-based approach used in constructing the digital human model. To enhance the accuracy of the size and shape of the models, we proposed a framework to generate digital human models using three-dimensional (3D) anthropometric data. The 3D scan data from specific subjects' hands were segmented based on the estimated centres of rotation. The segments were then driven in forward kinematics to perform several functional postures. The constructed hand models were then verified, thereby validating the feasibility of the framework. The proposed framework helps generate accurate subject-specific digital human models, which can be utilised to guide product design and workspace arrangement. Practitioner Summary: Subject-specific digital human models can be constructed under the proposed framework based on three-dimensional (3D) anthropometry. This approach enables more reliable digital human simulation to guide product design and workspace arrangement.

Assessing Finger Joint Biomechanics by Applying Equal Force to Flexor Tendons In Vitro Using a Novel Simultaneous Approach

Background

The flexor digitorum superficialis (FDS) and flexor digitorum profundus (FDP) are critical for finger flexion. Although research has recently focused on these tendons’ coactivity, their contributions in different tasks remain unclear. This study created a novel simultaneous approach to investigate the coactivity between the tendons and to clarify their contributions in different tasks.

Methods

Ten human cadaveric hands were mounted on our custom frame with the FDS and FDP of the third finger looped through a mechanical pulley connected to a force transducer. Joint range of motion, tendon excursion and loading force were recorded during individual joint motion and free joint movement from rest to maximal flexion. Each flexor tendon’s moment arm was then calculated.

Results

In individual motions, we found that the FDP contributed more than the FDS in proximal interphalangeal (PIP) joint motion, with an overall slope of 1.34 and all FDP-to-FDS excursion (P/S) ratios greater than 1.0 with force increase. However, the FDP contributed less than the FDS in metacarpophalangeal (MCP) joint motion, with an overall slope of 0.95 and P/S ratios smaller than 1.0 throughout the whole motion except between 1.9% and 13.1% force. In free joint movement, the FDP played a greater role than the FDS, with an overall ratio of 1.37 and all P/S ratios greater than 1.0.

Conclusions

The new findings include differences in finger performance and excursion amounts between the FDS and FDP throughout flexion. Such findings may provide the basis for new hand models and treatments.

Assessment of Workspace Attributes Under Simulated Index Finger Proximal Interphalangeal Arthrodesis

This article presented an assessment of quantitative measures of workspace (WS) attributes under simulated proximal interphalangeal (PIP) joint arthrodesis of the index finger. Seven healthy subjects were tested with the PIP joint unconstrained (UC) and constrained to selected angles using a motion analysis system. A model of the constrained finger was developed in order to address the impact of the inclusion of prescribed joint arthrodesis angles on WS attributes. Model parameters were obtained from system identification experiments involving flexion-extension (FE) movements of the UC and constrained finger. The data of experimental FE movements of the constrained finger were used to generate the two-dimensional (2D) WS boundaries and to validate the model. A weighted criterion was formulated to define an optimal constraint angle among several system parameters. Results indicated that a PIP joint immobilization angle of 40-50 deg of flexion maximized the 2D WS. The analysis of the aspect ratio of the 2D WS indicated that the WS was more evenly distributed as the imposed PIP joint constraint angle increased. With the imposed PIP joint constraint angles of 30 deg, 40 deg, 50 deg, and 60 deg of flexion, the normalized maximum distance of fingertip reach was reduced by approximately 3%, 4%, 7%, and 9%, respectively.

Proposition, identification, and experimental evaluation of an inverse dynamic neuromusculoskeletal model for the human finger

PURPOSE

The purpose of this study is to develop an inverse dynamic model of the human middle finger in order to identify the muscle activation, muscle force, and neural activation of the muscles involved during motion. Its originality comes from the coupling of biomechanical and physiological models and the proposition of a dedicated optimization procedure and cost function for identifying the model unknowns.

METHODS

Three sub-models work in interaction: the first is the biomechanical model, primarily consisting of the dynamic equations of the middle finger system; the second is the muscle model, which helps to identify the muscle force from muscle activation and dynamic deformation for six involved muscles. The third model allows one to link muscle activation to neural intent from the Central Nervous System (CNS). This modeling procedure leads to a complex analytical nonlinear system identified using multi-step energy minimization procedure and a specific cost function.

RESULTS

Numerical simulations with different articulation velocities are presented and discussed. Then, experimental evaluation of the proposed model is performed following a protocol combining electromyography and motion capture during a hand opening-closing paradigm. After comparison, several results from the simulation and experiments were found to be in accordance. The difficulty in evaluating such complex dynamic models is also demonstrated.

CONCLUSIONS

Despite the model simplifications, the obtained preliminary results are promising. Indeed, the proposed model, once correctly validated in future works, should be a relevant tool to simulate and predict deficiencies of the middle finger system for rehabilitation purposes.

Finger muscle attachments for an OpenSim upper-extremity model

We determined muscle attachment points for the index, middle, ring and little fingers in an OpenSim upper-extremity model. Attachment points were selected to match both experimentally measured locations and mechanical function (moment arms). Although experimental measurements of finger muscle attachments have been made, models differ from specimens in many respects such as bone segment ratio, joint kinematics and coordinate system. Likewise, moment arms are not available for all intrinsic finger muscles. Therefore, it was necessary to scale and translate muscle attachments from one experimental or model environment to another while preserving mechanical function. We used a two-step process. First, we estimated muscle function by calculating moment arms for all intrinsic and extrinsic muscles using the partial velocity method. Second, optimization using Simulated Annealing and Hooke-Jeeves algorithms found muscle-tendon paths that minimized root mean square (RMS) differences between experimental and modeled moment arms. The partial velocity method resulted in variance accounted for (VAF) between measured and calculated moment arms of 75.5% on average (range from 48.5% to 99.5%) for intrinsic and extrinsic index finger muscles where measured data were available. RMS error between experimental and optimized values was within one standard deviation (S.D) of measured moment arm (mean RMS error = 1.5 mm < measured S.D = 2.5 mm). Validation of both steps of the technique allowed for estimation of muscle attachment points for muscles whose moment arms have not been measured. Differences between modeled and experimentally measured muscle attachments, averaged over all finger joints, were less than 4.9 mm (within 7.1% of the average length of the muscle-tendon paths). The resulting non-proprietary musculoskeletal model of the human fingers could be useful for many applications, including better understanding of complex multi-touch and gestural movements.

Orthopaedic applications of a validated force-based biomechanical model of the index finger

An anatomically realistic biomechanical model of the index finger was created using a force-based approach in order to predict the isometric fingertip force or dynamic movement based on the forces of 7 index finger musculotendons. The model was validated for static forces through comparison with experimental results from 5 cadaver specimens. The model reliably simulated the isometric fingertip force produced by loading individual tendons. The average error in fingertip force direction was less than 2° and the average error in magnitude was less than 10% across finger postures for each muscle. Subsequent employment of the model to examine force transmission from the long flexors revealed a strong dependence of joint contact force on finger posture for a given tendon load. This may have ramifications for osteoarthritis as high joint contact forces are thought to contribute to the disease.

Ergonomic evaluation of biomechanical hand function

The human hand is a complex structure that performs various functions for activities of daily living and occupations. This paper presents a literature review on the methodologies used to evaluate hand functions from a biomechanics standpoint, including anthropometry, kinematics, kinetics, and electromyography (EMG). Anthropometry describes the dimensions and measurements of the hand. Kinematics includes hand movements and the range of motion of finger joints. Kinetics includes hand models for tendon and joint force analysis. EMG is used on hand muscles associated with hand functions and with signal-processing technology.

AssistOn-Finger: An under-actuated finger exoskeleton for robot-assisted tendon therapy

We present AssistOn-Finger, a novel under-actuated active exoskeleton for robot-assisted tendon therapy of human fingers. The primary use for the exoskeleton is to assist flexion/extension motions of a finger within its full range, while decreasing voluntary muscle contractions helping to keep the tendon tension levels to stay within acceptable limits, avoiding gap formation or rupture of the suture. The device can also be employed to administer range of motion (RoM)/strengthening exercises. AssistOn-Fingeris designed to be passively back-driveable, can cover the whole RoM of patients, and can do so in a natural and coordinated manner. In particular, the device employs human finger as an integral part of its kinematics and when coupled to a human operator, the parallel kinematic structure of exoskeleton supports three independent degrees of freedom, dictated by the kinematics of the human finger. Automatically aligning its joint axes to match finger joint axes, AssistOn-Fingercan guarantee ergonomy and comfort throughout the therapy. The self-aligning feature also significantly shortens the setup time required to attach the patient to the exoskeleton. We present the kinematic type selection for the exoskeleton to satisfy the design requirements for tendon therapy applications, detail optimal dimensional synthesis of the device considering trade-offs between multiple design criteria and discuss implementation details of the exoskeleton. We also present feasibility studies conducted on healthy volunteers and provide statistical evidence on the efficacy of exoskeleton driven exercises in keeping the average muscle recruitment and the maximum tendon tension levels as low as human guided therapies.

Biomechanical analysis of force distribution in human finger extensor mechanisms

The complexities of the function and structure of human fingers have long been recognised. The in vivo forces in the human finger tendon network during different activities are critical information for clinical diagnosis, surgical treatment, prosthetic finger design, and biomimetic hand development. In this study, we propose a novel method for in vivo force estimation for the finger tendon network by combining a three-dimensional motion analysis technique and a novel biomechanical tendon network model. The extensor mechanism of a human index finger is represented by an interconnected tendinous network moving around the phalanx's dorsum. A novel analytical approach based on the "Principle of Minimum Total Potential Energy" is used to calculate the forces and deformations throughout the tendon network of the extensor mechanism when subjected to an external load and with the finger posture defined by measurement data. The predicted deformations and forces in the tendon network are in broad agreement with the results obtained by previous experimental in vitro studies. The proposed methodology provides a promising tool for investigating the biomechanical function of complex interconnected tendon networks in vivo.

Biomechanical analysis of the human finger extensor mechanism during isometric pressing

This study investigated the effects of the finger extensor mechanism on the bone-to-bone contact forces at the interphalangeal and metacarpal joints and also on the forces in the intrinsic and extrinsic muscles during finger pressing. This was done with finger postures ranging from very flexed to fully extended. The role of the finger extensor mechanism was investigated by using two alternative finger models, one which omitted the extensor mechanism and another which included it. A six-camera three-dimensional motion analysis system was used to capture the finger posture during maximum voluntary isometric pressing. The fingertip loads were recorded simultaneously using a force plate system. Two three-dimensional biomechanical finger models, a minimal model without extensor mechanism and a full model with extensor mechanism (tendon network), were used to calculate the joint bone-to-bone contact forces and the extrinsic and intrinsic muscle forces. If the full model is assumed to be realistic, then the results suggest some useful biomechanical advantages provided by the tendon network of the extensor mechanism. It was found that the forces in the intrinsic muscles (interosseus group and lumbrical) are significantly reduced by 22% to 61% due to the action of the extensor mechanism, with the greatest reductions in more flexed postures. The bone-to-bone contact force at the MCP joint is reduced by 10% to 41%. This suggests that the extensor mechanism may help to reduce the risk of injury at the finger joints and also to moderate the forces in intrinsic muscles. These apparent biomechanical advantages may be a result of the extensor mechanism's distinctive interconnected fibrous structure, through which the contraction of the intrinsic muscles as flexors of the MCP joint can generate extensions at the DIP and PIP joints.

Analysis of the effects of surface stiffness on the contact interaction between a finger and a cylindrical handle using a three-dimensional hybrid model

Contact interactions between the hand and handle, such as the contact surface softness and contact surface curvature, will affect both physical effort and musculoskeletal fatigue, thereby the comfort and safety of power tool operations. Previous models of hand gripping can be categorized into two groups: multi-body dynamic models and finite element (FE) models. The goal of the current study is to develop a hybrid FE hand gripping model, which combines the features of conventional FE models and multi-body dynamic models. The proposed model is applied to simulate hand-gripping on a cylindrical handle with covering materials of different softness levels. The model included three finger segments (distal, middle, and proximal phalanxes), three finger joints (the distal interphalangeal (DIP), proximal interphalangeal (PIP), and metacarpophalangeal (MCP) joint), and major anatomical substructures. The model was driven by joint moments, which are the net effects of all passive and active muscular forces acting about the joints. The finger model was first calibrated by using experimental data of human subject tests, and then applied to investigate the effects of surface softness on contact interactions between a finger and a cylindrical handle. Our results show that the maximal compressive stress and strain in the soft tissues of the fingers can be effectively reduced by reducing the stiffness of the covering material.

Development of a tendon-driven robotic finger for an anthropomorphic robotic hand

Our paper proposes a tendon-driven robotic finger based on an anatomical model of a human finger and a suitable method for its analysis. Our study aims to realize an anthropomorphic robotic hand that has the same characteristics and dexterity as that of a human hand, and it also aims to identify the advantages of the human musculoskeletal structure for application to the design and control of robot manipulators. When designing an anthropomorphic robotic hand, several devices are required to apply the human finger structure to a tendon-driven robotic finger. Reasons for this include that one of the human finger muscles, namely, the lumbrical muscle, is situated between tendons, which is an unfavorable configuration for the tendon-driven mechanism. Second, unlike a standard pulley used in a tendon-driven mechanism, some moment arms of the human finger change nonlinearly according to the joint angle. In our robotic finger design, we address these difficulties by rearranging its tendons and develop a mechanism to change the moment arm. We also propose a method to analyze and control this robotic fingers coordinating joints using non-stretch branching tendons based on the human extensor mechanism with a virtual tendon Jacobian matrix and the advantage is that this constraint virtually reduces the degrees-of-freedom (DOF) of the mechanism. Further, we build a prototype to confirm its motion using this method. In addition, we show that the state with the reduced DOF can be lost by external forces acting on the mechanism, and this condition can be changed manually by adjusting the tendon forces. This makes it possible to control the virtual DOFs to satisfy the requirements of the task. Finally, we discuss the benefits from anthropomorphic structures including the tendon arrangement, which mimic the human lumbrical muscle, and the above mentioned mechanism with non-linear moment arms from the perspective that there are two states of DOFs. These insights may provide new perspectives in the design of robotic hands.

Quantification of finger joint loadings using musculoskeletal modelling clarifies mechanical risk factors of hand osteoarthritis

Owing to limited quantitative data related to the loadings (forces and pressures) acting upon finger joints, several clinical observations regarding mechanical risk factors of hand osteoarthritis remain misunderstood. To improve the knowledge of this pathology, the present study used musculoskeletal modelling to quantify the forces and pressures acting upon hand joints during two grasping tasks. Kinematic and grip force data were recorded during both a pinch and a power grip tasks. Three-dimensional magnetic resonance imaging measurements were conducted to quantify joint contact areas. Using these datasets as input, a musculoskeletal model of the hand and wrist, including twenty-three degrees of freedom and forty-two muscles, has been developed to estimate joint forces and joint pressures. When compared with the power grip task, the pinch grip task resulted in two to eight times higher joint loadings whereas the grip forces exerted on each finger were twice lower. For both tasks, joint forces and pressures increased along a disto-proximal direction for each finger. The quantitative dataset provided by the present hand model clarified two clinical observations about osteoarthritis development which were not fully understood, i.e., the strong risk associated to pinch grip tasks and the high frequency of thumb-base osteoarthritis.

Wrist posture affects hand and forearm muscle stress during tapping

Non-neutral wrist posture is a risk factor of the musculoskeletal disorders among computer users. This study aimed to assess internal loads on hand and forearm musculature while tapping in different wrist postures. Ten healthy subjects tapped on a key switch using their index finger in four wrist postures: straight, ulnar deviated, flexed and extended. Torque at the finger and wrist joints were calculated from measured joint postures and fingertip force. Muscle stresses of the six finger muscles and four wrist muscles that balanced the calculated joint torques were estimated using a musculoskeletal model and optimization algorithm minimizing the squared sum of muscle stress. Non-neutral wrist postures resulted in greater muscle stresses than the neutral (straight) wrist posture, and the stress in the extensor muscles were greater than the flexors in all conditions. Wrist extensors stress remained higher than 4.5 N/cm² and wrist flexor stress remained below 0.5 N/cm² during tapping. The sustained high motor unit recruitment of extensors suggests a greater risk than other muscles especially in flexed wrist posture. This study demonstrated from the perspective of internal tissue loading the importance of maintaining neutral wrist posture during keying activities.

Quantifying catch-and-release: the extensor tendon force needed to overcome the catching flexors in trigger fingers

The extensor tendon forces required to overcome the catching flexors in trigger fingers are unknown. A biomechanical model with moment equilibrium equations and method of least squares was developed for estimating the tendon force at triggering in trigger fingers. Trigger fingers that exhibited significant catching and sudden release during finger extension were tested. A customized "pulling tester" was used to pull the finger from flexion to extension and provide synchronic measurement of the pulling force. The displacement of the tested finger was measured by a motion capture system. This preliminary study presents kinematic and kinetic data at triggering of 10 trigger fingers. The distal and proximal interphalangeal (PIP) joints presented sudden release while the metacarpophalangeal (MCP) joint started extension in the early phase of finger extension. The tendon tension of flexor digitorum profundus was greater than that of flexor digitorum superficialis (FDS) in six fingers, and less than that of FDS in three fingers. The tension of two flexor tendons was almost equal in one finger. At the PIP and MCP joints, 1.54 times the force of flexors was needed for the extensors to overcome the catching flexors in trigger fingers. This biomechanical model provides clinicians with a clearer idea of the tendon force at triggering. The quantitative results may help in the understanding of movement characteristics of trigger fingers. These findings are useful to better understand the etiology and nature of trigger finger development, and thus aid in further development of better assessments and treatments related to this.

Carpe diem, Carpe ampulla: a numerical model as an aid to the design of child-resistant closures

The population of most developed countries is ageing. Despite continuing medical advances, ageing brings with it a host of issues, not least a loss in strength and dexterity. One major area of concern is the ability of elderly consumers to access packaged goods such as food and medicines. In previous studies, the authors developed a numerical model of a human hand that was used to investigate the effect of physical dimensions and choice of grip on joint stresses to aid the understanding between physical effort, ability and discomfort. This previous work was supported by ethnographic studies and led to recommendations for packaging design. In this paper, a small ethnographic study is undertaken which identifies the grip types used to access to a product that is known to cause particular difficulties for the elderly, the "squeeze and turn" child-resistant closure or CRC, commonly used on medicines and cleaning products. One of the grip types used was chosen to be modelled using the numerical model developed in previous studies by the author. Model geometry and loading were adapted to simulate the "squeeze and turn" nature of the initial opening for closures of this type. A series of studies were then undertaken using different hand geometries; an average male hand, an average female hand and a fifth percentile female hand. The prediction from the model here is that female users with smaller hands will experience more discomfort when accessing squeeze and turn CRC's and that the turn process whilst maintaining the squeeze is problematic.

Human hand modelling: kinematics, dynamics, applications

An overview of mathematical modelling of the human hand is given. We consider hand models from a specific background: rather than studying hands for surgical or similar goals, we target at providing a set of tools with which human grasping and manipulation capabilities can be studied, and hand functionality can be described. We do this by investigating the human hand at various levels: (1) at the level of kinematics, focussing on the movement of the bones of the hand, not taking corresponding forces into account; (2) at the musculotendon structure, i.e. by looking at the part of the hand generating the forces and thus inducing the motion; and (3) at the combination of the two, resulting in hand dynamics as well as the underlying neurocontrol. Our purpose is to not only provide the reader with an overview of current human hand modelling approaches but also to fill the gaps with recent results and data, thus allowing for an encompassing picture.

Sensitivity of a biomechanical model of the finger to errors in experimental input

Biomechanical models of the fingers are used to gain a greater understanding of internal loading. This can help guide the treatment of injuries and pathologies. However, to be valid these models require accurate measurement of body kinematics, external reaction forces and soft tissue architecture. This study aimed to quantify the sensitivity of one such model, to errors in these inputs. Experimental data was collected from a single subject carrying out a simple gripping activity and the experimental data altered to introduce artificial errors. We found that the correlations between errors in measurement of body kinematics and the model outputs could be used to express errors in motion capture data in terms of internal loading. However, these correlations were specific to grip type, therefore, if the grip changed significantly a new analysis would be required. Sensitivity analysis of the muscle and tendon locations indicated which parameters were most important to measure accurately; outputs were most sensitive to changes in the most highly loaded muscle-tendon units, these results were applicable across different hand orientations.

Motor control theories improve biomechanical model of the hand for finger pressing tasks

BACKGROUND

Biomechanical models are a useful tool to estimate tendon tensions. Unfortunately, in previous fingers' models, each finger acts independently from the others. This is contradictory with hand motor control theories which show that fingers are functionally linked in order to balance the wrist/forearm joint with minimal tendon tensions. (i.e. principle of minimization of the secondary moments). We propose to adapt a hand biomechanical model according to this principle by including the wrist joint. We will determine whether the finger tendon tensions changed with the wrist joint added to the model.

METHODS

Two models have been tested: one considering fingers independently (model A) and one with the fingers mechanically linked by the inclusion of the wrist balance (model B). A single set of data, additional results from the literature and in-vivo values have been used to compare the results.

RESULTS

Model A corroborates previous results in the literature. Contrast results were obtained with model B, especially for the Ring and Little fingers. Different tendon tensions were obtained, particularly, in finger extensor muscles critical to balance the wrist.

DISCUSSION

We discuss the biomechanical results in accordance with the hand/finger motor control theories. It appears that the wrist joint balance is critical for finger tendon tension estimation. When including the wrist joint into finger models, the tendon tension estimations agree well with the minimization of secondary moments and the force deficit.

Reverse engineering finger extensor apparatus morphology from measured coupled interphalangeal joint angle trajectories - a generic 2D kinematic model

The interphalangeal (IP) finger joints coordinate as a mechanism when the deep flexor is active. This mechanism is created by the complex finger extensor apparatus (EA) - a confluence of end tendons of one or two extensors, radial and ulnar interossei, and lumbrical - which inserts as a single structure into both the middle and distal phalanges. Although the IP-coupling principle was well demonstrated more than half a century ago, the detailed relationship between EA morphology and IP coupling remains not well described. Main reasons are that by dissection the EA's fiber network loses functional consistency, while fibers becoming taut or slack beyond measuring resolutions complicate measuring functional fiber motions. To circumvent these difficulties, we present a two dimensional kinematic multi tendon-string EA model of fiber slackness and tautness through IP motion, including the retinacular and oblique retinacular EA ligaments. The model parameters were the strings' lengths and attachment points. The model's functional redundancies were resolved by individually interactively fitting model IP trajectories to previously measured IP trajectories of 68 fingers. All model trajectories accurately fitted their target IP trajectories for proximal interphalangeal (PIP) joint ranges smaller than 25° to 45°; about half accurately fitted over the entire IP range with the remaining half having maximum approximation errors between 3° to 12°, while all models again converged to target trajectories for full IP flexion. These accuracies suggest the model reflects real functional EA principles, with potential applications in biomechanical modeling, surgical reconstruction, rehabilitation, and prosthetic EA replacements.

Effect of Object Width on Muscle and Joint Forces During Thumb–Index Finger Grasping

The objective of this study was to identify the impact of modifying the object width on muscle and joint forces while gripping objects. The experimental protocol consisted to maintain horizontally five objects of different widths (3.5, 4.5, 5.5, 6.5, and 7.5 cm) with a thumb–index finger grip. Subjects were required to grasp spontaneously the object without any instruction regarding the grip force (GF) to apply. A biomechanical model of thumb–index finger pinch was developed to estimate muscle and joint forces. This model included electromyography, fingertip force, and kinematics data as inputs. The finger joint postures and the GF varied across the object widths. The estimated muscle forces also varied significantly according to the object width. Interestingly, we observed that the muscle force/GF ratios of major flexor muscles remain particularly stable with respect to the width whereas other muscle ratios differed largely. This may argue for a control strategy in which the actions of flexors were preserved in spite of change in joint postures. The estimated joint forces tended to increase with object width and increased in the distal–proximal sense. Overall, these results are of importance for the ergonomic design of handheld objects and for clinical applications.

A probabilistic finger biodynamic model better depicts the roles of the flexors during unloaded flexion

Previous deterministic finger biomechanical models predicted that the flexor digitorum superficialis (FDS) was silent and the flexor digitorum profundus (FDP) was the only active flexor during finger flexion. Experimental studies in vivo, however, recorded activities of both flexors. In this study, in an attempt to elucidate the roles of the flexors, a probabilistic biodynamic model of the index finger was constructed to estimate the muscle-tendon forces during an experimentally measured index finger flexion movement. A Monte-Carlo simulation was performed with four model parameters, including moment arms, physiological cross sectional areas (PCSA), passive torques, and anthropometric measures as independent random variables. The muscle-tendon forces at each time point were determined using a nonlinear optimization technique. The model predicted that both FDS and FDP contributed to sustaining the movement and the FDS was not necessarily silent. The two distinct force patterns observed in vivo in experimental studies were also corroborated by the simulation. These findings, contrary to previous deterministic models' predictions but in agreement with experimental measurements, explained the observed coactivation of FDS and FDP, and resolved the controversy regarding the roles of the flexors in finger movement dynamics.