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

BackgroundBiomechanical 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.MethodsTwo 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.ResultsModel 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.DiscussionWe 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.     

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.     

Estimation of the effective static moment arms of the tendons in the index finger extensor mechanism

A novel technique to estimate the contribution of finger extensor tendons to joint moment generation was proposed. Effective static moment arms (ESMAs), which represent the net effects of the tendon force on joint moments in static finger postures, were estimated for the 4 degrees of freedom (DOFs) in the index finger. Specifically, the ESMAs for the five tendons contributing to the finger extensor apparatus were estimated by directly correlating the applied tendon force to the measured resultant joint moments in cadaveric hand specimens. Repeated measures analysis of variance revealed that the finger posture, specifically interphalangeal joint angles, had significant effects on the measured ESMA values in 7 out of 20 conditions (four DOFs for each of the five muscles). Extensor digitorum communis and extensor indicis proprius tendons were found to have greater MCP ESMA values when IP joints are flexed, whereas abduction ESMAs of all muscles except extensor digitorum profundus were mainly affected by MCP flexion. The ESMAs were generally smaller than the moment arms estimated in previous studies that employed kinematic measurement techniques. Tendon force distribution within the extensor hood and dissipation into adjacent structures are believed to contribute to the joint moment reductions, which result in smaller ESMA values.     

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 27 small mass 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.     

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.     

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.     

Muscle moment arm and normalized moment contributions as reference data for musculoskeletal elbow and wrist joint models

A geometric musculoskeletal model of the elbow and wrist joints was developed to calculate muscle moment arms throughout elbow flexion/extension, forearm pronation/supination, wrist flexion/extension and radial/ulnar deviation. Model moment arms were verified with data from cadaver specimen studies and geometric models available in the literature. Coefficients of polynomial equations were calculated for all moment arms as functions of joint angle, with special consideration to coupled muscles as a function of two joint angles. Additionally, a “normalized potential moment (NPM)” contribution index for each muscle across the elbow and wrist joints in four degrees-of-freedom was determined using each muscle's normalized physiological cross-sectional area (PCSA) and peak moment arm (MA). We hypothesize that (a) a geometric model of the elbow and wrist joints can represent the major attributes of MA versus joint angle from many literature sources of cadaver and model data and (b) an index can represent each muscle's normalized moment contribution to each degree-of-freedom at the elbow and wrist. We believe these data serve as a simple, yet comprehensive, reference for how the primary 16 muscles across the elbow and wrist contribute to joint moment and overall joint performance.     

Using EMG data to constrain optimization procedure improves finger tendon tension estimations during static fingertip force production

Determining tendon tensions of the finger muscles is crucial for the understanding and the rehabilitation of hand pathologies. Since no direct measurement is possible for a large number of finger muscle tendons, biomechanical modelling presents an alternative solution to indirectly evaluate these forces. However, the main problem is that the number of muscles spanning a joint exceeds the number of degrees of freedom of the joint resulting in mathematical under-determinate problems. In the current study, a method using both numerical optimization and the intra-muscular electromyography (EMG) data was developed to estimate the middle finger tendon tensions during static fingertip force production. The method used a numerical optimization procedure with the muscle stress squared criterion to determine a solution while the EMG data of three extrinsic hand muscles serve to enforce additional inequality constraints. The results were compared with those obtained with a classical numerical optimization and a method based on EMG only. The proposed method provides satisfactory results since the tendon tension estimations respected the mechanical equilibrium of the musculoskeletal system and were concordant with the EMG distribution pattern of the subjects. These results were not observed neither with the classical numerical optimization nor with the EMG-based method. This study demonstrates that including the EMG data of the three extrinsic muscles of the middle finger as inequality constraints in an optimization process can yield relevant tendon tensions with regard to individual muscle activation patterns, particularly concerning the antagonist muscles.     

Sensitivity of model predictions of muscle function to changes in moment arms and muscle-tendon properties: a Monte-Carlo analysis

Hill-type muscle models are commonly used in musculoskeletal models to estimate muscle forces during human movement. However, the sensitivity of model predictions of muscle function to changes in muscle moment arms and muscle-tendon properties is not well understood. In the present study, a three-dimensional muscle-actuated model of the body was used to evaluate the sensitivity of the function of the major lower limb muscles in accelerating the whole-body center of mass during gait. Monte-Carlo analyses were used to quantify the effects of entire distributions of perturbations in the moment arms and architectural properties of muscles. In most cases, varying the moment arm and architectural properties of a muscle affected the torque generated by that muscle about the joint(s) it spanned as well as the torques generated by adjacent muscles. Muscle function was most sensitive to changes in tendon slack length and least sensitive to changes in muscle moment arm. However, the sensitivity of muscle function to changes in moment arms and architectural properties was highly muscle-specific; muscle function was most sensitive in the cases of gastrocnemius and rectus femoris and insensitive in the cases of hamstrings and the medial sub-region of gluteus maximus. The sensitivity of a muscle's function was influenced by the magnitude of the muscle's force as well as the operating region of the muscle on its force-length curve. These findings have implications for the development of subject-specific models of the human musculoskeletal system.     

Musculoskeletal model of the upper limb based on the visible human male dataset

A mathematical model of the human upper limb was developed based on high-resolution medical images of the muscles and bones obtained from the Visible Human Male (VHM) project. Three-dimensional surfaces of the muscles and bones were reconstructed from Computed Tomography (CT) images and Color Cryosection images obtained from the VHM cadaver. Thirteen degrees of freedom were used to describe the orientations of seven bones in the model: clavicle, scapula, humerus, radius, ulna, carpal bones, and hand. All of the major articulations from the shoulder girdle down to the wrist were included in the model. The model was actuated by 42 muscle bundles, which represented the actions of 26 muscle groups in the upper limb. The paths of the muscles were modeled using a new approach called the Obstacle-set Method [33]. The calculated paths of the muscles were verified by comparing the muscle moment arms computed in the model with the results of anatomical studies reported in the literature. In-vivo measurements of maximum isometric muscle torques developed at the shoulder, elbow, and wrist were also used to estimate the architectural properties of each musculotendon actuator in the model. The entire musculoskeletal model can be reconstructed using the data given in this paper, along with information presented in a companion paper which defines the kinematic structure of the model [26].     

Finger joint coordination during tapping

We investigated finger joint coordination during tapping by characterizing joint kinematics and torques in terms of muscle activation patterns and energy profiles. Six subjects tapped with their index finger on a computer keyswitch as if they were typing on the middle row of a keyboard. Fingertip force, keyswitch position, kinematics of the metacarpophalangeal (MCP) and the proximal and distal interphalangeal (IP) joints, and intramuscular electromyography of intrinsic and extrinsic finger muscles were measured simultaneously. Finger joint torques were calculated based on a closed-form Newton–Euler inverse dynamic model of the finger. During the keystroke, the MCP joint flexed and the IP joints extended before and throughout the loading phase of the contact period, creating a closing reciprocal motion of the finger joints. As the finger lifted, the MCP joint extended and the interphalangeal (IP) joints flexed, creating an opening reciprocal motion. Intrinsic finger muscle and extrinsic flexor activities both began after the initiation of the downward finger movement. The intrinsic finger muscle activity preceded both the IP joint extension and the onset of extrinsic muscle activity. Only extrinsic extensor activity was present as the finger was lifted. While both potential energy and kinetic energy are present and large enough to overcome the work necessary to press the keyswitch, the motor control strategies utilize the muscle forces and joint torques to ensure a successful keystroke.     

Analysis of the musculoskeletal loading of the thumb during pipetting – A pilot study

Previous epidemiological studies indicate that the use of thumb-push mechanical pipettes is associated with musculoskeletal disorders (MSDs) in the hand. The goal of the current study was to analyze the loading in the muscle–tendon units in the thumb during pipetting. The hand is modeled as a multi-body linkage system and includes four fingers (index, long, ring, and little finger), a thumb, and a palm segment. Since the current study is focused on the thumb, the model includes only nine muscles attached to the thumb via tendons. The time-histories of joint angles and push force at the pipette plunger during pipetting were determined experimentally and used as model input; whereas forces in the muscle–tendon units in the thumb were calculated via an inverse dynamic approach combined with an optimization procedure. Results indicate that all nine muscles have force outputs during pipetting, and the maximal force was in the abductor pollicis brevis (APB). The ratio of the mean peak muscle force to the mean peak push force during the dispensing cycle was approximately 2.3, which is comparable to values observed in grasping tasks in the literature. The analysis method and results in the current study provide a mechanistic understanding of MSD risk factors associated with pipetting, and may be useful in guiding ergonomic designs for manual pipettes.     

Biarticular hip extensor and knee flexor muscle moment arms of the feline hindlimb

Moment arms are important for understanding muscular behavior and for calculating internal muscle forces in musculoskeletal simulations. Biarticular muscles cross two joints and have moment arms that depend on the angle of both joints the muscles cross. The tendon excursion method was used to measure the joint angle-dependence of hamstring (biceps femoris, semimembranosus and semitendinosus) moment arm magnitudes of the feline hindlimb at the knee and hip joints. Knee angle influenced hamstring moment arm magnitudes at the hip joint; compared to a flexed knee joint, the moment arm for semimembranosus posterior at the hip was at most 7.4 mm (25%) larger when the knee was extended. On average, hamstring moment arms at the hip increased by 4.9 mm when the knee was more extended. In contrast, moment arm magnitudes at the knee varied by less than 2.8 mm (mean=1.6 mm) for all hamstring muscles at the two hip joint angles tested. Thus, hamstring moment arms at the hip were dependent on knee position, while hamstring moment arms at the knee were not as strongly associated with relative hip position. Additionally, the feline hamstring muscle group had a larger mechanical advantage at the hip than at the knee joint.     

Wrist and digital joint motion produce unique flexor tendon force and excursion in the canine forelimb

The force and excursion within the canine digital flexor tendons were measured during passive joint manipulations that simulate those used during rehabilitation after flexor tendon repair and during active muscle contraction, simulating the active rehabilitation protocol. Tendon force was measured using a small buckle placed upon the tendon while excursion was measured using a suture marker and video analysis method. Passive finger motion imposed with the wrist flexed resulted in dramatically lower tendon force (approximately 5 N) compared to passive motion imposed with the wrist extended (approximately 17 N). Lower excursions were seen at the level of the proximal interphalangeal joint with the wrist flexed (approximately 1.5 mm) while high excursion was observed when the wrist was extended or when synergistic finger and wrist motion were imposed (approximately 3.5 mm). Bivariate discriminant analysis of both force and excursion data revealed a natural clustering of the data into three general mechanical paradigms. With the wrist extended and with either one finger or four fingers manipulated, tendons experienced high loads of approximately 1500 g and high excursions of approximately 3.5 mm. In contrast, the same manipulations performed with the wrist flexed resulted in low tendon forces (4-8 N) and low tendon excursions of approximately 1.5 mm. Synergistic wrist and finger manipulation provided the third paradigm where tendon force was relatively low (approximately 4 N) but excursion was as high as those seen in the groups which were manipulated with the wrist extended. Active muscle contraction produced a modest tendon excursion (approximately 1 mm) and high or low tendon force with the wrist extended or flexed, respectively. These data provide the basis for experimentally testable hypotheses with regard to the factors that most significantly affect functional recovery after digital flexor tendon injury and define the normal mechanical operating characteristics of these tendons.     

Modelling tendon excursions and moment arms of the finger flexors: anatomic fidelity versus function

A detailed musculoskeletal model of the human hand is needed to investigate the pathomechanics of tendon disorders and carpal tunnel syndrome. The purpose of this study was to develop a biomechanical model with realistic flexor tendon excursions and moment arms. An existing upper extremity model served as a starting point, which included programmed movement of the index finger. Movement capabilities were added for the other fingers. Metacarpophalangeal articulations were modelled as universal joints to simulate flexion/extension and abduction/adduction while interphalangeal articulations used hinges to represent flexion. Flexor tendon paths were modelled using two approaches. The first method constrained tendons with control points, representing annular pulleys. The second technique used wrap objects at the joints as tendon constraints. Both control point and joint wrap models were iteratively adjusted to coincide with tendon excursions and moment arms from a anthropometric regression model using inputs for a 50th percentile male. Tendon excursions from the joint wrap method best matched the regression model even though anatomic features of the tendon paths were not preserved (absolute differences: mean<0.33 mm, peak<0.74 mm). The joint wrap model also produced similar moment arms to the regression (absolute differences: mean<0.63 mm, peak<1.58 mm). When a scaling algorithm was used to test anthropometrics, the scaled joint wrap models better matched the regression than the scaled control point models. Detailed patient-specific anatomical data will improve model outcomes for clinical use; however, population studies may benefit from simplified geometry, especially with anthropometric scaling.     

Development of an anatomically based whole‐body musculoskeletal model of the Japanese macaque (Macaca fuscata)

We constructed a three‐dimensional whole‐body musculoskeletal model of the Japanese macaque (Macaca fuscata) based on computed tomography and dissection of a cadaver. The skeleton was modeled as a chain of 20 bone segments connected by joints. Joint centers and rotational axes were estimated by joint morphology based on joint surface approximation using a quadric function. The path of each muscle was defined by a line segment connecting origin to insertion through an intermediary point if necessary. Mass and fascicle length of each were systematically recorded to calculate physiological cross‐sectional area to estimate the capacity of each muscle to generate force. Using this anatomically accurate model, muscle moment arms and force vectors generated by individual limb muscles at the foot and hand were calculated to computationally predict muscle functions. Furthermore, three‐dimensional whole‐body musculoskeletal kinematics of the Japanese macaque was reconstructed from ordinary video sequences based on this model and a model‐based matching technique. The results showed that the proposed model can successfully reconstruct and visualize anatomically reasonable, natural musculoskeletal motion of the Japanese macaque during quadrupedal/bipedal locomotion, demonstrating the validity and efficacy of the constructed musculoskeletal model. The present biologically relevant model may serve as a useful tool for comprehensive understanding of the design principles of the musculoskeletal system and the control mechanisms for locomotion in the Japanese macaque and other primates. Am J Phys Anthropol, 2009. © 2008 Wiley‐Liss, Inc.     

Musculoskeletal Model of the Upper Limb Based on the Visible Human Male Dataset

A mathematical model of Ihe human upper limb was developed based on high-resolution medical images of the muscles and bones obtained from the Visible Human Male ( HM) project. Three-dimensional surfaces of the muscles and bones were reconstructed from Computed Tomography (CT) images and Color Cryosection images obtained from the VHM cadaver. Thirteen degrees of freedom were used to describe the orientations of seven bones in the model: clavicle, scapula, humerus, radius, ulna, carpal bones, and hand. All of the major articulations from the shoulder girdle down to the wrist were included in the model. The model was actuated by 42 muscle bundles, which represented the actions of 26 muscle groups in the upper limb. The paths of the muscles were modeled using a new approach called the Obstacle-set Method (33) The calculated paths of the muscles were verified by comparing the muscle moment arms computed in the model with the results of anatomical studies reported in the literature, In-vivo measurements of maximum isometric muscle torques developed at the shoulder, elbow, and wrist were also used to estimate the architectural properties of each musculotendon actuator in the model. The entire musculoskeletal model can be reconstructed using the data given in this paper, along with information presented in a companion paper which defines the kinematic structure of the model (26)     

An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo

This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle-tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and low-pass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R(2) of 0.91 and mean residual error of approximately 12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during movement tasks.     

The contribution of the wrist, elbow and shoulder joints to single-finger tapping

We aimed to determine the role of the wrist, elbow and shoulder joints to single-finger tapping. Six human subjects tapped with their index finger at a rate of 3 taps/s on a keyswitch across five conditions, one freestyle (FS) and four instructed tapping strategies. The four instructed conditions were to tap on a keyswitch using the finger joint only (FO), the wrist joint only (WO), the elbow joint only (EO), and the shoulder joint only (SO). A single-axis force plate measured the fingertip force. An infra-red active-marker three-dimensional motion analysis system measured the movement of the fingertip, hand, forearm, upper arm and trunk. Inverse dynamics estimated joint torques for the metacarpal-phalangeal (MCP), wrist, elbow, and shoulder joints. For FS tapping 27%, 56%, and 18% of the vertical fingertip movement were a result of flexion of the MCP joint and wrist joint and extension of the elbow joint, respectively. During the FS movements the net joint powers between the MCP, wrist and elbow were positively correlated (correlation coefficients between 0.46 and 0.76) suggesting synergistic efforts. For the instructed tapping strategies (FO, WO, EO, and SO), correlations decreased to values below 0.35 suggesting relatively independent control of the different joints. For FS tapping, the kinematic and kinetic data indicate that the wrist and elbow contribute significantly, working in synergy with the finger joints to create the fingertip tapping task.     

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

The purpose of this work is to develop a 3D inverse dynamic model of the human finger for estimating the muscular forces involved during free finger movements. A review of the existing 3D models of the fingers is presented, and an alternative one is proposed. The validity of the model has been proved by means of two simulations: free flexion-extension motion of all joints, and free metacarpophalangeal (MCP) adduction motion. The simulation shows the need for a dynamic model including inertial effects when studying fast movements and the relevance of modelling passive forces generated by the structures studying free movements, such as the force exerted by the muscles when they are stretched and the passive action of the ligaments over the MCP joint in order to reproduce the muscular force pattern during the simulation of the free MCP abduction-adduction movements.