Development of a mathematical model for predicting electrically elicited quadriceps femoris muscle forces during isovelocity knee joint motion
- Ramu Perumal^{1}Email author,
- Anthony S Wexler^{2} and
- Stuart A Binder-Macleod^{1}
https://doi.org/10.1186/1743-0003-5-33
© Perumal et al; licensee BioMed Central Ltd. 2008
Received: 12 December 2007
Accepted: 10 December 2008
Published: 10 December 2008
Abstract
Background
Direct electrical activation of skeletal muscles of patients with upper motor neuron lesions can restore functional movements, such as standing or walking. Because responses to electrical stimulation are highly nonlinear and time varying, accurate control of muscles to produce functional movements is very difficult. Accurate and predictive mathematical models can facilitate the design of stimulation patterns and control strategies that will produce the desired force and motion. In the present study, we build upon our previous isometric model to capture the effects of constant angular velocity on the forces produced during electrically elicited concentric contractions of healthy human quadriceps femoris muscle. Modelling the isovelocity condition is important because it will enable us to understand how our model behaves under the relatively simple condition of constant velocity and will enable us to better understand the interactions of muscle length, limb velocity, and stimulation pattern on the force produced by the muscle.
Methods
An additional term was introduced into our previous isometric model to predict the force responses during constant velocity limb motion. Ten healthy subjects were recruited for the study. Using a KinCom dynamometer, isometric and isovelocity force data were collected from the human quadriceps femoris muscle in response to a wide range of stimulation frequencies and patterns. % error, linear regression trend lines, and paired t-tests were used to test how well the model predicted the experimental forces. In addition, sensitivity analysis was performed using Fourier Amplitude Sensitivity Test to obtain a measure of the sensitivity of our model's output to changes in model parameters.
Results
Percentage RMS errors between modelled and experimental forces determined for each subject at each stimulation pattern and velocity showed that the errors were in general less than 20%. The coefficients of determination between the measured and predicted forces show that the model accounted for ~86% and ~85% of the variances in the measured force-time integrals and peak forces, respectively.
Conclusion
The range of predictive abilities of the isovelocity model in response to changes in muscle length, velocity, and stimulation frequency for each individual make it ideal for dynamic applications like FES cycling.
Keywords
Introduction
Phenomenological Hill-type [5–10], Huxley-type cross-bridge[11, 12], or analytical approaches [13, 14] have been developed to explore different aspects of muscle contraction under both isometric and non-isometric conditions. However, each of these models either: (a) could not predict the force or motion response to a range of stimulation frequencies and patterns, (b) have a large number of free parameters that make the model identification process difficult, (c) were not tested for intact human muscles, and (d) were evaluated only under isometric conditions.
Previously, our laboratory developed isometric models for rat gastrocnemius and soleus muscles that addressed the first two shortcomings outlined above. We then extended and modified these models for human quadriceps muscles under isometric fatigue and non-fatigue conditions [15–19]. Recently, comparisons of different isometric force models to fit and predict isometric forces in response to range of stimulation trains showed that our isometric model performed better than the linear models and had similar performance when compared to Bobet-Stein's model [20, 21]. Hence, for the present study, we build upon our isometric models to capture the effects of constant angular velocity (isovelocity) of the lower limb on the forces produced in response to electrical stimulation of the quadriceps femoris muscle. Modeling the isovelocity condition is important because it enables us to understand how our model behaves under the relatively simple condition of constant velocity before trying to model the more complicated non-isometric conditions, where limb velocities change as function of time. More importantly, the current model would enable us to better understand the interactions of muscle length, limb velocity, and stimulation pattern on the force produced by the muscle. This would, in turn, enable us to design stimulation patterns for FES. Hence the purposes of this study are to derive the equations to model the effect of velocity and stimulation on the muscle force under isovelocity conditions and determine if the model can capture the variations in force as a function of velocity when the muscle is activated with a range of stimulation frequencies, patterns, muscle lengths, and number of pulses.
Methods
Model development
The isovelocity model is based on the Hill-type isometric force model developed by our laboratory [5, 16, 17, 22]. This isometric model is used because it is the only model that can predict forces in response to a wide range of stimulation frequencies and because the parameters in the model have a physiological basis, which make it less phenomenological than other Hill-type models. Our model divides the contractile responses of the muscle are decomposed into two distinct physiological steps: activation dynamics and the force dynamics. In addition, we developed the equations of motion for the lower limb moving at constant velocities.
Activation dynamics
where
R_{ i }= 1 for i = 1
R_{ i }= 1 + (R_{0} - 1)exp[-(t_{ i }- t_{i-1})/τ_{ c }] for i > 1.
In Eqns. (1) and (2), t (ms) is the time since the beginning of the stimulation train, t_{ i }(ms) is the time of the i th stimulation pulse since the beginning of the stimulation train, n is the number of stimulation pulses before time t in the train, and τ_{ c }(ms) is the time constant controlling the transient shape of C_{ N }. R_{ i }(unitless) is the scaling term that accounts for the difference in the degree of activation by each pulse relative to the first pulse in the train [24]. The enhancement of R_{ i }is characterized by R_{0} (unitless) and its dynamics is characterized by τ_{ c }(ms). R_{ i }decays with interpulse interval t_{ i }-t_{i-1}. Hence, R_{ i }= 1 for a pulse that occurs at a long time after the preceding pulse, and R_{ i }approaches R_{0} for the smallest interpulse interval tested, 5 ms.
Force dynamics
When calcium binds to troponin, the inhibitory effect of tropomyosin is removed and results in the exposure of binding sites on actin. The crossbridges attach to actin and pull the actin filaments toward the center of the myosin filaments. The macroscopic result of this process is the shortening of the muscle and the generation of force. Force generation is modeled by a Hill-type representation of the skeletal muscle as shown in Fig. 1. Here the skeletal muscle is modeled as a spring (with stiffness k_{ S }), a damper (with a damping coefficient b), and a motor (with velocity V). The series spring represents the tendonous portion and the series elastic component of the muscle [25], the damper represents the viscous resistance of the contractile and connective tissue [26], and the motor represents the contractile component or the sliding of actin and myosin filaments of muscle fibers [19]. The series spring is assumed linear and the force exerted by the spring is given by
>F = k_{ s }x,
where k_{ S }is the spring constant or stiffness and x is the displacement of the spring under the force F.
where b is the damping coefficient, y is the distance moved by the right hand side of the damper in Fig. 1, and $\frac{d{C}_{N}}{dt}=\frac{1}{{\tau}_{c}}{\displaystyle \sum _{i=1}^{n}{R}_{i}\mathrm{exp}(\frac{t-{t}_{i}}{{\tau}_{c}})-\frac{{C}_{N}}{{\tau}_{c}}},$ is the relative velocity of the damper.
where B is the constant of proportionality and K_{ M }mathematically represents the sensitivity of strongly bound cross-bridges to Ca^{2+}-troponin complex [22].
As it is experimentally difficult to measure z and its derivative with respect to time, z is viewed as a function of the knee flexion angle θ. Thus, z is written as z = g(θ).
When $\dot{\theta}$ = 0, the above equation reduces to the isometric form explored in previous studies [16, 17, 22]. By assuming only A to be a function of the knee flexion angle θ, and by fixing other parameter at their 40° knee flexion angle values, the isometric form of the model is able to capture changes in force with muscle length. A was found to vary in a parabolic manner and was modeled as
A(θ) = a(40 - θ)^{2} + b(40 - θ) + A_{40},
where A_{40} is the value of A at 40° of knee flexion, and a and b are constants that need to be identified for each subject [18]. Hence, A captures the effect of muscle length on the force due to stimulation and the model is able to predict the force response to a wide variety of stimulation frequencies.
It is necessary to identify the functional form of G(θ) to model the variation of force with velocity. As seen from Eqn. (9), G(θ) is dependent on an unknown function g(θ) and k_{ S }. Previous studies [29, 30] have used exponential functions to model the nonlinear relationship between knee flexion angle and joint stiffness torque. Hence, we assumed G(θ) to be of the form
G(θ) = V_{1}θ exp(-V_{2}θ),
Definition of symbols used in the model.
Symbol | Unit | Definition |
---|---|---|
C _{ N } | --- | normalized amount of Ca^{2+}-troponin complex |
t | ms | time since the beginning of the stimulation |
t _{ i } | ms | time when the i th pulse is delivered |
τ _{ c } | ms | time constant controlling the rise and decay of C_{ N } |
R _{0} | ---- | term characterizing the magnitude of enhancement in C_{ N }from the following stimuli |
F | N | instantaneous force due to stimulation |
k _{ s } | N/m | spring stiffness |
b | Ns/m | damping coefficient |
V | m/s | shortening velocity of motor |
A _{40} | N/ms | scaling factor for force at 40° of knee flexion |
a | N/ms-deg^{2} | scaling factor to account for force at each knee flexion angle |
b | N/ms-deg | scaling factor to account for force at each knee flexion angle |
θ | deg | knee flexion angle |
H | Nm | resistance moment knee extension |
l | m | distance between knee center of rotation and center of mass of leg |
L | M | length of lever arm from center of force transducer to center of knee rotation |
V _{1} | N/deg^{2} | scaling factor in the term G(θ) |
T _{ STIM } | Nm | knee joint torque due to stimulation |
mg | N | weight of the tibia and foot |
V _{2} | 1/deg | constant that is linearly realted to τ_{2} (see Eqn. 20) |
K _{ m } | --- | sensitivity of strongly bound cross-bridges to C_{ N } |
τ _{1} | ms | time constant of force decline in the absence of strongly bound cross-bridges |
τ _{2} | ms | time constant of force decline due to the extra friction between actin and myosin resulting from the presence of strongly bound cross-bridges |
M | N | resistance to knee extension |
F _{ EXT } | N | experimental force measured by the KinCom dynamometer |
It is important to understand the practical meaning of F in Eqn. (13). The model must be fitted to experimental force data to evaluate the parameters (see SectionB.5). The experimental force is measured in a Kin-Com machine by placing a force transducer above the ankle joint (see Equipment and experimental setup section). When the quadriceps femoris muscle is stimulated, it exerts a force on the patellar ligament, which then transfers the quadriceps force onto the tibia in a complicated manner [33]. Hence, the quadriceps muscle exerts a force, F, on the transducer placed above the ankle joint. This force F is a function of patellar tendon force and the distance from the center of the force transducer to the center of knee rotation. Hence, the F in Eqn. (13) is now the force above the ankle joint exerted by the quadriceps in response to stimulations through the knee joint. From here on, we define this force (F) as the force due to the stimulation, as we have done previously [15, 18], so that the parameters incorporate the kinematic transfer of force from the muscle to the transducer.
Equations of motion
F_{ EXT }L - T_{ STIM }+ mg cosθ·l + H = 0,
where F_{ EXT }is the resistance the Kin-Com exerts above the ankle joint to move the tibia with a constant angular velocity and is the measured force from the Kin-Com,T_{ STIM }is the torque at the knee joint due to stimulation of the quadriceps femoris muscle, mg is the weight of the tibia and foot, H is the resistance moment to knee extension due to visco-elasticity of the musculotendon complex of the knee joint, θ is the knee flexion angle, l is the distance between knee center of rotation and center of mass of the leg below the knee, and L is the length of the lever arm from the center of the force transducer above the ankle joint to the center of knee rotation. The right hand side of Eqn. (14) is zero because there is no angular acceleration during the isovelocity phase of the contraction. Because the experimental force is measured with a force transducer placed just above the ankle joint we can write
T_{ STIM }= F·L,
Replacing $(mg\cdot \frac{l}{L}+R)$ by M in Eqn. (17) we obtain,
F_{ EXT }= F - M cosθ.
Thus, to obtain muscle force due to stimulation, F, it is necessary to add M cosθ to F_{ EXT }, the force measured by the Kin-Com force transducer. This was done during data analysis (see Experimental procedure for model development section for details), so that experimental forces can be compared to model predictions.
Subjects
Equipment and experimental setup
Subjects were seated on a computer controlled (KinCom III 500-11, Chattecx Corporation, Chattanooga, TN) dynamometer with their hips flexed to ~75° [36]. The dynamometer axis was aligned with the knee joint axis and the force transducer pad was positioned anteriorly against the tibia, 4 cm proximal to the lateral malleolus. Two 7.62 cm × 12.7 cm self-adhesive electrodes were used to stimulate the muscle. With the knee positioned at 90°, the anode was placed proximally over the motor point of the rectus femoris portion of the quadriceps femoris muscle. The cathode was placed distally over the vastus medialis motor point with the knee in 15° of flexion to compensate for skin movement during knee extension [37]. The trunk, pelvis, and thigh of the leg being tested were each stabilized with inelastic straps. A Grass S8800 stimulator with an SIU8T stimulus isolation unit (Grass Instruments, West Warwick, RI) was used for stimulation. The stimulator was driven by a personal computer using customized LabView (National Instruments, Austin, TX) software. Force and motion data from the transducer were sampled at 200 Hz using an analog-to-digital board. The data were then analyzed using a custom program written in LabView.
Using a KinCom dynamometer, isometric and isovelocity force data were collected from the human quadriceps femoris muscle in response to electrical stimulation. Each subjects performed a maximum voluntary isometric contraction (MVIC) of the quadriceps femoris muscle with the knee positioned at 90° of flexion. The burst-superimposition technique was used to ensure that a true maximal contraction was being performed [38]. Next, with the knee at 90° flexion the stimulation amplitude was set to activate ~20% of the muscle MVIC using a 300 ms-long 100-Hz stimulation train. Once the amplitude was set, it was held constant for the remainder of the session. The pulse duration was fixed at 600 μs throughout this study. To ensure consistency in the force responses to stimulation, we first potentiated the muscle using 14-Hz, 770 ms long trains before delivering the parameterizing and testing trains (see the section below for details of the parameterizing and testing trains).
Experimental procedure for model development
First, three subjects were recruited to participate in two testing sessions. A 48-hour rest period separated the two sessions. During the first session, testing was performed isometrically at angles of 15°, 40°, 65°, and 90°. The order of testing for the four angles was randomly determined and five minutes of rest was provided between each angle. Five minutes following the isometric testing, subjects were tested at one of the four isovelocity speeds of -25°/s, -75°/s, -125°/s, or -200°/s (all shortening velocities are assigned negative values in this study). During the second session, subjects were tested at the remaining three velocities. The order of testing the three velocities was randomly determined and five minutes of rest was provided between each velocity.
For the isometric testing, two one-second long trains were used to stimulate the muscle. Each train had an initial interpulse interval (IPI) of 5 ms and the remaining IPIs were either 20 or 80 ms (Fig. 1). These two variable-frequency trains (VFTs) were referred to as VFT20 and VFT80, respectively. Previous study by Ding and colleagues [5] showed that our model had the best predictive ability for human quadriceps femoris muscle if the model's parameter values were identified using force responses to these two trains. Within the stimulation protocol, first the VFT80 train followed the VFT20 train and then these trains were delivered in reverse order. Only one train was delivered every 10 s to minimize muscle fatigue. For the isovelocity study, 16 different trains were used: six constant-frequency trains (CFTs) referred to as CFT10, CFT20, CFT30, CFT50, CFT70, and CFT100; six VFTs referred to as VFT20, VFT30, VFT50, VFT70, VFT80, and VFT100; and four doublet frequency trains (DFTs) with 5 ms doublets throughout the train referred to as DFT30, DFT50, DFT70, and DFT100 (Fig. 1). The maximum number of pulses in each train was limited to 50, except for VFT20, which had a maximum of 51 pulses.
During isovelocity testing the KinCom was set to the Isokinetic mode, where the subjects remained passive and the KinCom arm moved the leg at predetermined speeds. The leg motion was initiated at 110° of knee flexion and stimulation began when the leg reached 90° of knee flexion and was terminated at 15° of knee flexion, unless all the pulses were already delivered. The KinCom arm moved the leg to 0° of knee flexion and then returned the leg back to 110° of knee flexion at a constant velocity of 25°/s. A 10 s rest time was provided before delivering the next train. Software, custom written in LabView, was used to determine the timing of each of the pulses delivered to each subject. In addition, force data were collected while passively moving the leg at constant velocity of -25°/s, -75°/s, -125°/s, and -200°/s from 110° to 0° of knee flexion to determine the value of M. The absolute value of M cosθ was then added to the measured force data, F_{ EXT }, to obtain the stimulation muscle-joint force, F (Eqn. 18) throughout the study.
Under isovelocity conditions, first the value of M was obtained by fitting the function M cosθ to the passive knee extension force data from 90° to 15° of knee flexion at each of the four velocities. The absolute value of M cosθ was then added to the measured force data, F_{ EXT }, to obtain the stimulation muscle-joint force, F (Eqn. 18). The model (Eqns. 1 and 13) was then fitted to the forces elicited by the VFT20-VFT80 train combination at -25°/s, -75°/s, -125°/s, and -200°/s to obtain the values of V_{1} and V_{2} at each of four velocities. This was done to determine the best velocity to identify the values of V_{1} and V_{2}. For all the data collected, the two occurrences of each of the stimulation trains were averaged to reduce the effects of physiological variability on the muscle's response to each train.
Results for model development
Values of parameter M at each velocity for the three subjects tested.
Velocity (°/s) | Subject 1 | Subject 2 | Subject 3 |
---|---|---|---|
-25 | -65.8 | -119.6 | -58.0 |
-75 | -62.7 | -110.4 | -46.5 |
-125 | -68.9 | -154.2 | -71.2 |
-200 | -66.5 | -120.0 | -52.9 |
Validation of the model
The model was validated by determining its ability to predict forces in response to wide range stimulation frequencies and patterns at velocities of -25°/s, -75°/s, -125°/s, and -200°/s. Data were collected from three additional subjects. The same protocol used for the first three subjects recruited for the model development phase was tested and the data for the six subjects were pooled (Fig. 3).
Data analysis for model validation
% error, linear regression trend lines, and paired t-tests were used to test how well the model predicted the experimental forces. Mean % errors between the model and experimental forces normalized to the experimental peak force and measured at each 5 ms time interval were calculated for each subject. The experimentally measured and model's predicted force-time integrals and peak forces were averaged across six subjects at each velocity and at each stimulation pattern. Paired t-tests were used to compare the average measured and predicted data. The paired t-test comparisons were considered significant if p = 0.05. Linear regression trend lines were used to determine how well the model predicted the force-time integrals (area under the force-time plots) and the peak forces for each train tested at each velocity for all the six subjects. The slope of the trend line was set to one and the intercept was set to zero. A perfectly accurate model would have a coefficient of determination, R^{2}, of one.
Model simplification
R^{2} values for the relationships of parameters V_{1} and V_{2} with the other model parameters
V_{1}-vs-a | V_{1}-vs-b | V_{1}-vs-A_{40} | V_{1}-vs-K_{ M } | V_{1}-vs-τ_{1} | V_{1}-vs-τ_{2} | V_{2}-vs-a | V_{2}-vs-b | V_{2}-vs-A_{40} | V_{2}-vs-K_{ M } | V_{2}-vs-τ_{1} | V_{2}-vs-τ_{2} | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
R^{2} | 0.05 | 0.00 | 0.04 | 0.27 | 0.00 | 0.24 | 0.46 | 0.62 | 0.04 | 0.02 | 0.16 | 0.81 |
V_{2} = 0.0002 * τ_{2} + 0.0048.
The above empirical relationship between V_{2} and τ_{2} was then incorporated in the model and the force responses to the VFT20-VFT80 train combination at -200°/s were fit again to calculate the new value of V_{1} for the six subjects used to validate the model.
Sensitivity analysis
Sensitivity analysis was performed using Fourier Amplitude Sensitivity Test (FAST) to obtain a measure of the sensitivity of our model's output to changes in model parameters. The FAST method was used to estimate the expected value and variance of the output, and the contribution of individual inputs to the variance of the output [40]. The ratio of the contribution of each input to the total output variance is referred to as the first order sensitivity index and can be used to rank the inputs [41]. For the current sensitivity analysis, the output of the model was the force-time integral (area under the force-time curve) in response to a 50-pulse, 33-Hz stimulation train at each of the four velocities tested and under isometric conditions at 90° of knee flexion. Parameters τ_{ c }and R_{0} were kept fixed at 20-ms and 2, respectively, and equation 20 was used to calculate the value of V_{2}. Values of the other seven parameters were varied within the following ranges: a (-0.003 to -0.0005), b (-0.22 to -0.02), A_{40} (1.5 to 8.7), τ_{1} (26 to 76), τ_{2} (58 to 280), K_{ M }(0.15 to 0.66), and V_{1} (0.0007 to 0.0028). The range of values for the above seven parameters were determined based on the parameter values of 10 subjects in the current study. SIMLAB [42] software was used to carry out the sensitivity analysis. A total of 623 sample sets were generated using Monte Carlo methods. Each sample set consisted of the different values of the eight model parameters. FAST first order sensitivity index was calculated for each parameter. The higher the value of the sensitivity index of a parameter, the greater is the sensitivity of the model output (force-time integral) to changes in that model parameter.
Results
Mean % errors (± SE) between the model and experimental forces normalized to the experimental peak force and measured at each 5 ms time interval for each subject. Data are the averages (± SE) across 6 IPIs (CFTs or VFTs) or 4 IPIs (DFTs) tested.
-25°/s | -75°/s | -125°/s | -200°/s | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
%Error | CFT | VFT | DFT | CFT | VFT | DFT | CFT | VFT | DFT | CFT | VFT | DFT |
S1 | 8.2 (1.3) | 9.7 (1.2) | 10.7 (0.5) | 16.7 (0.6) | 17.4 (1.2) | 16.8 (0.4) | 18.8 (4.9) | 18.7 (3.4) | 11.9 (1.4) | 11.6 (0.8) | 9 (0.9) | 8.6 (1.0) |
S2 | 12.6 (1.6) | 13.9 (1.8) | 12.4 (1.8) | 11.9 (1.8) | 10.9 (1.0) | 10 (0.5) | 16.9 (3.6) | 14.9 (2.7) | 11.3 (0.7) | 18.8 (2.9) | 10.1(1.6) | 9.5 (2.7) |
S3 | 18.3 (5.3) | 17.6 (3.2) | 13.5 (3.2) | 11.7 (2.1) | 10.9 (0.6) | 11 (0.6) | 12.6 (3.2) | 10.6 (1.2) | 11(2.5) | 20.1(5.4) | 12.2(2.0) | 11.6(2.4) |
S4 | 11.9 (2.7) | 11.2 (2.3) | 9.3 (0.8) | 21.2 (2.8) | 21.5 (1.8) | 20.3 (1.0) | 35.5 (7.2) | 29.1(4.3) | 25.1(1.8) | 19.2(6.9) | 17 (3.1) | 10.0(1.2) |
S5 | 24.0 (8.0) | 16.5 (4.9) | 18.6 (7.8) | 17.2 (4.7) | 10.9 (0.7) | 11.4 (2.7) | 17.5 (2.3) | 17.6 (2.8) | 16.3(1.1) | 27.2(3.9) | 22.5(1.1) | 22.4 (2.0) |
S6 | 9.3 (.14) | 8.7 (1.7) | 6.9 (1.7) | 22.8 (1.9) | 20.2 (1.8) | 22.5 (1.6) | 29.5 (1.7) | 23.5 (1.7) | 23.6(1.2) | 12.3(1.5) | 11.4(0.8) | 11.0 (1.3) |
Mean | 14.1 (3.4) | 13.0 (2.5) | 11.9 (2.6) | 16.9 (2.3) | 15.3 (1.2) | 15.4 (1.1) | 21.8 (3.8) | 19.1 (2.7) | 16.6 (1.5) | 18.2 (2.6) | 13.7 (1.6) | 12.2 (1.8) |
Discussion
In this study we developed a mathematical model of healthy human quadriceps femoris muscle that predicted forces under isovelocity conditions. Our results showed that our model had the ability to predict the force responses of the quadriceps femoris muscle to a wide range of clinically relevant stimulation frequencies and patterns when the leg was moved at a variety of constant velocities. In the current model, by identifying the values of the parameters a, b, A_{40}, τ_{1}, τ_{2}, and K_{ M }under isometric conditions, only the value of the parameter V_{1} needed to be identified at -200°/s for the model to capture the shortening and lengthening forces of the muscle over a wide range of constant velocities. All the above parameters were identified by fitting the force responses to only two trains, the VFT20-VFT80 train combination.
The term G(θ)$\dot{\theta}$ (see Eqn. (12)) was motivated by the formulation that represents the muscle by a motor-damper-spring combination in series. Other than this new term, the current model used the same equations used for the isometric models that we developed previously [16–18]. In isometric contractions, the motor's rate of shortening is balanced by the damper and spring to produce muscle force. For shortening contractions, however, the shortening of the muscle reduces stretching of the damper and spring, resulting in lower muscle forces. Physiologically, because the cross-bridge recycling rate, which is modeled as the motor's rate of shortening, is finite, increased rates of shortening reduce the stretching of the muscle's viscous and elastic components, resulting in lower muscle forces. The exact form of the function G(θ)$\dot{\theta}$ = V_{1}θ exp(-V_{2}θ)$\dot{\theta}$ captures the above non-isometric effects and accounts for the influence of the knee joint kinematics on the force-velocity relationship. Interestingly, a correlation was found between the parameters V_{2} and τ_{2} (see Eqn. (20)). Wexler and colleagues [19] have shown that τ_{2} varied in muscles with different fiber type composition. Previous studies have shown that fiber type composition plays an important role in influencing the force-velocity relationship [43, 44]. The influence of fiber type on the force-velocity relationship in the current model was captured through the parameter V_{2}, which was incorporated in the function G(θ)$\dot{\theta}$ = V_{1}θ exp(-V_{2}θ)$\dot{\theta}$; the relationships between model parameters and muscle type were supported by the τ_{2}-V_{2} relationship.
During eccentric contractions, the muscle lengthens instead of shortening. Such contractions are characterized by higher forces than isometric and shortening contractions and are important parts of many functional activities, such as walking. Hill-type models that predict eccentric muscle forces use a separate set of equations to describe the behavior of the muscle during eccentric contractions [6]. For the current model only one set of equations were necessary. The reason for the model's ability to predict lengthening contractions was due to the coupling between G(θ)$\dot{\theta}$ and C_{ N }/(K_{ M }+ C_{ N }) (see Eqn. 13). G(θ)$\dot{\theta}$ increases with lengthening velocity whereas C_{ N }/(K_{ M }+ C_{ N }) decreases. At a given stimulation frequency, the greater the lengthening velocity the fewer the number of pulses delivered and, hence, smaller the value of C_{ N }. The increase in G(θ)$\dot{\theta}$ with lengthening velocity was, therefore, compensated by a decrease in C_{ N }/(K_{ M }+ C_{ N }) and helped to maintain the peak forces at almost a constant value with increasing velocities in the flat portion of the force-lengthening velocity curve (see Figs. 10c and 10d).
The current model development and verification were done for healthy human quadriceps femoris muscle under isovelocity conditions. Because of the various assumptions made in developing the current model, the model may not be valid when applied to other muscle groups, when muscles become fatigued, or to patient population. For example, the assumptions of having τ_{ c }at a fixed value of 20 ms or that parameter M is independent of joint velocity may not be valid for spinal cord injured or stroke patients. In addition, for these and other patient populations reflex activity needs to be considered when predicting the forces in response to electrical stimulation. Also, for the current model to be useful for an FES application like walking, the model needs to predict the angular velocity based on the load and stimulation pattern. Finally, the current modelling work only considered the effect of stimulation frequency and pattern. Future modelling work will also need to predict the effects of stimulation intensity on the forces produced by the muscle.
Conclusion
This study showed that the current model predicted the forces in response to a wide range of stimulation frequencies and constant velocities in able-bodied human quadriceps muscles. Our model did not assume an a priori force-velocity relationship. Rather, the relationship was a natural outcome of modeling the viscoelastic and contractile behavior of the muscle. The range of predictive abilities of the isovelocity model in response to changes in muscle length, velocity, and stimulation frequency for each individual make it ideal for dynamic applications like FES cycling [47]. In FES cycling where an external motor maintains the speed of cycling constant, our model can be used to design stimulation patterns that can produce the targeted level of power output from the muscle.
Declarations
Acknowledgements
The authors would like to thank Dr. Jun Ding for her helpful comments. This study was supported by the National Institutes of Health Grants HD 36797, HD38582, and NR010786.
Authors’ Affiliations
References
- Bajd T, Kralj A, Turk R, Benko H, Sega J: Use of functional electrical stimulation in the rehabilitation of patients with incomplete spinal cord injuries. J Biomed Eng 1989,11(2):96-102. 10.1016/0141-5425(89)90115-5View ArticlePubMedGoogle Scholar
- Binder-Macleod S, Kesar T: Catchlike property of skeletal muscle: recent findings and clinical implications. Muscle Nerve 2005,31(6):681-693. 10.1002/mus.20290View ArticlePubMedGoogle Scholar
- Kebaetse MB, Binder-Macleod SA: Strategies that improve human skeletal muscle performance during repetitive, non-isometric contractions. Pflugers Arch 2004,448(5):525-532. 10.1007/s00424-004-1279-0View ArticlePubMedGoogle Scholar
- Garland SJ, Griffin L: Motor unit double discharges: statistical anomaly or functional entity? Can J Appl Physiol 1999,24(2):113-130.View ArticlePubMedGoogle Scholar
- Ding J, Wexler AS, Binder-Macleod SA: A mathematical model that predicts the force-frequency relationship of human skeletal muscle. Muscle Nerve 2002,26(4):477-485. 10.1002/mus.10198View ArticlePubMedGoogle Scholar
- Brown IE, Cheng EJ, Loeb GE: Measured and modeled properties of mammalian skeletal muscle. II. The effects of stimulus frequency on force-length and force-velocity relationships. J Muscle Res Cell Motil 1999,20(7):627-643. 10.1023/A:1005585030764View ArticlePubMedGoogle Scholar
- Dorgan SJ, O'Malley MJ: A mathematical model for skeletal muscle activated by N-let pulse trains. IEEE Trans Rehabil Eng 1998,6(3):286-299. 10.1109/86.712226View ArticlePubMedGoogle Scholar
- Shue GH, Crago PE: Muscle-tendon model with length history-dependent activation-velocity coupling. Ann Biomed Eng 1998,26(3):369-380. 10.1114/1.93View ArticlePubMedGoogle Scholar
- Riener R, Quintern J, Schmidt G: Biomechanical model of the human knee evaluated by neuromuscular stimulation. J Biomech 1996,29(9):1157-1167. 10.1016/0021-9290(96)00012-7View ArticlePubMedGoogle Scholar
- Durfee WK, Palmer KI: Estimation of force-activation, force-length, and force-velocity properties in isolated, electrically stimulated muscle. IEEE Trans Biomed Eng 1994,41(3):205-216. 10.1109/10.284939View ArticlePubMedGoogle Scholar
- Stein RB, Bobet J, Oguztöreli MN, Fryer M: The kinetics relating calcium and force in skeletal muscle. Biophysical Journal 1988, 54: 705-716.PubMed CentralView ArticlePubMedGoogle Scholar
- Zahalak GI, Ma SP: Muscle activation and contraction: constitutive relations based directly on cross-bridge kinetics. J Biomech Eng 1990,112(1):52-62. 10.1115/1.2891126View ArticlePubMedGoogle Scholar
- Ferrarin M, Pedotti A: The relationship between electrical stimulus and joint torque: a dynamic model. IEEE Trans Rehabil Eng 2000,8(3):342-352. 10.1109/86.867876View ArticlePubMedGoogle Scholar
- Bobet J, Stein RB: A simple model of force generation by skeletal muscle during dynamic isometric contractions. IEEE Trans Biomed Eng 1998,45(8):1010-1016. 10.1109/10.704869View ArticlePubMedGoogle Scholar
- Ding J, Binder-Macleod SA, Wexler AS: Two-step, predictive, isometric force model tested on data from human and rat muscles. J Appl Physiol 1998,85(6):2176-2189.PubMedGoogle Scholar
- Ding J, Wexler AS, Binder-Macleod SA: Development of a mathematical model that predicts optimal muscle activation patterns by using brief trains. J Appl Physiol 2000,88(3):917-925.PubMedGoogle Scholar
- Ding J, Wexler AS, Binder-Macleod SA: A predictive model of fatigue in human skeletal muscles. J Appl Physiol 2000,89(4):1322-1332.PubMedGoogle Scholar
- Perumal R, Wexler AS, Ding J, Binder-Macleod SA: Modeling the length dependence of isometric force in human quadriceps muscles. J Biomech 2002,35(7):919-930. 10.1016/S0021-9290(02)00049-0View ArticlePubMedGoogle Scholar
- Wexler AS, Ding J, Binder-Macleod SA: A mathematical model that predicts skeletal muscle force. IEEE Trans Biomed Eng 1997,44(5):337-348. 10.1109/10.568909View ArticlePubMedGoogle Scholar
- Bobet J, Gossen ER, Stein RB: A comparison of models of force production during stimulated isometric ankle dorsiflexion in humans. IEEE Trans Neural Syst Rehabil Eng 2005,13(4):444-451. 10.1109/TNSRE.2005.858461View ArticlePubMedGoogle Scholar
- Frey Law LA, Shields RK: Mathematical models use varying parameter strategies to represent paralyzed muscle force properties: a sensitivity analysis. J Neuroengineering Rehabil 2005, 2: 12. 10.1186/1743-0003-2-12PubMed CentralView ArticleGoogle Scholar
- Ding J: Mathematical models that predict muscle isometric forces and fatigue. In PhD Thesis. University of Delaware; 2001.Google Scholar
- Rüegg JC: Calcium in muscle contraction. Springer-Verlag; 1992.View ArticleGoogle Scholar
- Duchateau J, Hainaut K: Nonlinear summation of contractions in striated muscle. I. Twitch potentiation in human muscle. J Muscle Res Cell Motil 1986,7(1):11-17. 10.1007/BF01756197View ArticlePubMedGoogle Scholar
- Hill AV: First and last experiments in muscle mechanics. Cambridge Univ. Press, Cambridge; 1970.Google Scholar
- Martin A, Martin L, Morton B: Theoretical and experimental behavior of muscle viscosity coefficient during maximal concentric actions. Eur J Appl Physiol Occup Physiol 1994,69(6):539-544. 10.1007/BF00239872View ArticlePubMedGoogle Scholar
- Aidley DJ: The Physiology of Excitable Cells. New York: Cambridge University Press; 1989.Google Scholar
- Allen DG, Blinks JR: The interpretation of light signals from aequorin-injected skeletal and cardiac muscle cells: a new method of calibration. In: Detection and Measurement of Free Ca2+ in Cells. A.K. Amsterdam: Elsevier/North-Holland; 1979.Google Scholar
- Mansour JM, Audu ML: The passive elastic moment at the knee and its influence on human gait. J Biomech 1986,19(5):369-373. 10.1016/0021-9290(86)90013-8View ArticlePubMedGoogle Scholar
- Riener R, Edrich T: Identification of passive elastic joint moments in the lower extremities. J Biomech 1999,32(5):539-544. 10.1016/S0021-9290(99)00009-3View ArticlePubMedGoogle Scholar
- Heckman CJ, Weytjens JL, Loeb GE: Effect of velocity and mechanical history on the forces of motor units in the cat medial gastrocnemius muscle. J Neurophysiol 1992,68(5):1503-1515.PubMedGoogle Scholar
- de Haan A: The influence of stimulation frequency on force-velocity characteristics of in situ rat medial gastrocnemius muscle. Exp Physiol 1998,83(1):77-84.View ArticlePubMedGoogle Scholar
- Buff HU, Jones LC, Hungerford DS: Experimental determination of forces transmitted through the patello-femoral joint. J Biomech 1988,21(1):17-23. 10.1016/0021-9290(88)90187-XView ArticlePubMedGoogle Scholar
- Stein RB, Momose K, Bobet J: Biomechanics of human quadriceps muscles during electrical stimulation. J Biomech 1999,32(4):347-357. 10.1016/S0021-9290(98)00187-0View ArticlePubMedGoogle Scholar
- Riener R: Model-based development of neuroprosthesis for paraplegic patients. Philos Trans R Soc Lond B Biol Sci 1999,354(1385):877-894. 10.1098/rstb.1999.0440PubMed CentralView ArticlePubMedGoogle Scholar
- Binder-Macleod SA, Lee SC, Baadte SA: Reduction of the fatigue-induced force decline in human skeletal muscle by optimized stimulation trains. Arch Phys Med Rehabil 1997,78(10):1129-1137. 10.1016/S0003-9993(97)90140-4View ArticlePubMedGoogle Scholar
- Lee SC, Binder-Macleod SA: Effects of activation frequency on dynamic performance of human fresh and fatigued muscles. J Appl Physiol 2000,88(6):2166-2175.PubMedGoogle Scholar
- Snyder-Mackler L, De Luca PF, Williams PR, Eastlack ME, Bartolozzi AR 3rd: Reflex inhibition of the quadriceps femoris muscle after injury or reconstruction of the anterior cruciate ligament. J Bone Joint Surg Am 1994,76(4):555-560.PubMedGoogle Scholar
- Abbas JJ, Riener R: Using Mathematical Models and Advanced Control Systems Techniques to Enhance Neuroprosthesis Function. Neuromodulation 2001, 4: 187-195. 10.1046/j.1525-1403.2001.00187.xView ArticlePubMedGoogle Scholar
- Cukier R, Fortuin C, Shuler K, Petschek A, Schailby J: Study of the sensitivity of the coupled reaction systems to uncertainties in rate coefficients: I. Theory. Journal of Chemical Physics 1973,59(8):3873-3878. 10.1063/1.1680571View ArticleGoogle Scholar
- Saltelli A, Chan K, Scott E: Sensitivity Analysis. John Wiley and Son, Ltd.: West Sussex, England; 2000.Google Scholar
- SIMLAB: Simulation Environment for uncertainty and sensitivity analysis, developed by Joint Research Center of the European Commission. 2.2nd edition. 2004.Google Scholar
- Phillips CA, Petrofsky JS: Velocity of contraction of skeletal muscle as a function of activation and fiber composition: a mathematical model. J Biomech 1980,13(7):549-558. 10.1016/0021-9290(80)90055-XView ArticlePubMedGoogle Scholar
- Baratta RV, Solomonow M, Best R, Zembo M, D'Ambrosia R: Architecture-based force-velocity models of load-moving skeletal muscles. Clin Biomech (Bristol, Avon) 1995,10(3):149-155. 10.1016/0268-0033(95)93705-XView ArticleGoogle Scholar
- Dudley GA, Harris RT, Duvoisin MR, Hather BM, Buchanan P: Effect of voluntary vs. artificial activation on the relationship of muscle torque to speed. J Appl Physiol 1990,69(6):2215-2221.PubMedGoogle Scholar
- Harris RT, Dudley GA: Factors limiting force during slow, shortening actions of the quadriceps femoris muscle group in vivo. Acta Physiol Scand 1994,152(1):63-71. 10.1111/j.1748-1716.1994.tb09785.xView ArticlePubMedGoogle Scholar
- Hunt KJ, Stone B, Negard NO, Schauer T, Fraser MH, Cathcart AJ, Ferrario C, Ward SA, Grant S: Control strategies for integration of electric motor assist and functional electrical stimulation in paraplegic cycling: utility for exercise testing and mobile cycling. IEEE Trans Neural Syst Rehabil Eng 2004,12(1):89-101. 10.1109/TNSRE.2003.819955View ArticlePubMedGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.