Search for: All records

IntroductionCompartment based models of muscle fatigue have been particularly successful in accurately modeling isometric (static) tasks or actions. However, dynamic actions, which make up most everyday movements, are governed by different central and peripheral processes, and must therefore be modeled in a manner accounting for the differences in the responsible mechanisms. In the literature, a three-component controller (3CC) muscle fatigue model (MFM) has been proposed and validated for static tasks. A recent study reported a four-compartment muscle fatigue model considering both short- and long-term fatigued states. However, neither has been validated for both static and dynamic tasks. MethodsIn this work we proposed a new four-compartment controller model of muscle fatigue with enhanced recovery (4CCr) that allows the modeling of central and peripheral fatigue separately and estimates strength decline for static and dynamic tasks. Joint velocity was used as an indicator of the degree of contribution of either mechanism. Model parameters were estimated from part of the experimental data and finally, the model was validated through the rest of experimental data that were not used for parameter estimation. ResultsThe 3CC model cannot capture the fatigue phenomenon that the velocity of contraction would affect isometric strength measurements as shown in experimental data. The new 4CCr model maintains the predictions of the extensively validated 3CC model for static tasks but provides divergent predictions for isokinetic activities (increasing fatigue with increasing velocity) in line with experimentally observed trends. This new 4CCr model can be extended to various domains such as individual muscle fatigue, motor units’ fatigue, and joint-based fatigue. 

Abstract This paper predicts the optimal motion for a repetitive lifting task considering muscle fatigue. The Denavit–Hartenberg (DH) representation is employed to characterize the two-dimensional (2D) digital human model with 10 degrees-of-freedom (DOFs). Two joint-based muscle fatigue models, i.e., a three-compartment controller (3CC) muscle fatigue model (validated for isometric tasks) and a four-compartment controller with augmented recovery (4CCr) muscle fatigue model (validated for dynamic tasks), are utilized to account for the fatigue effect due to the repetitive motion. The lifting problem is formulated mathematically as an optimization problem, with the objective of minimizing dynamic effort and joint acceleration subjected to both physical and task-specific constraints. The design variables include joint angle profiles, discretized by quartic B-splines, and the control points of the profiles of the fatigue compartments associated with major body joints (spinal, shoulder, elbow, hip, and knee joints). The outcomes of the simulation encompass profiles of joint angles, joint torques, and the advancement of joint fatigue. It is notable that the profiles of joint angles and torques exhibit distinct periodic patterns. Numerical simulations and experiments with a 20 kg box reveal that the maximum predicted lifting cycles are 11 for the 3CC fatigue model and 13 for the 4CCr fatigue model while the experimental result is 13 cycles. The results indicate that the 4CCr muscle fatigue model provides enhanced accuracy over the 3CC model for predicting task duration (number of cycles) of repetitive lifting. 

Abstract This study presents a comprehensive finite element (FE) model for the human wrist, constructed from a CT scan of a 68-year-old male (type 1 wrist). This model intricately captures the bone and soft tissue geometries to study the biomechanics of wrist axial loading through tendon-driven simulations and grasping biomechanics using metacarpal loads. Validation is carried out by assessing the radial and ulnar axial loading distribution, radiocarpal articulation contact patterns, and other standard finite element metrics. The results show radial transmission of the load, consistent with results from wrist finite element models conducted in the last decade and other experimental studies. Our results confirm the model's efficacy in reproducing key known biomechanical aspects, laying the groundwork for future investigations into ongoing wrist biomechanics challenges and pathology mechanism studies. 

Abstract Estimating muscle forces is crucial for understanding joint dynamics and improving rehabilitation strategies, particularly for patients with neurological disorders who suffer from impaired muscle function. Muscle forces are directly proportional to muscle activations, which can be obtained using electromyography (EMG). EMG-driven modeling estimates muscle forces and joint moments from muscle activations. While surface muscles' activations can be obtained using surface electrodes, deep muscles require invasive methods and are not readily available for real-time applications. This study aims to extend our previously developed method for a single unmeasured muscle to a comprehensive approach for the simultaneous prediction of multiple unmeasured muscle activations in the upper extremity using muscle synergy extrapolation and EMG-driven modeling. By employing non-negative matrix factorization to decompose known EMG data into synergy components, the activations of unmeasured muscles are reconstructed with high accuracy by minimizing differences between joint moments obtained by EMG-driven modeling and inverse dynamics. This methodology is validated through experimentally collected muscle activations, demonstrating over 90% correlation with EMG signals in various scenarios. 

Abstract Patients with neuromuscular disease fail to produce necessary muscle force and have trouble maintaining joint moment required to perform activities of daily living. Measuring muscle force values in patients with neuromuscular disease is important but challenging. Electromyography (EMG) can be used to obtain muscle activation values, which can be converted to muscle forces and joint torques. Surface electrodes can measure activations of superficial muscles, but fine-wire electrodes are needed for deep muscles, although it is invasive and require skilled personnel and preparation time. EMG-driven modeling with surface electrodes alone could underestimate the net torque. In this research, authors propose a methodology to predict muscle activations from deeper muscles of the upper extremity. This method finds missing muscle activation one at a time by combining an EMG-driven musculoskeletal model and muscle synergies. This method tracks inverse dynamics joint moments to determine synergy vector weights and predict muscle activation of selected shoulder and elbow muscles of a healthy subject. In addition, muscle-tendon parameter values (optimal fiber length, tendon slack length, and maximum isometric force) have been personalized to the experimental subject. The methodology is tested for a wide range of rehabilitation tasks of the upper extremity across multiple healthy subjects. Results show this methodology can determine single unmeasured muscle activation up to Pearson's correlation coefficient (R) of 0.99 (root mean squared error, RMSE = 0.001) and 0.92 (RMSE = 0.13) for the elbow and shoulder muscles, respectively, for one degree-of-freedom (DoF) tasks. For more complicated five DoF tasks, activation prediction accuracy can reach up to R = 0.71 (RMSE = 0.29). 

Abstract Cerebrovascular accidents like a stroke can affect the lower limb as well as upper extremity joints (i.e., shoulder, elbow, or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles’ ability to generate forces reduces, thus affecting the joint’s torque production. Understanding how muscles generate forces is a key element to injury detection. Researchers have developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain joint torques from muscle activity measurements. Musculoskeletal models and EMG-driven models require necessary muscle-specific parameters for the calculation. The focus of this study is to investigate the EMG-driven approach along with an upper extremity musculoskeletal model to determine muscle forces of two major muscle groups, biceps brachii and triceps brachii, consisting of seven muscle-tendon units. Estimated muscle forces are used to determine the elbow joint torque. Experimental EMG signals and motion capture data are collected for a healthy subject. The musculoskeletal model is scaled to match the geometric parameters of the subject. Then, the approach calculates muscle forces and joint moment for two tasks: simple elbow flexion extension and triceps kickback. Individual muscle forces and net joint torques for both tasks are estimated. The study also has compared the effect of muscle-tendon parameters (optimal fiber length and tendon slack length) on the estimated results. 

In this study, a 3D asymmetric lifting motion is predicted by using a hybrid predictive model to prevent potential musculoskeletal lower back injuries for asymmetric lifting tasks. The hybrid model has two modules: a skeletal module and an OpenSim musculoskeletal module. The skeletal module consists of a dynamic joint strength based 40 degrees of freedom spatial skeletal model. The skeletal module can predict the lifting motion, ground reaction forces (GRFs), and center of pressure (COP) trajectory using an inverse dynamics-based motion optimization method. The musculoskeletal module consists of a 324-muscle-actuated full-body lumbar spine model. Based on the predicted kinematics, GRFs and COP data from the skeletal module, the musculoskeletal module estimates muscle activations using static optimization and joint reaction forces through the joint reaction analysis tool in OpenSim. The predicted asymmetric motion and GRFs are validated with experimental data. Muscle activation results between the simulated and experimental EMG are also compared to validate the model. Finally, the shear and compression spine loads are compared to NIOSH recommended limits. The differences between asymmetric and symmetric liftings are also compared.