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![Human arm model diagram
(a) Side view of planar arm model with motion in the vertical plane, (b) Front view of the human arm muscle system, adapted from [36]. Antagonistic monoarticular muscle pairs for the shoulder are anterior deltoid A and posterior deltoid B; antagonistic monoarticular muscle pairs for the elbow are brachialis C and short head of triceps brachii D; biarticular muscles are biceps brachii E and long head of triceps brachii F. q1 and q2 are shoulder and elbow joint angles [35]. J1=5 cm and J2=3 cm are moment arms](publication/349219529/figure/fig1/AS:11431281179027948@1691146490764/Human-arm-model-diagram-a-Side-view-of-planar-arm-model-with-motion-in-the-vertical.png)
Human arm model diagram (a) Side view of planar arm model with motion in the vertical plane, (b) Front view of the human arm muscle system, adapted from [36]. Antagonistic monoarticular muscle pairs for the shoulder are anterior deltoid A and posterior deltoid B; antagonistic monoarticular muscle pairs for the elbow are brachialis C and short head of triceps brachii D; biarticular muscles are biceps brachii E and long head of triceps brachii F. q1 and q2 are shoulder and elbow joint angles [35]. J1=5 cm and J2=3 cm are moment arms
Source publication
This study synthesises modelling techniques and dynamic state estimation techniques for the simultaneous estimation of the muscle states, muscle forces, and joint motion states of a dynamic human arm model. The estimator considers both muscle dynamics and motion dynamics. The arm model has two joints and six muscles and contains dynamics both of th...
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... For instance, Thatte et al. [31] demonstrated the effectiveness of the EKF in estimating joint angles during human locomotion, providing valuable insights into gait analysis. Furthermore, Mohammadi et al. [32] employed EKF to estimate upper limb forces during activities of daily living, showcasing its applicability in studying force dynamics. While these traditional EKF approaches have made substantial contributions to the field, the proposed method in this study introduces a noteworthy innovation. ...
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... These approaches then minimize the kinematic error. The state vector can also include both kinematic and body segment parameters (Bonnet et al., 2017) or both kinematic and kinetic parameters (e.g., joint angles and muscle forces or joint torques) (Mohammadi et al., 2020). Or it can include uncertainty models of IMU-sensors to estimate the size of undesired errors (orientation errors, gyroscope bias and magnetic disturbances) to compensate these (Yuan et al., 2019). ...
... The Kalman Filter offers unique advantages when applied for musculoskeletal modeling and simulation since the method was specifically developed to enhance state estimations (originally position location) based on erroneous observations (Serra, 2018). Mohammadi et al. (2020) produced estimates that are both consistent with the system and the measurement model. They assumed that joint angle measurements are affected by white Gaussian noise; e.g., modelled as a normal probability distribution. ...
... Covariances have to be known to compensate measuring noise and STAs cannot yet be compensated in contrast to white gaussian measurement noise since no coherent error model exists. Further, the Kalman Filter is currently mostly applied for simulating two-dimensional models (Bonnet et al., 2017;Mohammadi et al., 2020). Bonnet et al. (2017) state that, although possible, expanding their method to include three-dimensional model estimation would require significantly more parameters and could lead to redundancy problems. ...
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Given the widespread use of the Kalman filter in robotics, an increasing number of researchers devote themselves to its study and application. This work underscores the importance of this filter while analyzing the modifications made to the same to improve its performance and reduce its deficiencies in some fields and presenting some of its applications in robotics. The following methods are presented in this study: least squares (LS), Hopfield Neural Networks (HNN), Extended Kalman filter (EKF), and Unscented Kalman filter (UKF). These methods are used in the parameter identification of a Selective Compliant Assembly Robot Arm (SCARA) robot with 3-Degrees of Freedom (3-DoF) and a clamp at its end. The dynamic model of this robot is obtained and employed to identify its parameters; then, the identification results are compared considering the difference between the obtained parameters and the real values of the robot parameters; in this comparison, the good results yielded by the LS and UKF method stand out. Subsequently, the obtained parameters through each method are validated by measuring different performance indexes—during trajectory tracking—such as: Residual Mean Square Error (RMSE), Integral of the Absolute Error (IAE), and the Integral of the Square Error (ISE). In this way, a comparison of the robot’s performance is possible.
... constant angular momentum) and considering the whole movement is not trivial (Bonnet et al., 2016). Mohammadi et al. (2020) proposed an original optimally tuned EKF with a simple musculoskeletal arm model. However, it needs to be extended to a 3D full-body model and validated for high-speed movements with large range of motion. ...
When estimating full-body motion from experimental data, inverse kinematics followed by inverse dynamics does not guarantee dynamical consistency of the resulting motion, especially in movements where the trajectory depends heavily on the initial state, such as in free-fall. Our objective was to estimate dynamically consistent joint kinematics and kinetics of complex aerial movements. A 42-degrees-of-freedom model with 95 markers was personalised for five elite trampoline athletes performing various backward and forward twisting somersaults. Using dynamic optimisation, our algorithm estimated joint angles, velocities and torques by tracking the recorded marker positions. Kinematics, kinetics, angular and linear momenta, and marker tracking difference were compared to results of an Extended Kalman Filter (EKF) followed by inverse dynamics. Angular momentum and horizontal linear momentum were conserved throughout the estimated motion, as per free-fall dynamics. Marker tracking difference went from 17 ± 4 mm for the EKF to 36 ± 11 mm with dynamic optimisation tracking the experimental markers, and to 49 ± 9 mm with dynamic optimisation tracking EKF joint angles. Joint angles from the dynamic optimisations were similar to those of the EKF, and joint torques were smoother. This approach satisfies the dynamics of complex aerial rigid-body movements while remaining close to the experimental 3D marker dataset.
