scholarly journals A passive movement method for parameter estimation of a musculo-skeletal arm model incorporating a modified hill muscle model

2014 ◽  
Vol 114 (3) ◽  
pp. e46-e59 ◽  
Author(s):  
Tung Fai Yu ◽  
Adrian J. Wilson
Keyword(s):  
Parameter Estimation ◽  
Passive Movement ◽  
Muscle Model ◽  
Hill Muscle Model

Simulation of a Three link- Six Musculo Skeletal Arm Activated by Hill Muscle Model

2019 ◽  
Author(s):  
Nafiseh Ebrahimi
Keyword(s):  
Inverse Dynamics ◽  
Joint Space ◽  
Initial Position ◽  
Muscle Model ◽  
End Effector ◽  
Joint Torques ◽  
Muscle Dynamics ◽  
Human Arm ◽  
The One ◽  
Hill Muscle Model

The study of humanoid character is of great interest of researchers in the field of robotics and biomechanics. The one might want to know the forces and torques required to move a limb from an initial position to the desired destination position. Inverse dynamics is a helpful method to compute the force and torques for an articulated body limb. It enables us to know the joint torques required to rotate a link between two positions. Our goal in this study was to control a human-like articulated manipulator for a specific task of path tracking. For this purpose, the human arm was modeled with a three-link planar manipulator activated by Hill muscle model. Applying a proportional controller, values of force and torques applied to the joints were calculated by inverse dynamics and then joints and muscle forces trajectories were computed and presented. To be more accurate to say, the kinematics of the muscle-joint space was formulated by which we defined the relationship between the muscle lengths and the geometry of the links and joints. Secondary, the kinematic of the links was introduced to calculate the position of the end-effector in terms of the geometry. Then, we considered the modeling of Hill muscle dynamics and after calculation of joint torques, finally, we applied them to the dynamics of the three-link manipulator obtained from the inverse dynamics to calculate the joint states, find and control the location of manipulator’s end-effector. The results show that the human arm model was successfully controlled to take the designated path of an ellipse precisely.

scholarly journals Extended Kalman filtering for state estimation of a Hill muscle model

2018 ◽  
Vol 12 (3) ◽  
pp. 384-394 ◽  
Author(s):  
Hanieh Mohammadi ◽  
Hong Yao ◽  
Gholamreza Khademi ◽  
Thang T. Nguyen ◽  
Dan Simon ◽  
...  
Keyword(s):  
State Estimation ◽  
Kalman Filtering ◽  
Muscle Model ◽  
Extended Kalman Filtering ◽  
Hill Muscle Model

scholarly journals Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1185 ◽  
Author(s):  
Baoping Xiong ◽  
Nianyin Zeng ◽  
Yurong Li ◽  
Min Du ◽  
Meilan Huang ◽  
...  
Keyword(s):  
Joint Moment ◽  
Muscle Model ◽  
Ann Model ◽  
Joint Angles ◽  
Joint Moments ◽  
Angular Velocities ◽  
Input Variables ◽  
The Hill ◽  
Human Joint ◽  
Hill Muscle Model

Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. In this method, the muscle activation, muscle-tendon moment velocity and length in the Hill muscle model and muscle-tendon moment arm are translated to the online measurable variables, i.e., muscle electromyography (EMG), joint angles and angular velocities of the muscle span. To test the predictive ability of these input variables, an ANN model is designed and trained to predict joint moments. The ANN model with the online measurable input variables is tested on the experimental data collected from ten healthy subjects running with the speeds of 2, 3, 4 and 5 m/s on a treadmill. The variance accounted for (VAF) between the predicted and inverse dynamics moment is used to evaluate the prediction accuracy. Results: The results suggested that the method can predict joint moments with a higher accuracy (mean VAF = 89.67±5.56 %) than those obtained by using other joint angles and angular velocities as inputs (mean VAF = 86.27±6.6%) evaluated by jack-knife cross-validation. Conclusions: The proposed method provides us with a powerful tool to predict joint moment based on online measurable variables, which establishes the theoretical basis for optimizing the input sensors and detection complexity of the prediction system. It may facilitate the research on exoskeleton robot control and real-time gait analysis in motor rehabilitation.

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