scholarly journals Maximum voluntary joint torque as a function of joint angle and angular velocity: Model development and application to the lower limb

2007 ◽  
Vol 40 (14) ◽  
pp. 3105-3113 ◽  
Author(s):  
Dennis E. Anderson ◽  
Michael L. Madigan ◽  
Maury A. Nussbaum
Keyword(s):  
Angular Velocity ◽  
Lower Limb ◽  
Joint Angle ◽  
Model Development ◽  
Velocity Model ◽  
Joint Torque

Automotive Side Glazing Ejection Mechanisms: Part I — Model Development

2008 ◽  
Author(s):  
Stephen A. Batzer ◽  
G. Grant Herndon ◽  
Paul T. Semones ◽  
Chandrashekar K. Thorbole ◽  
Mariusz Ziejewski ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Experimental Validation ◽  
Rotational Velocity ◽  
Model Development ◽  
Velocity Model ◽  
Vehicle Body ◽  
Rotational Acceleration ◽  
Centrifugal Acceleration ◽  
Low Velocity ◽  
Window Design

The mechanisms of occupant ejection through automotive side glazing during rollover collisions are analyzed. It is shown that partial or complete ejection can occur through centrifugal acceleration based motion through an open portal, or due to the changes in velocity (ΔVs) developed during corner impacts. Aspects of vehicle kinematics, effective mass, impact velocity, window design, rotational velocity, and injury are examined. Analysis indicates that the dominant ejection mode is rotational acceleration induced exit motion at a low velocity relative to the center of gravity of the vehicle facilitated by vehicle body flexure based fracture of tempered side glass. In this first part of a two paper series, a new rollover angular velocity model is presented and given experimental validation, and the concept of the two ideal ejection modes is developed.

scholarly journals Joint torque and velocity optimization for a redundant leg of quadruped robot

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773189 ◽  
Author(s):  
Taihui Zhang ◽  
Honglei An ◽  
Hongxu Ma
Keyword(s):  
Angular Velocity ◽  
Sagittal Plane ◽  
Load Capacity ◽  
Optimization Criterion ◽  
Quadruped Robot ◽  
Joint Torque ◽  
Quadratic Program ◽  
Hydraulic Actuator ◽  
Joint Torques ◽  
Physical Limitations

Hydraulic actuated quadruped robot similar to BigDog has two primary performance requirements, load capacity and walking speed, so that it is necessary to balance joint torque and joint velocity when designing the dimension of single leg and controlling its motion. On the one hand, because there are three joints per leg on sagittal plane, it is necessary to firstly optimize the distribution of torque and angular velocity of every joint on the basis of their different requirements. On the other hand, because the performance of hydraulic actuator is limited, it is significant to keep the joint torque and angular velocity in actuator physical limitations. Therefore, it is essential to balance the joint torque and angular velocity which have negative correlation under the condition of constant power of the hydraulic actuator. The main purpose of this article is to optimize the distribution of joint torques and velocity of a redundant single leg with joint physical limitations. Firstly, a modified optimization criterion combining joint torques with angular velocity that takes both support phase and flight phase into account is proposed, and then the modified optimization criterion is converted into a normal quadratic programming problem. A kind of recurrent neural network is used to solve the quadratic program problem. This method avoids tremendous matrix inversion and fits for time-varying system. The achieved optimized distribution of joint torques and velocity is useful for aiding mechanical design and the following motion control. Simulation results presented in this article confirm the efficiency of this optimization algorithm.

Design Human-Robot Collaborative Lifting Task Using Optimization

2021 ◽  
Author(s):  
Asif Arefeen ◽  
Yujiang Xiang
Keyword(s):  
Degrees Of Freedom ◽  
Joint Angle ◽  
Joint Torque ◽  
Robotic Arm ◽  
Inverse Dynamic ◽  
Floating Base ◽  
Grasping Forces ◽  
Design Variables ◽  
Human Arm ◽  
Lifting Task

Abstract In this paper, an optimization-based dynamic modeling method is used for human-robot lifting motion prediction. The three-dimensional (3D) human arm model has 13 degrees of freedom (DOFs) and the 3D robotic arm (Sawyer robotic arm) has 10 DOFs. The human arm and robotic arm are built in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The interactions between human arm and box, and robot and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. The inverse dynamic optimization is used to simulate the lifting motion where the summation of joint torque squares of human arm is minimized subjected to physical and task constraints. The design variables are control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box, and the box grasping forces at each time point. A numerical example is simulated for huma-robot lifting with a 10 Kg box. The human and robotic arms’ joint angle, joint torque, and grasping force profiles are reported. These optimal outputs can be used as references to control the human-robot collaborative lifting task.

Gyroscope-Free Link Parameter Measurement Using Accelerometers and Magnetometer

2014 ◽  
Author(s):  
Vishesh Vikas ◽  
Carl D. Crane
Keyword(s):  
Angular Velocity ◽  
Joint Angle ◽  
Inertial Sensors ◽  
Angular Acceleration ◽  
Parameter Measurement ◽  
Joint Angles ◽  
Symmetry Constraint ◽  
Estimation Techniques ◽  
Angular Velocities ◽  
Joint Parameters

Knowledge of joint angles, angular velocities is essential for control of link mechanisms and robots. The estimation of joint angles and angular velocity is performed using combination of inertial sensors (accelerometers and gyroscopes) which are contactless and flexible at point of application. Different estimation techniques are used to fuse data from different inertial sensors. Bio-inspired sensors using symmetrically placed multiple inertial sensors are capable of instantaneously measuring joint parameters (joint angle, angular velocities and angular acceleration) without use of any estimation techniques. Calibration of inertial sensors is easier and more reliable for accelerometers as compared to gyroscopes. The research presents gyroscope-less, multiple accelerometer and magnetometer based sensors capable of measuring (not estimating) joint parameters. The contribution of the improved sensor are four-fold. Firstly, the inertial sensors are devoid of symmetry constraint unlike the previously researched bio-inspired sensors. However, the accelerometer are non-coplanarly placed. Secondly, the accelerometer-magnetometer combination sensor allows for calculation of a unique rotation matrix between two link joined by any kind of joint. Thirdly, the sensors are easier to calibrate as they consist only of accelerometers. Finally, the sensors allow for calculation of angular velocity and angular acceleration without use of gyroscopes.

scholarly journals Can the use of proprioceptive knee braces have implications in the management of osteoarthritic knees: An exploratory study

2018 ◽  
Vol 43 (2) ◽  
pp. 140-147 ◽  
Author(s):  
Tariq A Kwaees ◽  
Jim Richards ◽  
Gill Rawlinson ◽  
Charalambos Panayiotou Charalambous ◽  
Ambreen Chohan
Keyword(s):  
Angular Velocity ◽  
Knee Osteoarthritis ◽  
Internal Rotation ◽  
Lower Limb ◽  
Knee Injury ◽  
Neuromuscular Control ◽  
Outcome Score ◽  
Knee Braces ◽  
Step Down ◽  
Healthy Participants

Background: Use of proprioceptive knee braces to control symptomology by altering neuromuscular control mechanisms has been shown in patellofemoral pain. Although their potential in patients with knee osteoarthritis is vast, little research has examined their efficacy. Objectives: This study examines the effect of a proprioceptive knee brace on lower limb kinematics and kinetics in healthy participants and in participants with OA. Methods: Thirteen healthy participants were asked to perform a 10-cm step-down task with and without a proprioceptive brace. Data were collected using a 10-camera Qualisys system. Individuals with osteoarthritis completed the Knee injury and Osteoarthritis Outcome Score before and after 4 weeks of intervention. Results: During step-down reductions in knee maximum internal rotation, transverse range of movement, transverse plane angular velocity and maximum internal rotation angular velocity was seen. Ankle plantar flexion and inversion angular velocity decreased while inversion and maximum supination angular velocity increased. Improvements in Knee injury and Osteoarthritis Outcome Score were noted across all parameters with brace use. Conclusion: Positive changes in kinematic variables in multiple planes can be achieved with proprioceptive bracing alongside improved patient outcome. These changes occur at the knee but analysis of other weight bearing joints should not be overlooked in future studies. This study supports the concept of neuromuscular reinforcement and re-education through proprioceptive bracing and its application in the management in knee osteoarthritis. Clinical relevance Proprioception can alter symptoms and biomechanics embraced and adjacent lower limb joints. The results of this study highlights the potential uses of non-mechanical bracing in the treatment of osteoarthritis and other potential to bridge the osteoarthritis treatment gap. Furthermore, large-scale research is needed to match disease subset to brace type.

scholarly journals Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6533
Author(s):  
Xinxin Li ◽  
Zuojun Liu ◽  
Xinzhi Gao ◽  
Jie Zhang
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Knee Joint ◽  
Lower Limb ◽  
Joint Angle ◽  
Support Vector ◽  
Swarm Optimization ◽  
Knee Joint Angle ◽  
Phase Recognition ◽  
Lower Limb Amputees

A novel method for recognizing the phases in bicycling of lower limb amputees using support vector machine (SVM) optimized by particle swarm optimization (PSO) is proposed in this paper. The method is essential for enhanced prosthetic knee joint control for lower limb amputees in carrying out bicycling activity. Some wireless wearable accelerometers and a knee joint angle sensor are installed in the prosthesis to obtain data on the knee joint and ankle joint horizontal, vertical acceleration signal and knee joint angle. In order to overcome the problem of high noise content in the collected data, a soft-hard threshold filter was used to remove the noise caused by the vibration. The filtered information is then used to extract the multi-dimensional feature vector for the training of SVM for performing bicycling phase recognition. The SVM is optimized by PSO to enhance its classification accuracy. The recognition accuracy of the PSO-SVM classification model on testing data is 93%, which is much higher than those of BP, SVM and PSO-BP classification models.

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