Development of 3D Equipment Interaction With Predictive Dynamics in Human Motion Simulation

2016 ◽  
Author(s):  
Hyun-Joon Chung ◽  
Yujiang Xiang
Keyword(s):  
Human Body ◽  
Degrees Of Freedom ◽  
Reaction Force ◽  
Human Motion ◽  
The Body ◽  
Joint Torque ◽  
Human Model ◽  
Motion Simulation ◽  
Predictive Dynamics ◽  
Human Motion Simulation

3D equipment interaction module in human motion simulation is developed in this paper. A predictive dynamics method is used to simulate human motion, and a helmet is modeled as the equipment that is attached to the human body. We then implement this method using the predictive dynamics task of walking. A mass-spring-damper system is attached at the top of the head as a helmet model. The equations of motion for the helmet are also derived in a recursive Lagrangian formulation within the same inertial reference frame as the human model’s. The total number of degrees of freedom for the human model is 55 — 6 degrees of freedom for global translation and rotation, and 49 degrees of freedom for the body. The helmet has 7 degrees of freedom, but 6 of them are dependent to the human model. The movement of the helmet is analyzed due to the human motion. Then, the reaction force between the human body and the equipment is calculated. Once the reaction force is obtained, it is applied to the human body as an external force in the predictive dynamics optimization process. Results include the motion of equipment, the force acting on body at the attachment point, the joint torque profiles, and the ground reaction force profiles at the foot contacting point.

Real-Time Motion Capture for a Human Body using Accelerometers

Robotica ◽  
2001 ◽  
Vol 19 (6) ◽  
pp. 601-610 ◽  
Author(s):  
Jihong Lee ◽  
Insoo Ha
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Human Body ◽  
Degrees Of Freedom ◽  
Low Cost ◽  
Human Motion ◽  
The Body ◽  
Sensor Data ◽  
Time Motion ◽  
Gravity Acceleration

In this paper we propose a set of techniques for a real-time motion capture of a human body. The proposed motion capture system is based on low cost accelerometers, and is capable of identifying the body configuration by extracting gravity-related terms from the sensor data. One sensor unit is composed of 3 accelerometers arranged orthogonally to each other, and is capable of identifying 2 rotating angles of joints with 2 degrees of freedom. A geometric fusion technique is applied to cope with the uncertainty of sensor data. A practical calibration technique is also proposed to handle errors in aligning the sensing axis to the coordination axis. In the case where motion acceleration is not negligible compared with gravity acceleration, a compensation technique to extract gravity acceleration from the sensor data is proposed. Experimental results not only for individual techniques but also for human motion capturing with graphics are included.

Predictive dynamics: an optimization-based novel approach for human motion simulation

2009 ◽  
Vol 41 (3) ◽  
pp. 465-479 ◽  
Author(s):  
Yujiang Xiang ◽  
Hyun-Joon Chung ◽  
Joo H. Kim ◽  
Rajankumar Bhatt ◽  
Salam Rahmatalla ◽  
...  
Keyword(s):  
Human Motion ◽  
Motion Simulation ◽  
Predictive Dynamics ◽  
Novel Approach ◽  
Human Motion Simulation

Effect of Foot Location on Human Running Prediction

2014 ◽  
Author(s):  
Hyun-Joon Chung ◽  
Rajan Bhatt ◽  
Yujiang Xiang ◽  
Jasbir S. Arora ◽  
Karim Abdel-Malek
Keyword(s):  
Degrees Of Freedom ◽  
Reaction Force ◽  
Performance Measure ◽  
Joint Torque ◽  
Upper Body ◽  
Human Model ◽  
Digital Human Model ◽  
Digital Human ◽  
Dynamics Method ◽  
Human Running

Human running is simulated in this work by using a skeletal digital human model with 55 degrees of freedom (DOFs). A predictive dynamics method is used to formulate the running problem, and normal running is formulated as a symmetric and cyclic motion. The dynamic effort and impulse are used as the performance measure, and the upper body yawing moment is also included in the performance measure. The joint angle profiles and joint torque profiles are calculated for the full-body human model, and the ground reaction force is determined. The effect of foot location on the running motion prediction are simulated and studied.

Balance Maintaining by Human

2016 ◽  
Vol 248 ◽  
pp. 155-160
Author(s):  
Andrzej Kot ◽  
Agata Nawrocka
Keyword(s):  
Nervous System ◽  
Human Body ◽  
Degrees Of Freedom ◽  
Inverted Pendulum ◽  
Balance Control ◽  
Human Motion ◽  
The Body ◽  
Posture Control ◽  
Body Parts ◽  
Receptor System

Harmonious cooperation of the skeletal, muscular and nervous systems, forming a human motion organ, is responsible for all undertaken movement activities. Motion organ in the illustrated embodiment responsible not only for two basic motion activities, locomotion and manipulation, but also for maintaining the posture of the human body. Standing posture control makes a particular dimension of physical activity, because correct, stable posture determines the ability to perform most human movements. In the case of a man to maintain a balance in a standing position seems to be something obvious and does not require much effort, but with the advent of lesions or aging we begin to see how complex it is the process of balance control. The changes lead to impaired balance control which in turn can lead to the appearance of postural instability and in extreme circumstances, even to collapse. Maintaining a stable posture it is primarily associated with motor control provided by the human nervous system. The nervous system acts as an posture control system and most of all giving to a body well-defined silhouette. This control relies heavily on the integration of information from the human receptor system. Muscle, joint, tendon and skin receptors communicate first to the brain information about the movement and position of individual body parts and then feedback these signals to the muscles, causing reflex reactions allowing for correction of posture and thus return the center of gravity to a position that maintaining equilibrium. Subdivide those human body into segments linked closely with the system osteoarthritis limbs and trunk can create a system of interconnected pendulums with many degrees of freedom. In the case of standing it will be largely complicated inverted pendulums system by which activities phenomena associated with maintaining balance and locomotion can be modeled. If additionally in an upright position, taking into account the natural motion restrictions movements in all joints except the ankles will be blocked, the body will be a close approximation behave like a rigid body. So we can assume that for supporting the human body at the ankle, it will behave like an inverted pendulum. The article presents the ways of describing the equilibrium of man as an inverted pendulum.

The Hybrid Predictive Dynamics Method for Analysis, Simulation and Prediction of Human Motion

2016 ◽  
Author(s):  
Mahdiar Hariri
Keyword(s):  
Motion Capture ◽  
Degrees Of Freedom ◽  
Human Motion ◽  
Human Model ◽  
Coordinate Frame ◽  
Optimization Approach ◽  
Predictive Dynamics ◽  
Digital Human Model ◽  
Digital Human ◽  
Dynamics Method

The ‘Hybrid Predictive Dynamics Method for Digital Human Modeling’ is analyzed in this work. The ‘Hybrid’ prefix mentioned in the literature recently [1], refers to the use of motion capture data for improving human motion simulations. This use of motion capture compensates for the inherent weaknesses of purely theoretical motion prediction due to deficiencies in computational power or available theoretical backgrounds. In this work, it is shown that while using the ‘Hybrid’ the more precisely and finely the human motion is modeled (if computational and theoretical limitations allow), the less will be the need for the ‘Hybrid’ method and the more will the human model be able to change the prediction if the inputs are varied (cause and effect). Several human motion scenarios are mentioned in this work. These motion tasks are: “Jogging around Markers”, “Rolling Over”, “Getting up from Prone”, “Vertical Jumping” and “Kneeling and Aiming”. The digital human model is a full-body, three dimensional model with 55 degrees of freedom. Six degrees of freedom specify the global position and orientation of the coordinate frame attached to the pelvic point of the digital human and 49 degrees of freedom represent the revolute joints which model the human joints and determine the kinematics of the entire digital human. Motion is generated by a multi-objective optimization approach. The optimization problem is subject to constraints which represent the limitations of the environment, the digital human model and the motion task. Design variables are the joint angle profiles. All the forces, inertial, gravitational as well as external, are known, except the ground reaction forces. The feasibility of the generation of that arbitrary motion by using the given ground contact areas is ensured by using the well-known Zero Moment Point (ZMP) constraint.

Posture prediction and physics-based human motion simulation

2019 ◽  
pp. 425-440
Author(s):  
Rajan Bhatt ◽  
Kimberly Farrell ◽  
Karim Abdel-Malek ◽  
Jasbir Arora ◽  
Chris Murphy
Keyword(s):  
Human Motion ◽  
Motion Simulation ◽  
Human Motion Simulation ◽  
Posture Prediction

Human Carrying Simulation With Symmetric and Asymmetric Loads

2012 ◽  
Author(s):  
Yujiang Xiang ◽  
Jasbir S. Arora ◽  
Salam Rahmatalla ◽  
Hyun-Joon Chung ◽  
Rajan Bhatt ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Human Motion ◽  
Human Model ◽  
Physical Constraints ◽  
Digital Human Model ◽  
Load Carrying ◽  
Reaction Forces ◽  
Asymmetric Load ◽  
Optimization Schemes ◽  
Digital Human

Human carrying is simulated in this work by using a skeletal digital human model with 55 degrees of freedom (DOFs). Predictive dynamics approach is used to predict the carrying motion with symmetric and asymmetric loads. In this process, the model predicts joints dynamics using optimization schemes and task-based physical constraints. The results indicated that the model can realistically match human motion and ground reaction forces data during symmetric and asymmetric load carrying task. With such prediction capability the model could be used for biomedical and ergonomic studies.

scholarly journals Jumping in frogs: assessing the design of the skeletal system by anatomically realistic modeling and forward dynamic simulation

2002 ◽  
Vol 205 (12) ◽  
pp. 1683-1702 ◽  
Author(s):  
William J. Kargo ◽  
Frank Nelson ◽  
Lawrence C. Rome
Keyword(s):  
Degrees Of Freedom ◽  
The Body ◽  
Joint Torque ◽  
Rotational Degree ◽  
Skeletal System ◽  
Dynamic Simulations ◽  
Inverse Dynamic ◽  
Maximal Distance ◽  
Out Of Plane ◽  
Rotational Degrees Of Freedom

SUMMARY Comparative musculoskeletal modeling represents a tool to understand better how motor system parameters are fine-tuned for specific behaviors. Frog jumping is a behavior in which the physical properties of the body and musculotendon actuators may have evolved specifically to extend the limits of performance. Little is known about how the joints of the frog contribute to and limit jumping performance. To address these issues, we developed a skeletal model of the frog Rana pipiens that contained realistic bones, joints and body-segment properties. We performed forward dynamic simulations of jumping to determine the minimal number of joint degrees of freedom required to produce maximal-distance jumps and to produce jumps of varied take-off angles. The forward dynamics of the models was driven with joint torque patterns determined from inverse dynamic analysis of jumping in experimental frogs. When the joints were constrained to rotate in the extension—flexion plane, the simulations produced short jumps with a fixed angle of take-off. We found that, to produce maximal-distance jumping,the skeletal system of the frog must minimally include a gimbal joint at the hip (three rotational degrees of freedom), a universal Hooke's joint at the knee (two rotational degrees of freedom) and pin joints at the ankle,tarsometatarsal, metatarsophalangeal and iliosacral joints (one rotational degree of freedom). One of the knee degrees of freedom represented a unique kinematic mechanism (internal rotation about the long axis of the tibiofibula)and played a crucial role in bringing the feet under the body so that maximal jump distances could be attained. Finally, the out-of-plane degrees of freedom were found to be essential to enable the frog to alter the angle of take-off and thereby permit flexible neuromotor control. The results of this study form a foundation upon which additional model subsystems (e.g. musculotendon and neural) can be added to test the integrative action of the neuromusculoskeletal system during frog jumping.

Parametric Human Body Model for Digital Apparel Design

2013 ◽  
Vol 365-366 ◽  
pp. 121-124
Author(s):  
Shu Xia Wang ◽  
Sheng Feng Qin ◽  
Cong Ying Guan ◽  
Sui Huai Yu
Keyword(s):  
Cross Section ◽  
Human Body ◽  
Body Shape ◽  
Body Part ◽  
The Body ◽  
Human Model ◽  
Body Model ◽  
Key Points ◽  
Cloud Points ◽  
Body Scanner

With the advance in 3D body scanning technology, it opens opportunities for virtual try-on and automatic made-to-measure in apparel products domain. This paper proposed a novel feature-based parametric method of human body shape from the cloud points of 3D body scanner [T2. Firstly, we improved the skeleton construction through adding and adjusting the position of joints. Secondly, automatic extraction approach of semantic feature cross-sections is developed based on the hierarchy. According to the unique distribution of cloud points of each cross-section of each body part, the extraction method of key points on the cross-section is described. Thirdly, we presented an interpolation approach of key points which fit cardinal spline to cross-section for each body part, in which tension parameter is used to represent the simple deformation of body shape. Finally, a connection approach of body part is proposed by sharing a boundary curve. The proposed method has been tested with our virtual human model (VHM) system which is robust and easier to use. The process generally requires about five minutes for generating a full body model that represents the body shape captured by 3D body scanner. The model can be imported in a CAD environment for application to a wide variety of ergonomic analyses.

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