Motion Capture Research:

Validation Work

The VSR research team uses optimization algorithms and techniques to predict motion and various performance of a virtual human model (Santos™). The human body is modeled as a kinematics system represented by a series of segments connected by joints which represent musculoskeletal joints such as the wrist, elbow, shoulder, clavicle, pelvis, etc. Optimization tools are used to determine the rotation at each degree of freedom of each joint that minimize a performance measure such as discomfort.

The VSR research team has built a skeletal structure for Santos^TM inside the Vicon motion capture system (Figure 2).  This model is similar to the analytical model in terms of the number of segments and joints. Eight cameras Vicon motion capture system is used to track the motion of real subject performing various types of motion. The acquired motion capture data from human subjects is then used to validate the analysis behind Santos^TM motion and performance measures.  Figure 3 demonstrates an application when motion capture data is used to animate Santos^TM skeletal model inside Virtools environment and then compares this results with that obtained form the analysis model; such experiments, will facilitate the process of enhancing Santos^TM prediction capability, also, it makes it possible to choose the right combination of objective functions to get realistic motion.

Verifying the Hand Model for Santos^TM

The VSR research team has built a skeletal structure for Santos^TM‘s hand (Figure 4) inside the Vicon motion capture system. This model is used to validate and test the analysis behind Santos ^TM‘s hand model. A preliminary study has been conducted toward this end and the ongoing and near future research is to use motion capture system to collect data for people with different anthropometry, gender, race, etc.

Validating Santos^TM Dynamics Model

A number of preliminary and ongoing tests are conducted in the motion capture research to verify the dynamic stability formulas behind the virtual human model (Santos^TM). In these tests, the research team is working to obtain stability parameters using a realistic human motion capture data and then compare these data with those predicted by the analysis and optimization modules.

One example of these tests is to track the motion of a person holding or pushing heavy loads (Figure 5). In this regard, the resulting gait parameters from the motion capture system, such as the joints angle, are used as input to the dynamics stability criterion to find the location of the Zero Moment Point (ZMP) and then compare these results with that predicted using the stability analysis model.

 Vibration and Impact Research

The objective of this research is to obtain and analyze motion data and select muscle activity for the upper and lower extremities of human subjects under whole body vibration scenarios (Figure 6). The scenarios will simulate riding conditions for selected heavy construction machines.

Our primary interest is in developing new technologies for human modeling and simulation, with the objective of creating a digital human (named Santos^TM) that can be deployed into virtual mockups of equipment, vehicles, and weapon systems to test for safety and human-factors issues.   The equipment requested consists of a shaker table (Figure 7) to replicate field vibration environments.  A person is placed on the device and exposed to controlled vibration; and a variety of motion analysis and/or human factors measures are monitored. This information can then be used to advance predictions by Santos^TM, subjected to identical vibration in the virtual environment.  

Vicon motion capture system will be used to collect motion data from reflected markers attached to bony prominences on each subject’s body.  Electromyography (EMG) will be used to acquire muscle activity data for three selected muscles: the upper trapezius, biceps brachii, and triceps brachii muscles. These measures will enable us to measure resting joint position and selected muscle activity in each desired posture, as well as determine how simulated work environment vibration is transmitted through the body and its effects on muscle activity.  The data will be analyzed to obtain significant information such as the displacement, velocity, and acceleration of selected points on the lower and upper limbs, degree of vibratory motion transmitted at various anatomical locations, agonist/antagonist muscle activity, etc.

Egress and Ingress Research

The objective of this project is to create a methodology for evaluating ingress/egress to a vehicle that is aided by digital human modeling and simulation technology.   Currently, substantial progress has been made on developing a novel process by which complex tasks can be simulated based on task-segmentation and modeling of various types of motion that compose the more complete task.

In task segmentation, given a specified task and captured through motion capture, the aim of this module is to enable the task segmentation and execution through kinematic representation of the motion.   This effort will leverage significant contributions from the Santos environment where posture and motion predictions have been developed. This module is proposed to work in tandem with the clothing model calculated effects (restrictions, strength, weight, etc) as well as the biomechanical model in terms of force, torque, energy, fatigue, and other quantifiable performance measures.

Selected references:
a. S. Rahmatalla; H. Kim; M. Shanahan; C.C. Swan,” Effect of Restrictive Clothing on Balance and Gait using Motion Capture and Dynamic Analysis”, SAE Digital Human Modeling for Design and Engineering Conference, 14-16 June, Iowa City, Iowa, 2005.

b R. T. Marler; S. Rahmatalla; M. Shanahan; K. Malek,” A New Discomfort Function for Optimization-Based Posture Prediction”, SAE Digital Human Modeling for Design and Engineering Conference, June 14-16, Iowa City, Iowa, 2005.

c. Farrell, K., Marler, R. T., and Abdel-Malek, K., "Modeling Dual-Arm Coordination for Posture: An Optimization-Based Approach", SAE Human Modeling for Design and Engineering Conference, June 14-16, Iowa City, Iowa, 2005.

d. Pena Pitarch, E., Yang, J., and Abdel-Malek, K., “Santos^TM hand: A 25-Degree-of-Freedom Model,” Proceedings of SAE Digital Human Modeling for Design and Engineering, June 14-16, Iowa City, Iowa, 2005. 

Motion Capture Lab at VSR

(a) predicted posture using the analysis model
   (b) predicted posture using motion capture