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International WorkshopAdvanced Researches in Computational Mechanics and
Virtual Engineering 18 – 20 October 2006, Brasov, Romania
SIMULATION OF THE HUMAN WAKING FOR BIOMEDICAL REHABILITATION PROCESS
Mihaela Ioana Baritz1, Ileana Rosca1, Luciana Cristea1, Diana Cotoros1 , Sorin Vlase1 1 Transilvania University Brasov, Romania,
[email protected] , [email protected] , [email protected] , [email protected] , [email protected]
Abstract: This paper presents the activities of our team to study and to establish a configuration for biomedical applications of a human body motion modeling. It is also presented a constraint-based methodology for reconstructing the 3D motion given image observations, and use this as a tool for understanding the problem of different patients with medical moving disabilities.Keywords : Modeling, control, motion capture, human body
1. INTRODUCTION
In this part of the paper we consider very important and necessary to establish and to present some considerations about movement, like classification and analyses the movement of the human body or different shape and parts of human body. Movement classification is the process of classifying and recognizing observed and recorded motion. If it is used the human visual system for observations, this process plays an important role. For instance, humans can recognize others by his/her gait, hand gestures and other complex sequences of motion. There are two methods used to analyze motion in this moment. The first method, Structure From Motion (SFM) tries to build structure from a series of frame, resulting in a time set of coordinates of points found in these frames. This derived structure is then used for classification and recognition. Unfortunately a lot of other useful and sometimes vital cues for recognizing motion are disregarded in this way, like optical flow and textures. The second method does not look at one frame at a time, but uses sequences of frames to extract motion information in its continuum. This way it can handle higher forms of motion and can classify more complex and organized movement, like walking or running. When performing movement classification, or motion recognition, there are some steps to follow. First a representation must be chosen for the objects or motions we want to model, from the motion cues of the image sequence. These can be simple, using only direct motion cues, or can be complex, using higher forms of representation. Secondly, the determined type of representation is used to build up models of the movement that is to be classified. Again, these models can be very simple, describing simple forms of movement, or very complex, modeling whole strategies or series of actions.Another principle of modeling and simulation establishes that biomechanical approach to movement analysis can be qualitative, with movement observed and described, or quantitative, meaning that some aspect of the movement will be measured. The use of the term biomechanics incorporates qualitative components with a more specific quantitative approach. In that approach, the motion characteristics of a human body are described using such parameters as speed and direction, how the motion is created through application of forces both inside and outside the body, and the optimal body positions and actions for efficient, effective motion. Two types of motion are present in a human body movement. First type of motion is linear motion, often named translation or translational motion (linear motion is movement along a straight or curved pathway in which all points on a body move the same distance in the same amount of time). The center of mass of the body or of a segment is usually the point monitored in a linear analysis and the center of mass is the point at which the mass of the object appears to be concentrated, and it represents the point at which the total effect of gravity acts on the object.
Fig.1.
International WorkshopAdvanced Researches in Computational Mechanics and
Virtual Engineering 18 – 20 October 2006, Brasov, Romania
However, any point can be selected and evaluated for linear motion.The second type of motion is angular motion, which is motion around some point so that different regions of the same body segment or object do not move through the same distance in a given amount of time. It is typical in biomechanics to examine the linear motion characteristics of an activity and then follow up with a closer look at the angular motions that create and contribute to the linear motion. All linear movements of the human body occur as a consequence of angular contributions. For that it is important to identify the angular motions and their sequence that make up a skill or human movement, because the angular motions will determine the success or failure of the linear movement and the interpretation of the normal or non-normal human walking.
2.THE MOTION CAPTURE PROBLEM
Motion capture is an attractive method for creating the movement for computer animation or for different technical and medical studies. The goal of motion capture is to record the movement of a “performer” (typically, but not always, human body) in a compact, usable and repetitive manner.
The methods used in computer graphics and computer vision usually abstract the body into a small number of rigid segments that rotate relative to one another. This approximation is crude and simply. Human knees, elbows and ankles do not have a single pivot point. The true motion of more complex joints, such as shoulders, hips, or the neck, are even further from their kinematics approximations. While the skeletal approximation is crude, it is required for tractability and simplifies the modeling and simulation activities. Because some information is necessarily thrown away, modeling and simulation animation techniques must take care to preserve the “essence” of the motion in doing any processing. What makes this particularly challenging is that the important properties of a motion are difficult to identify. The motion capture problem we consider, therefore, must have the following form: given a single stream of video observations of a “performer”, compute a 3D skeletal representation of the motion of sufficient quality to be useful for animation. This ill-defined last clause is the part unique to animation. But for medical studies of the movement of the human body, like in Parkinson disease, are sufficient to use the rigid form of the human body parts.
3. CHALLENGES OF MOTION CAPTURE FOR MODELING AND SIMULATION
Modeling or simulation of human body motion may seem to be easy applications of motion capture as precision is very important and the exact positions, like applications in medical analysis must be correct. For example, when processing errors are distributed amongst several frames, an uncharacteristic motion can be observed in a character’s knees, causing them to move in a disconcerting way that stands out against common movements like walking. In all of these cases, the magnitude of the errors is small, but their visible effects are significant especially when we want to take a decision in medical field. The same quantity of error, occurring at a more fortunate time or manner, would not be a problem for this decision. Since high-frequency noise is often a problem, a common approach to dealing with it is to low-pass filter all the data. This rarely situation has the desired effect: the same reasons that make unwanted high frequencies so obtrusive also make the removal of an important high-frequencies part. Significant high frequencies are often the result of important events in a motion, such a contact, impact, or fast gesture or moving of the some parts of the body. In fact, it is often these specific performances that are the reasons for wanting to perform motion capture in the first place. If we wanted a standard movement, we might use a recorded one from a library or for the ideal human body movement design in another software especially for ideal studies.
International WorkshopAdvanced Researches in Computational Mechanics and
Virtual Engineering 18 – 20 October 2006, Brasov, Romania
More often, like in medical studies, motion capture is required for recording a specific person, a specific situation, or something otherwise nonstandard. We are trying to record something unusual in some way, diseases of human loco-motor system, athletic behavior, rehabilitation of human movement, because predictable and standard movements do not need to be recreated. Virtual humans are articulated figures modeled with multiple layers: a virtual skin is usually attached to an underlying skeleton, which animates the whole body.
The skeleton is a hierarchically organized set of joints, and this set depends on the simulation and animation requirements (fig.2.). Real human bodies have so many degrees of freedom that virtual characters frequently omit some of them.
Fig.2.
International WorkshopAdvanced Researches in Computational Mechanics and
Virtual Engineering 18 – 20 October 2006, Brasov, Romania
The capture problem is sometimes difficult: the articulated model does not accurately reflect the real performer, articulations lead to self-occlusions, even the articulated models contain many degrees of freedom, the skeleton is internal and therefore cannot be observed directly. Additional information is needed to determine the position of a point in space. A variety of sources have been utilized in various computer vision techniques, and a few can be applied to motion reconstruction. Most methods assume strong models to place further restrictions on possible poses. Such assumptions are very difficult for motion capture applications: because we are interested in capturing novel motions, we cannot count on previous motions for cues.
We establish for that a human body structures with an architectural construction having, in the same time, the possibilities to introduce constraints and limitations of displacements and speeds (fig.3.).These initial conditions are important to our studies in Parkinson’s disease and sever head stroke. After these modeling of human body shapes, the motion and direction must be choose taking in account the anthropometric and medical information‘s from each human (weight, disease, age, gender).After modeling human body shapes and motion we obtain a virtual and normal movement of each joints with marks and we can show the normal trajectories of them.
4. CONCLUSION
We assume for now that we know the exact shape of the human body movement in each joints, most important points for the motion. For example, if we want to estimate, measure and control the motion parameters for a human body part, we supply a weight matrix W that defines the image support map of that specific body part, and run this estimation technique for several iterations. Tracking over multiple frames can be achieved by
Fig.3.
Fig.4. Fig.5 .
International WorkshopAdvanced Researches in Computational Mechanics and
Virtual Engineering 18 – 20 October 2006, Brasov, Romania
applying this optimization technique successively over the complete image sequence.In our futures researches work we already establish a configuration to record the movement of different people movement, having different initial conditions (speed, weight, space, number of recordings) and to compare with this modeling and simulation.
Acknowledgements
This work was a part of a Research Grant A1088/2005 financed by CNCSIS Romania.
REFERENCES:
[1] D.Gavrila, Vision-based 3D tracking of humans in action, PhD thesis Dissertation, University of Maryland, 1996;[2] Sander van Dijk, Movement Classification, Artificial Intelligence, 25 oktober 2004;[3] J. Saboune, F. Charpillet, Markerless Human Motion Capture for Gait Analysis, INRIA-LORIA, France, 2004;[4] J.S.Monzani, An architecture for behavioral animation of virtual humans, Ecole Politehnique Federale de Laussanne, Suisse, 2002.
