Abstract
Synthesizing human movement is useful for most applications where the use of avatars is required. These movements should be as realistic as possible and thus must take into account anthropometric characteristics (weight, height, etc.), gender, and the performance of the activity being developed. The aim of this study is to develop a new methodology based on the combination of principal component analysis and partial least squares regression model that can generate realistic motion from a set of data (gender, anthropometry and performance). A total of 18 volunteer runners have participated in the study. The joint angles of the main body joints were recorded in an experimental study using 3D motion tracking technology. A five-step methodology has been employed to develop a model capable of generating a realistic running motion. The described model has been validated for running motion, showing a highly realistic motion which fits properly with the real movements measured. The described methodology could be applied to synthesize any type of motion: walking, going up and down stairs, etc. In future work, we want to integrate the motion in realistic body shapes, generated with a similar methodology and from the same simple original data.
Highlights
It is well known that there is a large degree of information contained in the kinematics of a moving body which is influenced by parameters such as: gender, age, anthropometrical features, emotional state, personality traits, etc. (Troje, 2008)
To validate the five-step methodology described to develop the bio-motion generator we propose a comparison between each recorded observation and the prediction of running motion generated by the model by means of the ‘leave-one-out’ procedure
The method described in this article has been developed and validated for running motion, but this same methodology could be used to synthesize other types of motion: walking, going up and down stairs, or even for sport movements such as: jumping, pedalling, golf swing and putting, etc
Summary
It is well known that there is a large degree of information contained in the kinematics of a moving body which is influenced by parameters such as: gender, age, anthropometrical features, emotional state, personality traits, etc. (Troje, 2008). It is well known that there is a large degree of information contained in the kinematics of a moving body which is influenced by parameters such as: gender, age, anthropometrical features, emotional state, personality traits, etc. A number of studies demonstrate the capability of the human visual system to detect, recognize and interpret the information encoded in the biological motion (Johansson, 1973). There are many attempts to analyse this information encrypted in human motion. (Dvorak et al, 1992) Others focus their studies on the sequence of movement along time instead of recording simple parameters. In these cases, they analyse the complete function of time f (t ) (Feipel et al, 1999). A number of kinematical models are based on frequency domain manipulations (Davis, Bobick & Richards, 2000) and multiresolution filtering (Bruderlin & Williams, 1995)
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