Abstract
State of the art in the area of image processing and machine learning shows significant advances in the estimation of human posture in 2D and 3D. However, it has not been accurately reported whether the use of these methodologies provides per joint plane of motion information within the range of error of clinical measurements (5º).
The purpose of this work was to select a method for estimating human posture in 3D with a monocular camera from the state of the art and to statistically compare it with clinical measurements in the angular variables.
A discriminative method for estimating human posture was trained with the Human3.6M database, and results were obtained from the performance of the posture estimation model in 3D with a monocular camera. Subsequently, angular variables were obtained by plane of movement per predicted joint.
Our results show that are joints with less than 5º of error in a plane of movements, such as the knee and elbow in the sagittal plane and the wrist in the frontal plane. On the other hand, in other joints such as the hip and ankle, its results are dependent on the action, the time periods of the evaluated actions, and the view of the camera.
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Acknowledgments
The authors acknowledge the support of Agencia Nacional de Investigación y Desarrollo (ANID) grants FONDECYT 1221696, FONDEQUIP EQM210020, as well as PIA Anillo ACT192015.
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Carrasco-Plaza, J., Cerda, M. (2022). Evaluation of Human Pose Estimation in 3D with Monocular Camera for Clinical Application. In: Brito-Loeza, C., Martin-Gonzalez, A., Castañeda-Zeman, V., Safi, A. (eds) Intelligent Computing Systems. ISICS 2022. Communications in Computer and Information Science, vol 1569. Springer, Cham. https://doi.org/10.1007/978-3-030-98457-1_10
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