Abstract
In this study we have implemented and validated the Azure Kinect system for the acquisition and analysis of the human kinematics to be applied to the practical clinic for physiotherapy and rehabilitation, as well as in research studies. The progressive increase in the ageing of the world population in the first world countries, increasingly demands the need to find new, more automated, and versatile technological systems for the acquisition and analysis of human movement data that help us to diagnose, track the evolution of the pathologies and determine how our movements influence the development of the musculoskeletal pathologies. In this work, we were able to develop a measurement technology and validate the ability of the system based on Deep Learning (DL) and Convolutional Neural Networks (CNN), to make precise and fast measurements in real-time compared to the gold standard goniometry used by clinicians. Its precision has allowed verifying its validity for the measurement of large body joints.
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Acknowledgements
This project has received funding by grant RTI2018-098969-B-100 from the Spanish Ministerio de Ciencia Innovación y Universidades, by grant PROMETEO/2019/119 from the Generalitat Valenciana (Spain) and by grant agreement No. 899287 (project NeuraViPer), and by the Ayudas a la Investigación from the University Miguel Hernandez (2021).
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Peco, R.L., Ruiz, R.M., Soto-Sánchez, C., Fernández, E. (2022). Development and Validation of a Novel Technology for Postural Analysis and Human Kinematics. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. IWINAC 2022. Lecture Notes in Computer Science, vol 13259. Springer, Cham. https://doi.org/10.1007/978-3-031-06527-9_49
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DOI: https://doi.org/10.1007/978-3-031-06527-9_49
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