Abstract
Research over gait analysis has become more relevant in the last years, especially as a tool to detect early frailty signs. However, data gathering is often difficult and requires lots of resources. Synthetic data generation is a great complementary tool for data gathering that enables the augmentation of existing datasets. Despite not being a new concept, it has gained popularity in the last years thanks to Generative Adversarial Networks (GANs), a neural network architecture capable of creating data indistinguishable from the original one. In this article deep-convolutional GANs has been used to artificially expand a gait dataset containing data of the lower part of the body. The synthetic data has been studied through three approaches: looking animations of the points and comparing them to the originals; applying principal component analysis algorithm to both datasets to visually assess how each of them is distributed; and by extracting different features from both datasets to compare their statistical differences. The evaluation showed promising results, which opens a path for using synthetic data generation in the gait analysis domain.
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Acknowledgments
We would like to thank all the people who collaborated as participants in this experiment.
Funding
This work was funded by the MINISTERIO DE CIENCIA, INNOVACIÓN Y UNIVERSIDADES, grant number RTI2018-098780-B-I00 (national research project) and EQC2019-006053-P (WeCareLab); the 2022-PRED-20651 predoctoral contract by UNIVERSITY OF CASTILLA-LA MANCHA; and the PREJCCM2019/14 predoctoral contract by JUNTA DE COMUNIDADES DE CASTILLA-LA MANCHA.
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Carneros-Prado, D. et al. (2023). Approach to a Lower Body Gait Generation Model Using a Deep Convolutional Generative Adversarial Network. In: Bravo, J., Ochoa, S., Favela, J. (eds) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). UCAmI 2022. Lecture Notes in Networks and Systems, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-21333-5_42
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