Approach to a Lower Body Gait Generation Model Using a Deep Convolutional Generative Adversarial Network-Reference-Cited by-同舟云学术

Approach to a Lower Body Gait Generation Model Using a Deep Convolutional Generative Adversarial Network

Published:2022-11-21 Issue: Volume: Page:419-430
ISSN:2367-3370
Container-title:Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022)
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Author:

Carneros-Prado David^ORCID,Dobrescu Cosmin C.^ORCID,Cabañero Luis^ORCID,Altamirano-Flores Yulith V.^ORCID,Lopez-Nava Irvin Hussein^ORCID,González Iván^ORCID,Fontecha Jesús^ORCID,Hervas Ramon^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-21333-5_42

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3. Iseri, Y., Hata, Y., Yagi, N., Saji, Y., Sakai, Y.: Rehabilitation evaluation with gait analysis mat. In: 2021 World Automation Congress (WAC), pp. 248–253. IEEE, Taipei, Taiwan (2021). https://doi.org/10.23919/WAC50355.2021.9559453

4. Bowman, T., et al.: Wearable devices for biofeedback rehabilitation: a systematic review and meta-analysis to design application rules and estimate the effectiveness on balance and gait outcomes in neurological diseases. Sensors 21, 3444 (2021). https://doi.org/10.3390/s21103444

5. Advances in Intelligent Systems and Computing;A Laranjo,2021

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