Monitoring Human Performance Through Deep Learning and Computer Vision in Industry 4.0-Reference-Cited by-同舟云学术

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Monitoring Human Performance Through Deep Learning and Computer Vision in Industry 4.0

Published:2022-10-12 Issue: Volume: Page:309-318
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:
Short-container-title:

Author:

Alfaro-Viquez David,Zamora-Hernandez Mauricio-Andres,Benavent-Lledo Manuel,Garcia-Rodriguez Jose,Azorín-López Jorge

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-18050-7_30

Reference27 articles.

1. Al-Faris, M., Chiverton, J., Ndzi, D., Ahmed, A.I.: A review on computer vision-based methods for human action recognition. J. Imaging 6(6), 46 (2020). https://doi.org/10.3390/jimaging6060046

2. Ansari, S., Naghdy, F., Du, H., Pahnwar, Y.N.: Driver mental fatigue detection based on head posture using new modified reLU-BiLSTM deep neural network. IEEE Trans. Intell. Transp. Syst. 23(8), 10957–10969 (2021)

3. Azorin-Lopez, J., Saval-Calvo, M., Fuster-Guillo, A., Garcia-Rodriguez, J.: A novel prediction method for early recognition of global human behaviour in image sequences. Neural Process. Lett. 43(2), 363–387 (2015). https://doi.org/10.1007/s11063-015-9412-y

4. Advances in Intelligent Systems and Computing;ISM Fernández,2022

5. Gellert, A., Sorostinean, R., Pirvu, B.C.: Robust assembly assistance using informed tree search with Markov chains. Sensors 22(2), 495 (2022). https://doi.org/10.3390/s22020495

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