A Multimodal Dataset to Create Manufacturing Digital Twins
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-42536-3_16
Reference21 articles.
1. Cicirelli, G., et al.: The HA4M dataset: multi-modal monitoring of an assembly task for human action recognition in manufacturing. Sci. Data 9 (2022)
2. Shinde, S., Kothari, A., Gupta, V.: YOLO based human action recognition and localization. Procedia Comput. Sci. 133, 831–838 (2018)
3. Voronin, V., Zhdanova, M., Zelenskii, A., Agaian, S.: Action recognition for the robotics and manufacturing automation using 3-D binary micro-block difference. Int. J. Adv. Manuf. Technol. (2021)
4. Koch, J., Büsch, L., Gomse, M., Schüppstuhl, T.: A methods-time-measurement based approach to enable action recognition for multi-variant assembly in human-robot collaboration. Procedia CIRP 106, 233–238 (2022). https://doi.org/10.1016/j.procir.2022.02.184
5. Dallel, M., Havard, V., Dupuis, Y., Baudry, D.: Digital twin of an industrial workstation: a novel method of an auto-labeled data generator using virtual reality for human action recognition in the context of human-robot collaboration. Eng. Appl. Artif. Intell. 118, 105655 (2023). https://doi.org/10.1016/j.engappai.2022.105655
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