Abstract
An efficient collaborative work between a person and technical system requires a deeper understanding of the human nature including social, cognitive, emotional or any other relationship that the person could have toward the technical system. Such relationships depend also on the case or specific knowledge about the current task they are performing together. Due to safety reasons, increased variability of new products, flexibility, and demands for defect-resistant production, a contemporary production lack such applications where people and robots operate together using social signals or contextual information.
The new paradigms that connect vision of Industry 4.0 with artificial intelligence, robotics, and computer networks are inevitably starting the new era of emerging ubiquitous production cells that will be used in factories of the future.
Authors propose an addition to a safety framework using a worker intention recognition with head pose information. As an indicator of intentions of a person in everyday communication, besides the experiences that represent a priori knowledge, humans are relying on social signals.
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References
Mollaret, C., Mekonnen, A.A., Ferrané, I., Pinquier, J., Lerasle, F.: Perceiving user's intention-for-interaction: a probabilistic multimodal data fusion scheme. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2015). https://doi.org/10.1109/ICME.2015.7177514
Conte, D., Furukawa, T.: Autonomous robotic escort incorporating motion prediction and human intention. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 3480–3486 (2021). https://doi.org/10.1109/ICRA48506.2021.9561469
Stipancic, T., Koren, L., Korade, D., Rosenberg, D.: PLEA: a social robot with teaching and interacting capabilities. J. Pacific Rim Psychol. 15. (2021). https://doi.org/10.1177/18344909211037019
Koren, L., Stipancic, T., Ricko, A., Orsag, L.: Person localization model based on a fusion of acoustic and visual inputs. Electronics 11(3), 440 (2022). https://doi.org/10.3390/electronics11030440
Stipancic, T., Jerbic, B., Curkovic, P.: A context-aware approach in realization of socially intelligent industrial robots. Robot. Comput. Integrated Manuf. 37, 79–89 (2016). https://doi.org/10.1016/j.rcim.2015.07.002
Matsumoto, Y., Zelinsky, A.: An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement. In: Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), pp. 499–504 (2000). https://doi.org/10.1109/AFGR.2000.840680
Matsumoto, Y., Ogasawara, T., Zelinsky, A.: Behavior recognition based on head pose and gaze direction measurement. In: Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113), vol. 3, pp. 2127–2132 (2000). https://doi.org/10.1109/IROS.2000.895285
Murad, A., Pyun, J.Y.: Deep recurrent neural networks for human activity recognition. Sensors 17(11), 2556 (2017). https://doi.org/10.3390/s17112556
Banos, O., Galvez, J.M., Damas, M., Pomares, H., Rojas, I.: Window size impact in human activity recognition. Sensors 14(4), 6474–6499 (2014). https://doi.org/10.3390/s140406474
Shaikh, M.B., Chai, D.: RGB-D data-based action recognition: a review. Sensors 21(12), 4246 (2021). https://doi.org/10.3390/s21124246
Liu, X., Liang, W., Wang, Y., Li, S., Pei, M.: 3D head pose estimation with convolutional neural network trained on synthetic images. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1289–1293 (2016). https://doi.org/10.1109/ICIP.2016.7532566
Huang, Y., Cui, J., Davoine, F., Zhao, H., Zha, H.: Head pose based intention prediction using Discrete Dynamic Bayesian Network. In: 2013 Seventh International Conference on Distributed Smart Cameras (ICDSC), pp. 1–6 (2013). https://doi.org/10.1109/ICDSC.2013.6778228
Hjelmås, E., Low, B.K.: Face detection: a survey. Comput. Vis. Image Underst. 83(3), 236–274 (2001). https://doi.org/10.1006/cviu.2001.0921
Gogić, I., Ahlberg, J., Pandžić, I.S.: Regression-based methods for face alignment: a survey. Signal Process. 178, 107755 (2021). https://doi.org/10.1016/j.sigpro.2020.107755
Jerbic, B., Stipancic, T., Tomasic, T.: Robotic bodily aware interaction within human environments. In: Proceedings of the SAI Intelligent Systems Conference (IntelliSys 2015), London, UK, 10–11 November 2015. https://doi.org/10.1109/IntelliSys.2015.7361160P
Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. Pattern Recogn. Lett. 119, 3–11 (2019). https://doi.org/10.1016/j.patrec.2018.02.010
Stipancic, T., Jerbic, B.: Self-adaptive vision system. In: Camarinha-Matos, L.M., Pereira, P., Ribeiro, L. (eds.) DoCEIS 2010. IAICT, vol. 314, pp. 195–202. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11628-5_21
Liu, J., Wang, G., Hu, P., Duan, L.Y., Kot, A.C.: Global context-aware attention LSTM networks for 3D action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1647–1656 (2017). https://doi.org/10.1109/cvpr.2017.391
Dallel, M., Havard, V., Baudry, D., Savatier, X.: InHARD - industrial human action recognition dataset in the context of industrial collaborative robotics, Zenodo, 2020-09-30 2020. https://doi.org/10.5281/zenodo.4003541
Bulling, A., Blanke, U., Schiele, B.: A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. 46(3), 1–33 (2014)
Oresti, B.: Window size impact in human activity recognition. G. Juan-Manuel (2014). https://doi.org/10.3390/s140406474
Hu, Y., Huber, A., Anumula, J., Liu, S.C.: Overcoming the vanishing gradient problem in plain recurrent networks. arXiv preprint arXiv:1801.06105 (2018)
Liu, H., Wang, L.: Collision-free human-robot collaboration based on context awareness. Robot. Comput. Integrated Manuf. 67, 101997 (2021)
Acknowledgements
This research is partially supported by Visage Technolgies AB and by the Croa-tian Science Foundation under the project “Affective Multimodal Interaction based on Constructed Robot Cognition—AMICORC (UIP-2020–02-7184)”.
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Orsag, L., Stipancic, T., Koren, L., Posavec, K. (2022). Human Intention Recognition for Safe Robot Action Planning Using Head Pose. In: Kurosu, M., et al. HCI International 2022 - Late Breaking Papers. Multimodality in Advanced Interaction Environments. HCII 2022. Lecture Notes in Computer Science, vol 13519. Springer, Cham. https://doi.org/10.1007/978-3-031-17618-0_23
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