Abstract
In this paper, we propose a method based on machine learning, which estimates the mass of an object from a body motion performed to lift it. In the field of behavior recognition and prediction, some previous studies had focused on estimating the current or future state of a person from his/her motion. In contrast, this research estimates the information of an object in contact with a person. Using this method, we can obtain a rough estimate of an object’s mass without using a weighing machine. Such a measurement system will be useful in several applications, for example, for estimating the excess weight of baggage before checking-in at the airport. We believe that this system can also be used for the evaluation of haptic illusions such as the size–weight illusion. The proposed system detects human-body joints as the input dataset for machine learning. We created a neural network that estimated an object’s mass in real-time, u/sing data from a single person for training. The experimental results showed that the proposed system could estimate an object’s mass more accurately than human senses.
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References
Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6M: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325–1339 (2014). https://doi.org/10.1109/TPAMI.2013.248
Martinez, J., Black, M.J., Romero, J.: On human motion prediction using recurrent neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4674–4683 (2017). https://doi.org/10.1109/CVPR.2017.497
Jain, A., Zamir, A.R., Savarese, S., Saxena, A.: Structural-RNN: deep learning on spatio-temporal graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5308–5317 (2016). https://doi.org/10.1109/CVPR.2016.573
Fragkiadaki, K., Levine, S., Felsen, P., Malik, J.: Recurrent network models for human dynamics. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4346–4354 (2015). https://doi.org/10.1109/ICCV.2015.494
Horiuchi, Y., Makino, Y., Shinoda, H.: Computational foresight: forecasting human body motion in real-time for reducing delays in interactive system. In: Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces, pp. 312–317 (2017). https://doi.org/10.1145/3132272.3135076
Fermüller, C., Wang, F., Yang, Y., Zampogiannis, K., Zhang, Y., Barranco, F., Pfeiffer, M.: Prediction of manipulation actions. Int. J. Comput. Vis. 126(2–4), 358–374 (2018). https://doi.org/10.1007/s11263-017-0992-z
Pham, T.-H., Kyriazis, N., Argyros, A.A., Kheddar, A.: Hand-object contact force estimation from markerless visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. (2017). https://doi.org/10.1109/TPAMI.2017.2759736
Hwang, W., Lim, S.-C.: Inferring interaction force from visual information without using physical force sensors. Sensors 17(11), 2455 (2017). https://doi.org/10.3390/s17112455
Lederman, S.J., Jones, L.A.: Tactile and haptic illusions. IEEE Trans. Haptics 4(4), 273–294 (2011). https://doi.org/10.1109/TOH.2011.2
Park, S.-B., Kim, S.-Y., Hyeong, J.-H., Chung, K.-R.: A study on the development of image analysis instrument and estimation of mass, volume and center of gravity using CT image in Korean. J. Mech. Sci. Technol. 28(3), 971–977 (2014). https://doi.org/10.1007/s12206-013-1168-6
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning (2015)
Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1302–1310 (2017). https://doi.org/10.1109/CVPR.2017.143
Mehta, D., Sridhar, S., Sotnychenko, O., Rhodin, H., Shafiei, M., Seidel, H.-P., Xu, W., Casas, D., Theobalt, C.: VNect: real-time 3D human pose estimation with a single RGB camera. ACM Trans. Graph. 36, 4 (2017). https://doi.org/10.1145/3072959.3073596
Acknowledgments
This research was supported by JST PRESTO 17939983. We would like to thank Editage (www.editage.jp) for English language editing.
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Oji, T., Makino, Y., Shinoda, H. (2018). Weight Estimation of Lifted Object from Body Motions Using Neural Network. In: Prattichizzo, D., Shinoda, H., Tan, H., Ruffaldi, E., Frisoli, A. (eds) Haptics: Science, Technology, and Applications. EuroHaptics 2018. Lecture Notes in Computer Science(), vol 10894. Springer, Cham. https://doi.org/10.1007/978-3-319-93399-3_1
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