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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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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DOI: https://doi.org/10.1007/978-3-319-93399-3_1
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