Weight Estimation of Lifted Object from Body Motions Using Neural Network
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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.
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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