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PurposeThe purpose of this paper is to deal with a method for gesture encoding and reproduction, particularly aiming at a text‐to‐gesture (TTG) system that enables robotic agents to generate proper gestures automatically and naturally in human‐robot interaction.Design/methodology/approachReproducing proper gestures, naturally synchronized with speech, is important under the TTG concept. The authors first introduce a gesture model that is effective to abstract and describe a variety of human gestures. Based on the model, a gesture encoding/decoding scheme is proposed to encode observed gestures symbolically and parametrically and to reproduce robot gestures from the codes. In particular, this paper mainly addresses a gesture scheduling method that deals with the alignment and refinement of gestural motions, in order to reproduce robotic gesticulation in a human‐like, natural fashion.FindingsThe proposed method has been evaluated through a series of questionnaire surveys, and it was found that reproduced gestures by a robotic agent could appeal satisfactorily to human beings.Originality/valueThis paper provides a series of algorithms to treat overlapped motions and to refine the timing parameters for the motions, so that robotic agents reproduce human‐like, natural gestures.

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In this study, we investigated the performance and reliability of commercial corrosion sensors for monitoring the integrity of piping systems in various fluid environments as an alternative to ultrasonic transducers. To this end, we investigated pipes’ wall-thinning using commercial electrical resistance (ER), linear polarization resistance (LPR), and ultrasonic transducer (UT) sensors under various operating environments. A pilot-scale closed-loop test bed was built to simulate a real pipeline flow situation, from which the sensor data were collected and analyzed. Experimental results indicate that, in the case of the LPR sensor, it is challenging to accurately measure the corrosion rate when a specific measure exceeds the threshold in a severe corrosion environment. In contrast, the ER sensor could measure metal loss under all conditions and reflect the corresponding characteristics. The metal loss (about 0.25 mm) of the real pipe after the experiment was confirmed to be equal to the metal loss (0.254 mm) measured by the sensor. Furthermore, the regression analysis revealed a high correlation between the results obtained from the ER and UT sensors. Thus, evaluating the remaining thickness of the piping system using the commercial ER sensor is deemed to be effective and reliable.

This paper proposes a robust hand segmentation method using view-invariant characteristic of a wrist-mounted camera, and deals with a hand shape recognition system based on segmented hand information. We actively utilize the advantage of the proposed camera device that provides view-invariant images physically, and segment hand region using a Bayesian rule based on adaptive histograms. We construct HSV histograms from RGB histograms, and update HSV histograms using hand region information from a current image. We also propose a user adaptation method by which hand models gradually approach user-dependent models from user-independent models as the user uses the system. The proposed method was evaluated using 16 Korean manual alphabet, and we obtained increases of 27.91% in recognition success rate.