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  • 2017

  • Baranwal, Neha; Nandi, Gora Chand; Singh, Avinash Kumar (2017): Real‐time gesture–based communication using possibility theory–based hidden Markov model. In: Computational Intelligence 33 (4), S. 843-862. DOI: 10.1111/coin.12116

    DOI: https://doi.org/10.1111/coin.12116 

    Abstract: Exploring correct patterns from low‐frequency time‐series data is challenging. For resolving this problem, the concept of possibility theory–based hidden Markov model (PTBHMM) has been proposed. In this article, all three fundamental problems (evaluation, decoding, and learning) of conventional HMM have been addressed using possibility theory. For handling uncertainty, we have used an axiomatic approach of possibility theory proposed by Zadeh. The time complexity of existing solutions of HMM (forward, backward, Viterbi, and Baum Welch) and proposed possibility‐based solutions has been calculated and compared. From the comparison result, it has been found that PTBHMM has lesser time complexity and hence will be more suitable for real‐time gesture–based communication. (PsycINFO Database Record (c) 2018 APA, all rights reserved)

  • Hsiao, Shih-Wen; Lee, Chu-Hsuan; Yang, Meng-Hua; Chen, Rong-Qi (2017): User interface based on natural interaction design for seniors. In: Computers in Human Behavior 75, S. 147-159. DOI: 10.1016/j.chb.2017.05.011

    DOI: https://doi.org/10.1016/j.chb.2017.05.011 

    Abstract: With world population ageing, how to help seniors to adapt to technology life is an important issue. Technology is becoming life rather than resistance, because many of the technology applications are often accompanied by a lot of information to process. This makes the user interface to become an important bridge between human computer interactions. Especially the inconvenience caused by human ageing, these related issues from the cognitive and operational of products are derived. This study proposes a study of user interface design based on natural interaction to increase seniors’ usage intention. In the proposed contents, the Kinect sensor is used to retrieve seniors’ in-depth information in movements, thus the user interface of system can be operated by the gesture intuitively. In the framework of the system, in the first all, the morphology is applied to identify the features of a hand from depth values obtained from the sensor. Gesture is used to recognize operating behavior of users to implement the interactive action, and collision detection is applied to confirm effectiveness of operation. On the other hand, through interpretive structural model (ISM), each design element of interactive interface can be decomposed and realized, and the solution for target and direction of design problem is also proposed. At the meanwhile, the concept of affordance is conducted to the development of interface for graphic users that proposed in this study, and the design achievement contains operation and usability of intuition can further be acquired. Finally, based on the proposed methodology, an intuitive user interface of digital devices is constructed by Java programming language that allows for verifying the feasibility of user interface for seniors. Besides, the proposed method can be widely used to develop the user interface for various products. (PsycINFO Database Record (c) 2017 APA, all rights reserved)

  • Rodriguez, I.; Martínez-Otzeta, J. M.; Lazkano, E.; Ruiz, T.; Sierra, B. (2017) : On how self-body awareness improves autonomy in social robots: 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO): Parisian Macao, China: IEEE, S. 1688-1693

    DOI: https://doi.org/10.1109/ROBIO.2017.8324661 

    Abstract: Just as humans show consciousness of their body, social robots, in the way to be truly autonomous need to be aware of their body posture. Feasible gestures, moves and actions depend on the current body posture. The work developed in this paper aims to empirically show how self configuration recognition augments the degree of autonomy of a robot in the context of entertainment robotics. The integration of a classification tree for body posture identification based on data acquired from proprioceptive sensors of a NAO robot allows to interact with the robot in a more flexible and persistent manner. As a result, the robot shows a more sound behavior and greater degree of autonomy. Moreover, even if the body-awareness has been developed for minstrel robots, its application can be generalized to other contexts.

  • 2014

  • Obaid, Mohammad; Kistler, Felix; Häring, Markus; Bühling, René; André, Elisabeth (2014): A Framework for User-Defined Body Gestures to Control a Humanoid Robot. In: International Journal of Social Robotics 6 (3), S. 383-396. DOI: 10.1007/s12369-014-0233-3

    Abstract: This paper presents a framework that allows users to interact with and navigate a humanoid robot using body gestures. The first part of the paper describes a study to define intuitive gestures for eleven navigational commands based on analyzing 385 gestures performed by 35 participants. From the study results, we present a taxonomy of the user-defined gesture sets, agreement scores for the gesture sets, and time performances of the gesture motions. The second part of the paper presents a full body interaction system for recognizing the user-defined gestures. We evaluate the system by recruiting 22 participants to test for the accuracy of the proposed system. The results show that most of the defined gestures can be successfully recognized with a precision between 86100 % and an accuracy between 7396 %. We discuss the limitations of the system and present future work improvements.

  • Zaraki, A.; Giuliani, M.; Dehkordi, M. B.; Mazzei, D.; D’ursi, A.; De Rossi, D. (2014) : An RGB-D based social behavior interpretation system for a humanoid social robot: 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM): Tehran, Iran: IEEE, S. 185-190

    DOI: https://doi.org/10.1109/ICRoM.2014.6990898 

    Abstract: Humanoid social robots that interact with people need to be capable of interpreting the social behavior of their interaction partners in order to respond in a socially appropriate way. In this paper, we present a social behavior interpretation system that enables a humanoid robot to recognize human social behavior by analyzing communicative signals. The system receives the constructed RGB-D scene from a Kinect sensor, extracts information about body gesture and head pose from the scene using Microsoft Kinect SDK, and recognizes eight human social behaviors using a Hidden Markov Model (HMM). We trained the eight-state HMM with a corpus of 35 recorded human-human interaction scenes. The evaluation of the system shows a weighted average recognition rate of 81% for all states.

  • 2012

  • Kistler, Felix; Endrass, Birgit; Damian, Ionut; Dang, Chi Tai; André, Elisabeth (2012): Natural interaction with culturally adaptive virtual characters. In: Journal on Multimodal User Interfaces 6 (1-2), S. 39-47. DOI: 10.1007/s12193-011-0087-z

    Abstract: Recently, the verbal and non-verbal behavior of virtual characters has become more and more sophisticated due to advances in behavior planning and rendering. Nevertheless, the appearance and behavior of these characters is in most cases based on the cultural background of their designers. Especially in combination with new natural interaction interfaces, there is the risk that characters developed for a particular culture might not find acceptance when being presented to another culture. A few attempts have been made to create characters that reflect a particular cultural background. However, interaction with these characters still remains an awkward experience in particular when it comes to non-verbal interaction. In many cases, human users either have to choose actions from a menu their avatar has to execute or they have to struggle with obtrusive interaction devices. In contrast, our paper combines an approach to the generation of culture-specific behaviors with full body avatar control based on the Kinect sensor. A first study revealed that users are able to easily control an avatar through their body movements and immediately adapt its behavior to the cultural background of the agents they interact with.

  • 2005

  • Sugiyama, O.; Kanda, T.; Imai, M.; Ishiguro, H.; Hagita, N. (2005) : Three-layered draw-attention model for humanoid robots with gestures and verbal cues: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems: Piscataway NJ: IEEE, S. 2423-2428

    DOI: https://doi.org/10.1109/IROS.2005.1545293 

    Abstract: When we talk about objects in an environment, we indicate to a listener which object is currently under consideration by using pointing gesture and such reference terms as "this" and "that". Such reference terms play an important role in human interaction by quickly informing the listener of an indicated object’s location. In this research, we propose a three-layered draw-attention model for humanoid robots with gestures and verbal cues. Our proposed three-layered model consists of three sub models: reference term model (RTM), limit distance model (LDM) and object property model (OPM). RTM decides an appropriate reference term using functions constructed by an analysis of human behavior. LDM decides whether to use the object’s property with a reference term. OPM decides the appropriate property for indicating the object by comparing object properties with each other. We developed an attention drawing system in a communication robot named "Robovie" based on the three layered model. We confirmed its effectiveness through the experiments.

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