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Human engagement intention intensity recognition method based on two states fusion fuzzy inference system

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Abstract

Natural human–robot interaction requires social robots to have human-like perception of engagement intention. In the multi-person interaction scenario, it’s a vital task for social robots to rationally select the main interaction object. Existing studies mostly focus on analyzing whether a person has engagement intention before interaction. However, this qualitative analysis of engagement intention is only applicable in single-person interaction scenarios. When multiple people have the intention to engage with the robot, the robot needs to quantitatively analyze the engagement intention intensity (EII) of all people to make a reasonable interaction decision. In addition, for EII recognition, it is an ideal state that social robots can imitate human social thinking as much as possible. For these purposes, a method that can efficiently recognize the EII by fusing transient features and temporal features is proposed. First, the 3D pose extractor is used to extract the 3D skeleton information which can calculate the transient features including linear distance and body orientation. Second, an improved ConvLSTM network is proposed to effectively identify pedestrian motion states which can reflect temporal information. Finally, based on the proposed two states fusion fuzzy inference system (TSFFIS), the EII can be judged by the three features which are linear distance, body orientation and motion states. Comparative experiments show that our method can effectively identify the EII of different pedestrians relative to the robot. Compared with existing methods, the EII recognition method based on TSFFIS has better performance.

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Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the Cooperative Project between universities in Chongqing and affiliated institutes of Chinese Academy of Sciences (No. HZ2021011), Youth Project of Science and Technology Research Program of Chongqing Education Commission of China (No. KJQN202101131), Graduate Innovation Project of Chongqing University of Technology (clgycx20201010) and Postdoctoral Science Foundation Program of Chongqing Science and Technology Bureau (No. CSTB2022NSCQ-BHX0674).

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Authors and Affiliations

  1. College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China

    Jian Bi, Fangchao Hu, Yujin Wang, Mingnan Luo & Miao He

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Contributions

All authors contributed to the study conception and design. Material preparation and data collection were performed by FH, YW and ML. Data analysis were performed by JB and MH. The first draft of the manuscript was written by JB and all authors commented on previous versions of the manuscript. Conceptualization and supervision was mainly performed by MH. All authors read and approved the final manuscript.

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Correspondence to Miao He.

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Bi, J., Hu, F., Wang, Y. et al. Human engagement intention intensity recognition method based on two states fusion fuzzy inference system. Intel Serv Robotics 16, 307–322 (2023). https://doi.org/10.1007/s11370-023-00464-8

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