期刊
INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS
卷 5, 期 3, 页码 367-378出版社
SPRINGER
DOI: 10.1007/s12369-013-0189-8
关键词
Proxemics; Spatial interaction; Spatial dynamics; Sociable spacing; Social robot; Human-robot interaction; PrimeSensor; Microsoft Kinect
类别
资金
- NSF
- ONR [MURI N00014-09-1-1031]
- NSF [IIS-1208500]
- [CNS-0709296]
- [IIS-1117279]
- [IIS-0803565]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1117279] Funding Source: National Science Foundation
In this work, we discuss a set of feature representations for analyzing human spatial behavior (proxemics) motivated by metrics used in the social sciences. Specifically, we consider individual, physical, and psychophysical factors that contribute to social spacing. We demonstrate the feasibility of autonomous real-time annotation of these proxemic features during a social interaction between two people and a humanoid robot in the presence of a visual obstruction (a physical barrier). We then use two different feature representations-physical and psychophysical-to train Hidden Markov Models (HMMs) to recognize spatiotemporal behaviors that signify transitions into (initiation) and out of (termination) a social interaction. We demonstrate that the HMMs trained on psychophysical features, which encode the sensory experience of each interacting agent, outperform those trained on physical features, which only encode spatial relationships. These results suggest a more powerful representation of proxemic behavior with particular implications in autonomous socially interactive and socially assistive robotics.
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