Implicit affectivity and rapid processing of affective body language: An fMRI study

Thomas Suslow; Klas Ihme; Markus Quirin; Vladimir Lichev; Nicole Rosenberg; Jochen Bauer; Luise Bomberg; Anette Kersting; Karl-Titus Hoffmann; Donald Lobsien

doi:10.1111/sjop.12227

A Human Affect Recognition System for Socially Interactive Robots

Robotics ◽

10.4018/978-1-4666-4607-0.ch015 ◽

2013 ◽

pp. 295-314

Author(s):

Derek McColl ◽

Goldie Nejat

Keyword(s):

Human Body ◽

Recognition System ◽

Sensory Systems ◽

Body Language ◽

Body Parts ◽

Language Recognition ◽

Human Body Model ◽

Socially Interactive Robots ◽

Body Shapes ◽

Affective Body Language

This chapter presents a real-time robust affect classification methodology for socially interactive robots engaging in one-on-one human-robot-interactions (HRI). The methodology is based on identifying a person’s body language in order to determine how accessible he/she is to a robot during the interactions. Static human body poses are determined by first identifying individual body parts and then utilizing an indirect 3D human body model that is invariant to different body shapes and sizes. The authors implemented and tested their technique using two different sensory systems in social HRI scenarios to motivate its robustness for the proposed application. In particular, the experiments consisted of integrating the proposed body language recognition and affect classification methodology with imaging-based sensory systems onto the human-like socially interactive robot Brian 2.0 in order for the robot to recognize affective body language during one-on-one interactions.

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A socially assistive robot that can interpret affective body language during one-on-one human-robot interactions

International Journal of Biomechatronics and Biomedical Robotics ◽

10.1504/ijbbr.2012.049594 ◽

2012 ◽

Vol 2 (1) ◽

pp. 39

Author(s):

Derek McColl ◽

Goldie Nejat

Keyword(s):

Body Language ◽

Assistive Robot ◽

Affective Body Language ◽

Socially Assistive Robot

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A Human Affect Recognition System for Socially Interactive Robots

Handbook of Research on Technoself ◽

10.4018/978-1-4666-2211-1.ch029 ◽

2013 ◽

pp. 554-573

Author(s):

Derek McColl ◽

Goldie Nejat

Keyword(s):

Human Body ◽

Recognition System ◽

Sensory Systems ◽

Body Language ◽

Body Parts ◽

Language Recognition ◽

Human Body Model ◽

Socially Interactive Robots ◽

Body Shapes ◽

Affective Body Language

This chapter presents a real-time robust affect classification methodology for socially interactive robots engaging in one-on-one human-robot-interactions (HRI). The methodology is based on identifying a person’s body language in order to determine how accessible he/she is to a robot during the interactions. Static human body poses are determined by first identifying individual body parts and then utilizing an indirect 3D human body model that is invariant to different body shapes and sizes. The authors implemented and tested their technique using two different sensory systems in social HRI scenarios to motivate its robustness for the proposed application. In particular, the experiments consisted of integrating the proposed body language recognition and affect classification methodology with imaging-based sensory systems onto the human-like socially interactive robot Brian 2.0 in order for the robot to recognize affective body language during one-on-one interactions.

Download Full-text