Understanding of human behavior with a robotic agent through daily activity analysis
File(s)
Accepted version
Author(s)
Kostavelis, Ioannis
Vasileiadis, Manolis
Skartados, Evangelos
Kargakos, Andreas
Giakoumis, Dimitrios
Type
Journal Article
Abstract
Personal assistive robots to be realized in the near future should have the ability to seamlessly coexist with humans in unconstrained environments, with the robot’s capability to understand and interpret the human behavior during human–robot cohabitation significantly contributing towards this end. Still, the understanding of human behavior through a robot is a challenging task as it necessitates a comprehensive representation of the high-level structure of the human’s behavior from the robot’s low-level sensory input. The paper at hand tackles this problem by demonstrating a robotic agent capable of apprehending human daily activities through a method, the Interaction Unit analysis, that enables activities’ decomposition into a sequence of units, each one associated with a behavioral factor. The modelling of human behavior is addressed with a Dynamic Bayesian Network that operates on top of the Interaction Unit, offering quantification of the behavioral factors and the formulation of the human’s behavioral model. In addition, light-weight human action and object manipulation monitoring strategies have been developed, based on RGB-D and laser sensors, tailored for onboard robot operation. As a proof of concept, we used our robot to evaluate the ability of the method to differentiate among the examined human activities, as well as to assess the capability of behavior modeling of people with Mild Cognitive Impairment. Moreover, we deployed our robot in 12 real house environments with real users, showcasing the behavior understanding ability of our method in unconstrained realistic environments. The evaluation process revealed promising performance and demonstrated that human behavior can be automatically modeled through Interaction Unit analysis, directly from robotic agents.
Date Issued
2019-06-01
Date Acceptance
2019-01-03
Citation
International Journal of Social Robotics, 2019, 11 (3), pp.437-462
ISSN
1875-4791
Publisher
Springer Verlag
Start Page
437
End Page
462
Journal / Book Title
International Journal of Social Robotics
Volume
11
Issue
3
Copyright Statement
© Springer Nature B.V. 2019. The final publication is available at Springer via https://link.springer.com/article/10.1007%2Fs12369-019-00513-2
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000474401100006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Technology
Robotics
Human behavior understanding
Daily activities interpretation
Interaction Unit analysis
Bayesian networks
Mobile robots
HUMAN ACTIVITY RECOGNITION
NAVIGATION
MAPS
Publication Status
Published
Date Publish Online
2019-01-18