Understanding of Human Behavior with a Robotic Agent Through Daily Activity Analysis

  • Ioannis KostavelisEmail author
  • Manolis Vasileiadis
  • Evangelos Skartados
  • Andreas Kargakos
  • Dimitrios Giakoumis
  • Christos-Savvas Bouganis
  • Dimitrios Tzovaras


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.


Human behavior understanding Daily activities interpretation Interaction Unit analysis Bayesian networks Mobile robots 



We would like to thank Fundacio ACE Barcelona Alzheimer Institute & Research Centre as well as the Medical University of Lublin, Poland, for offering their expertise in the annotation of the behavioral factors in the IU analysis as well as for offering their premises for the dataset acquisition with RAMCIP robot.


This work has been supported by the EU Horizon 2020 funded project namely: “Robotic Assistant for MCI Patients at home (RAMCIP)” under the Grant Agreement with No. 643433.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Ioannis Kostavelis
    • 1
    Email author
  • Manolis Vasileiadis
    • 2
  • Evangelos Skartados
    • 1
  • Andreas Kargakos
    • 1
  • Dimitrios Giakoumis
    • 1
  • Christos-Savvas Bouganis
    • 2
  • Dimitrios Tzovaras
    • 1
  1. 1.Information Technologies InstituteCentre for Research and Technology HellasThermiGreece
  2. 2.Department of Electrical and Electronic EngineeringImperial College LondonLondonUK

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