[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Published:
International Journal of Social Robotics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. Full reference of the experts that provided the annotation of the behavioral factors can be found in the “Acknowledgments” section

References

  1. Aggarwal JK, Ryoo MS (2011) Human activity analysis: a review. ACM Comput Surv (CSUR) 43(3):16

    Article  Google Scholar 

  2. Atkeson CG, Hale JG, Pollick F, Riley M, Kotosaka S, Schaul S, Shibata T, Tevatia G, Ude A, Vijayakumar S et al (2000) Using humanoid robots to study human behavior. IEEE Intell Syst Appl 15(4):46–56

    Article  Google Scholar 

  3. Baddeley AD, Baddeley H, Bucks R, Wilcock G (2001) Attentional control in Alzheimer’s disease. Brain 124(8):1492–1508

    Article  Google Scholar 

  4. Berelson B, Steiner GA (1964) Human behavior: an inventory of scientific findings. Trans-action 1:2–2

    Google Scholar 

  5. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

    MATH  Google Scholar 

  6. Bloem BR, Valkenburg VV, Slabbekoorn M, Willemsen MD (2001) The multiple tasks test: development and normal strategies. Gait Posture 14(3):191–202

    Article  Google Scholar 

  7. Charalampous K, Kostavelis I, Gasteratos A (2016) Robot navigation in large-scale social maps: an action recognition approach. Expert Syst Appl 66:261–273

    Article  Google Scholar 

  8. Charalampous K, Kostavelis I, Gasteratos A (2017) Recent trends in social aware robot navigation: a survey. Robot Auton Syst 93:85–104

    Article  Google Scholar 

  9. Chrungoo A, Manimaran S, Ravindran B (2014) Activity recognition for natural human robot interaction. In: International conference on social robotics. Springer, pp 84–94

  10. Coppola C, Cosar S, Faria DR, Bellotto N (2017) Automatic detection of human interactions from RGB-D data for social activity classification. In: 2017 26th IEEE international symposium on robot and human interactive communication (RO-MAN), pp 871–876

  11. Doumanoglou A, Kouskouridas R, Malassiotis S, Kim TK (2016) Recovering 6D object pose and predicting next-best-view in the crowd. In: IEEE conference on computer vision and pattern recognition, pp 3583–3592

  12. Faria DR, Premebida C, Nunes U (2014) A probabilistic approach for human everyday activities recognition using body motion from RGB-D images. In: The 23rd IEEE international symposium on robot and human interactive communication, pp 732–737

  13. Foka AF, Trahanias PE (2010) Probabilistic autonomous robot navigation in dynamic environments with human motion prediction. Int J Soc Robot 2(1):79–94

    Article  Google Scholar 

  14. Garrell A, Villamizar M, Moreno-Noguer F, Sanfeliu A (2017) Teaching robots proactive behavior using human assistance. Int J Soc Robot 9(2):231–249

    Article  Google Scholar 

  15. Grześ M, Hoey J, Khan SS, Mihailidis A, Czarnuch S, Jackson D, Monk A (2014) Relational approach to knowledge engineering for POMDP-based assistance systems as a translation of a psychological model. Int J Approx Reason 55(1):36–58

    Article  Google Scholar 

  16. Hall ET (1966) The hidden dimension. Doubleday, New York

    Google Scholar 

  17. Han F, Reily B, Hoff W, Zhang H (2017) Space-time representation of people based on 3D skeletal data: a review. Comput Vis Image Underst 158:85–105

    Article  Google Scholar 

  18. Hoey J, Plötz T, Jackson D, Monk A, Pham C, Olivier P (2011) Rapid specification and automated generation of prompting systems to assist people with dementia. Pervasive Mobile Comput 7(3):299–318

    Article  Google Scholar 

  19. Karplus K, Brown M, Hughey R, Krogh A, Mian IS, Haussler D (1996) Dirichlet mixtures: a method for improving detection of weak but significant protein sequence homology. Comput Appl Biosci 12:327–345

    Google Scholar 

  20. Katz S, Downs TD, Cash HR, Grotz RC (1970) Progress in development of the index of ADL1. Gerontologist 10(1 Part 1):20–30

    Article  Google Scholar 

  21. Kim B, Pineau J (2016) Socially adaptive path planning in human environments using inverse reinforcement learning. Int J Soc Robot 8(1):51–66

    Article  Google Scholar 

  22. Koppula H, Saxena A (2013) Learning spatio-temporal structure from RGB-D videos for human activity detection and anticipation. In: International conference on machine learning, pp 792–800

  23. Kostavelis I, Kargakos A, Giakoumis D, Tzovaras D (2017) Robots workspace enhancement with dynamic human presence for socially-aware navigation. In: International conference on computer vision systems. Springer, pp 279–288

  24. Leigh A, Pineau J (2014) Laser-based person tracking for clinical locomotion analysis. In: IROS-rehabilitation and assistive robotics

  25. Leite I, Martinho C, Paiva A (2013) Social robots for long-term interaction: a survey. Int J Soc Robot 5(2):291–308

    Article  Google Scholar 

  26. Martinez-Contreras F, Orrite-Urunuela C, Herrero-Jaraba E, Ragheb H, Velastin SA (2009) Recognizing human actions using silhouette-based HMM. In: Sixth IEEE international conference on advanced video and signal based surveillance. IEEE, pp 43–48

  27. Norman DA, Shallice T (1986) Attention to action. In: Davidson RJ, Schwartz GE, Shapiro D (eds) Consciousness and self-regulation. Springer, New York, pp 1–18

    Google Scholar 

  28. Oreifej O, Liu Z (2013) HON4D: Histogram of oriented 4D normals for activity recognition from depth sequences. In: IEEE conference on computer vision and pattern recognition, pp 716–723

  29. Panangadan A, Matarić M, Sukhatme GS (2010) Tracking and modeling of human activity using laser rangefinders. Int J Soc Robot 2(1):95–107

    Article  Google Scholar 

  30. Piyathilaka L, Kodagoda S (2015) Human activity recognition for domestic robots. In: Mejias L, Corke P, Roberts J (eds) Field and service robotics. Springer, Berlin, pp 395–408

    Chapter  Google Scholar 

  31. Premebida C, Faria D, Souza F (2017) Dynamic Bayesian network for time-dependent classification problems in robotics. In: Prieto Tejedor J (ed) Bayesian inference, chapter 15. InTech, Croatia

    Google Scholar 

  32. Rahmani H, Mahmood A, Huynh DQ, Mian A (2014) Real time action recognition using histograms of depth gradients and random decision forests. In: 2014 IEEE winter conference on applications of computer vision. IEEE, pp 626–633

  33. Roitberg A, Perzylo A, Somani N, Giuliani M, Rickert M, Knoll A (2014) Human activity recognition in the context of industrial human–robot interaction. In: Asia-Pacific Signal and Information Processing Association. IEEE, pp 1–10

  34. Rusu RB, Marton ZC, Blodow N, Dolha M, Beetz M (2008) Towards 3D point cloud based object maps for household environments. Robot Auton Syst 56(11):927–941

    Article  Google Scholar 

  35. Rybok L, Schauerte B, Al-Halah Z, Stiefelhagen R (2014) Important stuff, everywhere! activity recognition with salient proto-objects as context. In: IEEE winter conference on applications of computer vision. IEEE, pp 646–651

  36. Ryu H, Monk A (2005) Will it be a capital letter: signalling case mode in mobile phones. Interact Comput 17(4):395–418

    Article  Google Scholar 

  37. Ryu H, Monk A (2009) Interaction unit analysis: a new interaction design framework. Hum Comput Interact 24(4):367–407

    Article  Google Scholar 

  38. Salah A, Ruiz-del Solar J, Mericli C, Oudeyer PY (2012) Human behavior understanding for robotics. In: Salah AA, Ruiz-del-Solar J, Meriçli Ç, Oudeyer P-Y (eds) Human behavior understanding. Springer, Berlin, pp 1–16

    Chapter  Google Scholar 

  39. Salah AA, Gevers T, Sebe N, Vinciarelli A et al (2010) Challenges of human behavior understanding. In: Salah AA, Ruiz-del-Solar J, Meriçli Ç, Oudeyer P-Y (eds) HBU. Springer, Berlin, pp 1–12

    Google Scholar 

  40. Salah AA, Lepri B, Pianesi F, Pentland AS (2011) Human behavior understanding for inducing behavioral change: application perspectives. In: International workshop on human behavior understanding. Springer, pp 1–15

  41. Santos L, Khoshhal K, Dias J (2015) Trajectory-based human action segmentation. Pattern Recognit 48(2):568–579

    Article  Google Scholar 

  42. Schmidler SC, Liu JS, Brutlag DL (2000) Bayesian segmentation of protein secondary structure. J Comput Biol 7(1–2):233–248

    Article  Google Scholar 

  43. Schmidt T, Newcombe R, Fox D (2015) DART: dense articulated real-time tracking with consumer depth cameras. Auton Robots 39:239–258

    Article  Google Scholar 

  44. Schmidt-Rohr SR, Losch M, Dillmann R (2008) Human and robot behavior modeling for probabilistic cognition of an autonomous service robot. In: IEEE international symposium on robot and human interactive communication. IEEE, pp 635–640

  45. Shan J, Akella S (2014) 3D human action segmentation and recognition using pose kinetic energy. In: IEEE workshop on advanced robotics and its social impacts. IEEE, pp 69–75

  46. Shotton J, Sharp T, Kipman A, Fitzgibbon A, Finocchio M, Blake A, Cook M, Moore R (2013) Real-time human pose recognition in parts from single depth images. Commun ACM 56(1):116–124

    Article  Google Scholar 

  47. Skinner BF (1953) Science and human behavior. Simon and Schuster, New York

    Google Scholar 

  48. Sonn U (1996) Longitudinal studies of dependence in daily life activities among elderly persons. Scand J Rehabilit Med Suppl 34:1–35

    Google Scholar 

  49. Stavropoulos G, Giakoumis D, Moustakas K, Tzovaras D (2017) Automatic action recognition for assistive robots to support MCI patients at home. In: 10th international conference on pervasive technologies related to assistive environments. ACM, pp 366–371

  50. Sung J, Ponce C, Selman B, Saxena A (2012) Unstructured human activity detection from RGBD images. In: IEEE international conference on robotics and automation. IEEE, pp 842–849

  51. Takayama L, Pantofaru C (2009) Influences on proxemic behaviors in human–robot interaction. In: IEEE international conference on intelligent robots and systems. IEEE, pp 5495–5502

  52. Taylor J, Shotton J, Sharp T, Fitzgibbon A (2012) The Vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation. In: IEEE conference on computer vision and pattern recognition. IEEE, pp 103–110

  53. Tsai MJ, Wu CL, Pradhan SK, Xie Y, Li TY, Fu LC, Zeng YC (2016) Context-aware activity prediction using human behavior pattern in real smart home environments. In: 2016 IEEE international conference on automation science and engineering (CASE). IEEE, pp 168–173

  54. Vasileiadis M, Malassiotis S, Giakoumis D, Bouganis CS, Tzovaras D (2017) Robust human pose tracking for realistic service robot applications. In: IEEE international conference on computer vision workshops, pp 1363–1372

  55. Wang J, Liu Z, Wu Y (2014) Learning actionlet ensemble for 3D human action recognition. In: Wang J (ed) Human action recognition with depth cameras. Springer, Basel, pp 11–40

    Chapter  Google Scholar 

  56. Whiten C, Laganiere R, Bilodeau GA (2013) Efficient action recognition with MoFREAK. In: International conference on computer and robot vision. IEEE, pp 319–325

  57. Wu J, Osuntogun A, Choudhury T, Philipose M, Rehg JM (2007) A scalable approach to activity recognition based on object use. In: IEEE international conference on computer vision. IEEE, pp 1–8

  58. Yang X, Tian Y (2014) Effective 3D action recognition using eigenjoints. J Vis Commun Image Represent 25(1):2–11

    Article  MathSciNet  Google Scholar 

  59. Zhu Y, Chen W, Guo G (2014) Evaluating spatiotemporal interest point features for depth-based action recognition. Image Vis Comput 32(8):453–464

    Article  Google Scholar 

  60. Ziaeefard M, Bergevin R (2015) Semantic human activity recognition: a literature review. Pattern Recognit 48(8):2329–2345

    Article  Google Scholar 

  61. Zipf GK (2016) Human behavior and the principle of least effort: an introduction to human ecology. Ravenio Books, Cambridge

    Google Scholar 

Download references

Acknowledgements

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.

Funding

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.

Author information

Authors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, 6th Km Charilaou-Thermi Road, 57001, Thermi, Thessaloniki, Greece

    Ioannis Kostavelis, Evangelos Skartados, Andreas Kargakos, Dimitrios Giakoumis & Dimitrios Tzovaras

  2. Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK

    Manolis Vasileiadis & Christos-Savvas Bouganis

Authors
  1. Ioannis Kostavelis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manolis Vasileiadis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Evangelos Skartados
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andreas Kargakos
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dimitrios Giakoumis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christos-Savvas Bouganis
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dimitrios Tzovaras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ioannis Kostavelis.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kostavelis, I., Vasileiadis, M., Skartados, E. et al. Understanding of Human Behavior with a Robotic Agent Through Daily Activity Analysis. Int J of Soc Robotics 11, 437–462 (2019). https://doi.org/10.1007/s12369-019-00513-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12369-019-00513-2

Keywords

Search

Navigation