KI - Künstliche Intelligenz

, Volume 31, Issue 4, pp 349–355 | Cite as

Assigning Group Activity Semantics to Multi-Device Mobile Sensor Data

An Explanation-Based Perspective
Technical Contribution

Abstract

Numerous types of sensor data can be gathered via devices on mobile sensors, such as smartphones and smartwatches as well as things endowed with sensors. Such sensor data from disparate sources can be aggregated and inferences can be made about the user, the user’s physical activities as well as the physical activities of the group the user is part of. A perspective on this is that the group’s physical activity becomes an explanation for the sensor readings now obtained from this set of sensors. This paper proposes an explanation-based perspective on reasoning about multi-device sensor data, and describes a framework called GroupSense that prototypes this idea.

References

  1. 1.
    Su X, Tong H, Ji P (2014) Activity recognition with smartphone sensors. Tsinghua Sci Technol 19(3):235–249CrossRefGoogle Scholar
  2. 2.
    Gordon D, Hanne J-H, Berchtold M, Shirehjini AAN, Beigl M (2013) Towards collaborative group activity recognition using mobile devices. Mob Netw Appl 18(3):326–340CrossRefGoogle Scholar
  3. 3.
    Amin B Abkenar, Seng W Loke, J Wenny Rahayu, Arkady B Zaslavsky (2016) Energy considerations for continuous group activity recognition using mobile devices: the case of GroupSense. Proceedings of AINA 479–486Google Scholar
  4. 4.
    Shanahan M (2005) Perception as abduction: turning sensor data into meaningful representation. Cognit Sci 29(1):103–134CrossRefGoogle Scholar
  5. 5.
    K Thirunarayan, CA Henson, AP Sheth (2009) Situation awareness via abductive reasoning from semantic sensor data: a preliminary report, proceedings of the 2009 international symposium on collaborative technologies and systems, Baltimore, MD 111–118Google Scholar
  6. 6.
    T Hubauer (2016) Relaxed abduction: robust information interpretation for industrial applications, 1st edn. Springer, VerlagGoogle Scholar
  7. 7.
    Allen JF (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832–843CrossRefMATHGoogle Scholar
  8. 8.
    Loke SW (2004) Representing and reasoning with situations for context-aware pervasive computing: a logic programming perspective. Knowl Eng Rev 19(3):213–233CrossRefGoogle Scholar
  9. 9.
    Guo B, Yu Z, Chen L, Zhou X, Ma X (2016) MobiGroup: enabling lifecycle support to social activity organization and suggestion with mobile crowd sensing. IEEE Trans Hum Mach Syst 46(3):390–402CrossRefGoogle Scholar
  10. 10.
    Vahora SA, Chauhan NC (2017) A comprehensive study of group activity recognition methods in video. Indian J Sci Technol 10(23):1–11CrossRefGoogle Scholar
  11. 11.
    R Sen, Y Lee, K Jayarajah, A Misra, R Balan, K GruMon (2014) Fast and accurate group monitoring for heterogeneous urban spaces. Proceedings of the 12th ACM conference on embedded network sensor systems 46–60Google Scholar
  12. 12.
    AL Bourbia, H Son, B Shin, T Kim, D Lee, SJ Hyun (2016) Temporal dependency rule learning based group activity recognition in smart spaces. Proceedings of the IEEE 40th annual computer software and applications conference (COMPSAC) 658–663Google Scholar
  13. 13.
    V Elangovan, A Shirkhodaie (2014) Knowledge discovery in group activities through sequential observation analysis. Proc of SPIE vol 9091Google Scholar
  14. 14.
    K Jayarajah, Y Lee, A Misra, RK Balan (2015) Need accurate user behaviour?: pay attention to groups! Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing 855–866Google Scholar
  15. 15.
    DH Hu, X-X Zhang, J Yin, VW Zheng, Q Yang (2009) Abnormal activity recognition based on HDP-HMM models. Proceedings of IJCAI 1715–1720Google Scholar
  16. 16.
    X Cui, Q Liu, M Gao, DN Metaxas (2011) Abnormal detection using interaction energy potentials. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) 3161–3167Google Scholar
  17. 17.
    S Ali, S Khusro, I Ullah, A Khan, I Khan (2017) Smart onto sensor: ontology for semantic interpretation of smartphone sensors data for context-aware applications. J Sens 2017:26 (Article ID 8790198) Google Scholar
  18. 18.
    K Whitehouse, F Zhao, J Liu (2006) Semantic streams: a framework for composable semantic interpretation of sensor data. Proceedings of the third European conference on wireless sensor networks, Kay Rmer, Holger Karl, and Friedemann Mattern (eds.). Springer, Berlin, Heidelberg 5–20Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2017

Authors and Affiliations

  1. 1.School of Information TechnologyDeakin UniversityMelbourneAustralia
  2. 2.Department of Computer Science and Information TechnologyLa Trobe UniversityMelbourneAustralia

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