System for User Context Determination in a Network of IoT Devices

  • Kushal SinglaEmail author
  • Joy Bose
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10898)


In order to build a user profile using data from various connected IoT smart sensors and devices, determination of the current context of the user is vital. We assume a hierarchy of contexts (such as party, trip, exercise) based on common daily activities of users. Knowing the context can inform about the actual activity being performed by the user and predict what the user might be interested in at a given moment. This can then be used to suggest appropriate services to the user. In this paper, we propose a system to infer the user context from input data from various devices. Our system includes an app classifier, a Points of Interest (POI) classifier and a motion classifier to make sense of the input sensor data. We describe the implementation details of a system and some results on real world data to measure our model performance.


User modelling Context POI classifier App classifier 


  1. 1.
    Chon, J., Cha, H.: LifeMap: a smartphone-based context provider for location-based services. IEEE Pervasive Comput. 10(2), 58–67 (2011)CrossRefGoogle Scholar
  2. 2.
    Lee, Y.-S., Cho, S.-B.: Activity recognition using hierarchical Hidden Markov Models on a smartphone with 3D accelerometer. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011. LNCS (LNAI), vol. 6678, pp. 460–467. Springer, Heidelberg (2011). Scholar
  3. 3.
    Raento, M., Oulasvirta, A., Petit, R., Toivonen, H.: ContextPhone: a prototyping platform for context-aware mobile applications. IEEE Pervasive Comput. 4(2), 51–59 (2005)CrossRefGoogle Scholar
  4. 4.
    Otebolaku, A.M., Lee, G.M.: Towards context classification and reasoning in IoT. In: Proceedings of ConTEL. IEEE Press (2017)Google Scholar
  5. 5.
    Sharma, M., Srivastava, R., Anand, A., Prakash, D., Kaligounder, L.: Wearable motion sensor based phasic analysis of tennis serve for performance feedback. In: Proceedings of ICASSP (2017)Google Scholar
  6. 6.
    Kannan, R., Garg, A.: Adaptive sensor fusion technology for mobile and wearable applications. IEEE Sens. J. (2015)Google Scholar
  7. 7.
    Shaw, B., Shea, J., Sinha, S., Hogue, A.: Learning to rank for spatiotemporal search. In: Proceedings of WSDM 2013. ACM (2013)Google Scholar
  8. 8.
  9. 9.
    Reverse Geocoding: Google Maps Javascript API. Google Developers.
  10. 10.
    (Yue) Zhang, D., et al.: Large-scale point-of-interest category prediction using natural language processing models. In: Proceedings of IEEE BIGDATA. IEEE Press (2017)Google Scholar
  11. 11.
    Sun, L., Zhang, D., Li, B., Guo, B., Li, S.: Activity recognition on an accelerometer embedded mobile phone with varying positions and orientations. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds.) UIC 2010. LNCS, vol. 6406, pp. 548–562. Springer, Heidelberg (2010). Scholar
  12. 12.
    Alzantot, M., Youssef, M.: UPTIME: ubiquitous pedestrian tracking using mobile phones. In: Proceedings of WCNC 2012. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Samsung R&D InstituteBangaloreIndia

Personalised recommendations