Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chon, J., Cha, H.: LifeMap: a smartphone-based context provider for location-based services. IEEE Pervasive Comput. 10(2), 58–67 (2011)
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). https://doi.org/10.1007/978-3-642-21219-2_58
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)
Otebolaku, A.M., Lee, G.M.: Towards context classification and reasoning in IoT. In: Proceedings of ConTEL. IEEE Press (2017)
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)
Kannan, R., Garg, A.: Adaptive sensor fusion technology for mobile and wearable applications. IEEE Sens. J. (2015)
Shaw, B., Shea, J., Sinha, S., Hogue, A.: Learning to rank for spatiotemporal search. In: Proceedings of WSDM 2013. ACM (2013)
Foursquare Developers: API Explorer. https://foursquare.com/developers/explore#req=users%2Fself
Reverse Geocoding: Google Maps Javascript API. Google Developers. https://developers.google.com/maps/documentation/javascript/examples/geocoding-reverse
(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)
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). https://doi.org/10.1007/978-3-642-16355-5_42
Alzantot, M., Youssef, M.: UPTIME: ubiquitous pedestrian tracking using mobile phones. In: Proceedings of WCNC 2012. IEEE (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Singla, K., Bose, J. (2018). System for User Context Determination in a Network of IoT Devices. In: Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds) Smart Homes and Health Telematics, Designing a Better Future: Urban Assisted Living. ICOST 2018. Lecture Notes in Computer Science(), vol 10898. Springer, Cham. https://doi.org/10.1007/978-3-319-94523-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-94523-1_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94522-4
Online ISBN: 978-3-319-94523-1
eBook Packages: Computer ScienceComputer Science (R0)