Discovering Process Models of Activities of Daily Living from Sensors

  • Marco Cameranesi
  • Claudia Diamantini
  • Domenico PotenaEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 308)


In recent years, more and more effort was put in the design and development of smart environments, which are aimed at improving the life quality of people, providing users with advanced services supporting them during their daily activities. In order to implement these services, smart environments are equipped with several sensors that continuously monitor the activities performed by a user. Sensor data are activation sequences and could be seen as the execution of a process representing daily user behaviors and performed activities. In this paper we propose a methodology, which exploit Process Mining techniques to discover both the daily behavior model and macro activities models. The former represents the “standard” behavior of the user in the form of a process model. The latter is a set of process models representing the flow of sensors activations when given tasks or macro activities are performed. A real-world case study is introduced to empirically show the efficacy of the proposed methodology.


Ambient assisted living Activities of daily living Process mining Process discovery Sensor data mining 


  1. 1.
    van der Aalst, W.M.P.: Process Mining: Discovery Conformance and Enhancement of Business Processes, 1st edn. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  2. 2.
    Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefzbMATHGoogle Scholar
  3. 3.
    Diamantini, C., Genga, L., Potena, D., Storti, E.: Pattern discovery from innovation processes. In: 2013 International Conference on Collaboration Technologies and Systems, pp. 457–464, May 2013Google Scholar
  4. 4.
    Diamantini, C., Genga, L., Potena, D., van der Aalst, W.M.P.: Building instance graphs for highly variable processes. Expert Syst. Appl. 59, 101–118 (2016)CrossRefGoogle Scholar
  5. 5.
    Dimaggio, M., Leotta, F., Mecella, M., Sora, D.: Process-based habit mining: experiments and techniques. In: 2016 International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress, pp. 145–152, 18–21 July 2016Google Scholar
  6. 6.
    Fernández-Llatas, C., Lizondo, A., Sanchez, E.M., Benedí, J., Traver, V.: Process mining methodology for health process tracking using real-time indoor location systems. Sensors 15(12), 29821–29840 (2015)CrossRefGoogle Scholar
  7. 7.
    Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007). CrossRefGoogle Scholar
  8. 8.
    Jaroucheh, Z., Liu, X., Smith, S.: Recognize contextual situation in pervasive environments using process mining techniques. J. Ambient Intell. Hum. Comput. 2, 53–69 (2011)CrossRefGoogle Scholar
  9. 9.
    Katz, S.: Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living. J. Am. Geriatr. Soc. 31(12), 721–7 (1983)CrossRefGoogle Scholar
  10. 10.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 66–78. Springer, Cham (2014). CrossRefGoogle Scholar
  11. 11.
    Rashidi, P., Mihailidis, A.: A survey on ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inform. 17(3), 579–590 (2013)CrossRefGoogle Scholar
  12. 12.
    Rashidi, P., Cook, D.J.: Mining and monitoring patterns of daily routines for assisted living in real world settings. In: Proceedings of the 1st ACM International Health Informatics Symposium, pp. 336–345. ACM, New York (2010)Google Scholar
  13. 13.
    Rossi, L., Belli, A., Santis, A.D., Diamantini, C., Frontoni, E., Gambi, E., Palma, L., Pernini, L., Pierleoni, P., Potena, D., Raffaeli, L., Spinsante, S., Zingaretti, P., Cacciagrano, D., Corradini, F., Culmone, R., Angelis, F.D., Merelli, E., Re, B.: Interoperability issues among smart home technological frameworks. In: International Conference on Mechatronic and Embedded Systems and Applications, pp. 1–7, 27 September 2014Google Scholar
  14. 14.
    Senderovich, A., Rogge-Solti, A., Gal, A., Mendling, J., Mandelbaum, A.: The ROAD from sensor data to process instances via interaction mining. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 257–273. Springer, Cham (2016). Google Scholar
  15. 15.
    Sztyler, T., Völker, J., Carmona, J., Meier, O., Stuckenschmidt, H.: Discovery of personal processes from labeled sensor data - an application of process mining to personalized health care. In: Proceedings of the International Workshop on Algorithms and Theories for the Analysis of Event Data, pp. 31–46 (2015)Google Scholar
  16. 16.
    Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) Pervasive 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004). CrossRefGoogle Scholar
  17. 17.
    Weijters, A., van der Aalst, W., de Medeiros, A.: Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven, Technical report WP 166 (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Marco Cameranesi
    • 1
  • Claudia Diamantini
    • 1
  • Domenico Potena
    • 1
    Email author
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversità Politecnica delle MarcheAnconaItaly

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